From 5259a4d65d8676772ee5da1caf3794d4ac39ad8b Mon Sep 17 00:00:00 2001 From: Danilo Burbano <37355249+danilojsl@users.noreply.github.com> Date: Thu, 6 Apr 2023 12:56:54 -0500 Subject: [PATCH 01/26] SPARKNLP-782 Removes deprecated parameter enablePatternRegex (#13664) --- python/sparknlp/annotator/er/entity_ruler.py | 23 +------------------ .../annotators/er/EntityRulerApproach.scala | 22 ++---------------- .../nlp/annotators/er/EntityRulerModel.scala | 17 ++++---------- .../com/johnsnowlabs/storage/Database.scala | 4 ---- 4 files changed, 8 insertions(+), 58 deletions(-) diff --git a/python/sparknlp/annotator/er/entity_ruler.py b/python/sparknlp/annotator/er/entity_ruler.py index f5e57efac327..d470dec59ab2 100755 --- a/python/sparknlp/annotator/er/entity_ruler.py +++ b/python/sparknlp/annotator/er/entity_ruler.py @@ -27,9 +27,6 @@ class EntityRulerApproach(AnnotatorApproach, HasStorage): to be set as the "format" field in the ``option`` parameter map and depending on the file type, additional parameters might need to be set. - To enable regex extraction, ``setEnablePatternRegex(True)`` needs to be - called. - If the file is in a JSON format, then the rule definitions need to be given in a list with the fields "id", "label" and "patterns":: @@ -71,8 +68,6 @@ class EntityRulerApproach(AnnotatorApproach, HasStorage): ---------- patternsResource Resource in JSON or CSV format to map entities to patterns - enablePatternRegex - Enables regex pattern match useStorage Whether to use RocksDB storage to serialize patterns @@ -106,8 +101,7 @@ class EntityRulerApproach(AnnotatorApproach, HasStorage): ... "patterns.csv", ... ReadAs.TEXT, ... {"format": "csv", "delimiter": "\\\\|"} - ... ) \\ - ... .setEnablePatternRegex(True) + ... ) >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... tokenizer, @@ -135,11 +129,6 @@ class EntityRulerApproach(AnnotatorApproach, HasStorage): "Resource in JSON or CSV format to map entities to patterns", typeConverter=TypeConverters.identity) - enablePatternRegex = Param(Params._dummy(), - "enablePatternRegex", - "Enables regex pattern match", - typeConverter=TypeConverters.toBoolean) - useStorage = Param(Params._dummy(), "useStorage", "Whether to use RocksDB storage to serialize patterns", @@ -174,16 +163,6 @@ def setPatternsResource(self, path, read_as=ReadAs.TEXT, options={"format": "JSO """ return self._set(patternsResource=ExternalResource(path, read_as, options)) - def setEnablePatternRegex(self, value): - """Sets whether to enable regex pattern matching. - - Parameters - ---------- - value : bool - Whether to enable regex pattern matching. - """ - return self._set(enablePatternRegex=value) - def setUseStorage(self, value): """Sets whether to use RocksDB storage to serialize patterns. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala index 1fe96c9d45cc..19efe6bdad5a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala @@ -44,7 +44,6 @@ import scala.io.Source * `setPatternsResource`. The file format needs to be set as the "format" field in the `option` * parameter map and depending on the file type, additional parameters might need to be set. * - * To enable regex extraction, `setEnablePatternRegex(true)` needs to be called. * * If the file is in a JSON format, then the rule definitions need to be given in a list with the * fields "id", "label" and "patterns": @@ -115,7 +114,6 @@ import scala.io.Source * readAs = ReadAs.TEXT, * options = Map("format" -> "csv", "delimiter" -> "\\|") * ) - * .setEnablePatternRegex(true) * * val pipeline = new Pipeline().setStages(Array( * documentAssembler, @@ -170,19 +168,11 @@ class EntityRulerApproach(override val uid: String) * * @group param */ - val patternsResource = new ExternalResourceParam( + val patternsResource: ExternalResourceParam = new ExternalResourceParam( this, "patternsResource", "Resource in JSON or CSV format to map entities to patterns") - /** Enables regex pattern match (Default: `false`). - * - * @group param - */ - @deprecated("Enabling pattern regex now is define on each pattern", "Since 4.2.0") - val enablePatternRegex = - new BooleanParam(this, "enablePatternRegex", "Enables regex pattern match") - val sentenceMatch = new BooleanParam( this, "sentenceMatch", @@ -200,10 +190,6 @@ class EntityRulerApproach(override val uid: String) "alphabet", "Alphabet resource path to plain text file with all characters in a given alphabet") - /** @group setParam */ - @deprecated("Enabling pattern regex now is define on each pattern", "4.2.0") - def setEnablePatternRegex(value: Boolean): this.type = set(enablePatternRegex, value) - /** @group setParam */ def setPatternsResource( path: String, @@ -224,7 +210,6 @@ class EntityRulerApproach(override val uid: String) setDefault( storagePath -> ExternalResource("", ReadAs.TEXT, Map()), patternsResource -> null, - enablePatternRegex -> false, useStorage -> false, sentenceMatch -> false, caseSensitive -> true, @@ -568,10 +553,7 @@ class EntityRulerApproach(override val uid: String) } protected def createWriter(database: Name, connection: RocksDBConnection): StorageWriter[_] = { - database match { - case Database.ENTITY_PATTERNS => new PatternsReadWriter(connection) - case Database.ENTITY_REGEX_PATTERNS => new RegexPatternsReadWriter(connection) - } + new RegexPatternsReadWriter(connection) } override def indexStorage(fitDataset: Dataset[_], resource: Option[ExternalResource]): Unit = { diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala index fe0ddd7723d4..eab4a1dd52b9 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala @@ -20,7 +20,7 @@ import com.johnsnowlabs.nlp.AnnotatorType.{CHUNK, DOCUMENT, TOKEN} import com.johnsnowlabs.nlp.annotators.common._ import com.johnsnowlabs.nlp.serialization.StructFeature import com.johnsnowlabs.nlp.{Annotation, AnnotatorModel, HasPretrained, HasSimpleAnnotate} -import com.johnsnowlabs.storage.Database.{ENTITY_PATTERNS, ENTITY_REGEX_PATTERNS, Name} +import com.johnsnowlabs.storage.Database.{ENTITY_REGEX_PATTERNS, Name} import com.johnsnowlabs.storage._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.ml.PipelineModel @@ -60,7 +60,7 @@ class EntityRulerModel(override val uid: String) private val logger: Logger = LoggerFactory.getLogger("Credentials") - @deprecated("Enabling pattern regex now is define on each pattern", "Since 4.2.0") + // Keeping this parameter for backward compatibility private[er] val enablePatternRegex = new BooleanParam(this, "enablePatternRegex", "Enables regex pattern match") @@ -83,10 +83,6 @@ class EntityRulerModel(override val uid: String) private[er] val ahoCorasickAutomaton: StructFeature[Option[AhoCorasickAutomaton]] = new StructFeature[Option[AhoCorasickAutomaton]](this, "AhoCorasickAutomaton") - @deprecated("Enabling pattern regex now is define on each pattern", "Since 4.2.0") - private[er] def setEnablePatternRegex(value: Boolean): this.type = - set(enablePatternRegex, value) - private[er] def setRegexEntities(value: Array[String]): this.type = set(regexEntities, value) private[er] def setEntityRulerFeatures(value: EntityRulerFeatures): this.type = @@ -118,7 +114,7 @@ class EntityRulerModel(override val uid: String) } } - setDefault(useStorage -> false, caseSensitive -> true) + setDefault(useStorage -> false, caseSensitive -> true, enablePatternRegex -> false) /** Annotator reference id. Used to identify elements in metadata or to refer to this annotator * type @@ -321,10 +317,7 @@ class EntityRulerModel(override val uid: String) protected val databases: Array[Name] = EntityRulerModel.databases protected def createReader(database: Name, connection: RocksDBConnection): StorageReader[_] = { - database match { - case Database.ENTITY_PATTERNS => new PatternsReader(connection) - case Database.ENTITY_REGEX_PATTERNS => new RegexPatternsReader(connection) - } + new RegexPatternsReader(connection) } } @@ -332,7 +325,7 @@ trait ReadablePretrainedEntityRuler extends StorageReadable[EntityRulerModel] with HasPretrained[EntityRulerModel] { - override val databases: Array[Name] = Array(ENTITY_PATTERNS, ENTITY_REGEX_PATTERNS) + override val databases: Array[Name] = Array(ENTITY_REGEX_PATTERNS) override val defaultModelName: Option[String] = None diff --git a/src/main/scala/com/johnsnowlabs/storage/Database.scala b/src/main/scala/com/johnsnowlabs/storage/Database.scala index 8a76fa8fa168..02d08fb81c99 100644 --- a/src/main/scala/com/johnsnowlabs/storage/Database.scala +++ b/src/main/scala/com/johnsnowlabs/storage/Database.scala @@ -36,10 +36,6 @@ object Database { val TMNODES: Name = new Name { override val name: String = "TMNODES" } - @deprecated - val ENTITY_PATTERNS: Name = new Name { - override val name: String = "ENTITY_PATTERNS" - } val ENTITY_REGEX_PATTERNS: Name = new Name { override val name: String = "ENTITY_REGEX_PATTERNS" } From 87127c621dec8b5485634d63e889a727b8ad2976 Mon Sep 17 00:00:00 2001 From: Devin Ha <33089471+DevinTDHa@users.noreply.github.com> Date: Thu, 6 Apr 2023 19:57:30 +0200 Subject: [PATCH 02/26] SPARKNLP-748: Custom Entity Name for Date2Chunk (#13680) - added parameter "entityName" to change metadata name --- python/sparknlp/annotator/date2_chunk.py | 26 +++++++-- python/test/annotator/date2_chunk_test.py | 3 +- .../nlp/annotators/Date2Chunk.scala | 19 +++++- .../johnsnowlabs/nlp/Date2ChunkTestSpec.scala | 58 ++++++++++++++++--- 4 files changed, 90 insertions(+), 16 deletions(-) diff --git a/python/sparknlp/annotator/date2_chunk.py b/python/sparknlp/annotator/date2_chunk.py index 3bbe15e5c546..39e63fa48adf 100755 --- a/python/sparknlp/annotator/date2_chunk.py +++ b/python/sparknlp/annotator/date2_chunk.py @@ -29,16 +29,17 @@ class Date2Chunk(AnnotatorModel): Parameters ---------- - None + entityName + Entity name for the metadata, by default ``"DATE"``. Examples -------- >>> from pyspark.ml import Pipeline -import sparknlp -from sparknlp.base import * -from sparknlp.annotator import * -documentAssembler = DocumentAssembler() \\ + >>> import sparknlp + >>> from sparknlp.base import * + >>> from sparknlp.annotator import * + >>> documentAssembler = DocumentAssembler() \\ ... .setInputCol("text") \\ ... .setOutputCol("document") >>> date = DateMatcher() \\ @@ -70,3 +71,18 @@ class Date2Chunk(AnnotatorModel): @keyword_only def __init__(self): super(Date2Chunk, self).__init__(classname="com.johnsnowlabs.nlp.annotators.Date2Chunk") + self._setDefault(entityName="DATE") + + entityName = Param(Params._dummy(), "entityName", "Entity name for the metadata", + TypeConverters.toString) + + def setEntityName(self, name): + """Sets Learning Rate, by default 0.001. + + Parameters + ---------- + v : float + Learning Rate + """ + self._set(entityName=name) + return self diff --git a/python/test/annotator/date2_chunk_test.py b/python/test/annotator/date2_chunk_test.py index de3715f456c8..4918a866945b 100644 --- a/python/test/annotator/date2_chunk_test.py +++ b/python/test/annotator/date2_chunk_test.py @@ -51,7 +51,8 @@ def runTest(self): date_to_chunk = Date2Chunk() \ .setInputCols(['date']) \ - .setOutputCol("date_chunk") + .setOutputCol("date_chunk") \ + .setEntityName("DATUM") pipeline = Pipeline(stages=[ document_assembler, diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/Date2Chunk.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/Date2Chunk.scala index 2641716e9f37..5d558a7a1495 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/Date2Chunk.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/Date2Chunk.scala @@ -18,12 +18,13 @@ package com.johnsnowlabs.nlp.annotators import com.johnsnowlabs.nlp.AnnotatorType._ import com.johnsnowlabs.nlp.{Annotation, AnnotatorModel, HasSimpleAnnotate} +import org.apache.spark.ml.param.Param import org.apache.spark.ml.util.{DefaultParamsReadable, Identifiable} /** Converts `DATE` type Annotations to `CHUNK` type. * * This can be useful if the following annotators after DateMatcher and MultiDateMatcher require - * `CHUNK` types. + * `CHUNK` types. The entity name in the metadata can be changed with `setEntityName`. * * ==Example== * {{{ @@ -104,6 +105,20 @@ class Date2Chunk(override val uid: String) def this() = this(Identifiable.randomUID("Date2Chunk")) + /** Entity name for metadata (Default: `DATE`) + * + * @group param + */ + val entityName = + new Param[String](this, "entityName", "Entity name for the metadata") + + /** Sets entity name for metadata (Default: `DATE`) + * + * @group setParam + */ + def setEntityName(name: String): this.type = set(this.entityName, name) + + setDefault(entityName -> "DATE") override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = { annotations.map { date => Annotation( @@ -111,7 +126,7 @@ class Date2Chunk(override val uid: String) date.begin, date.end, date.result, - date.metadata ++ Map("entity" -> "DATE", "chunk" -> "0")) + date.metadata ++ Map("entity" -> $(entityName), "chunk" -> "0")) } } diff --git a/src/test/scala/com/johnsnowlabs/nlp/Date2ChunkTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/Date2ChunkTestSpec.scala index 678164d29c46..204b9118d5f6 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/Date2ChunkTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/Date2ChunkTestSpec.scala @@ -25,19 +25,15 @@ import org.scalatest.flatspec.AnyFlatSpec class Date2ChunkTestSpec extends AnyFlatSpec { - "Date2Chunk" should "correctly converts DATE to CHUNK type" taggedAs FastTest in { + behavior of "Date2Chunk" + + it should "correctly converts DATE to CHUNK type" taggedAs FastTest in { val data: Dataset[Row] = DataBuilder.multipleDataBuild( Array( """Omicron is a new variant of COVID-19, which the World Health Organization designated a variant of concern on Nov. 26, 2021/26/11.""", """Neighbouring Austria has already locked down its population this week for at until 2021/10/12, becoming the first to reimpose such restrictions.""")) - val dateChunkAnswer = Array( - Seq[Annotation]( - Annotation(AnnotatorType.CHUNK, 118, 121, "2021/01/01", Map("sentence" -> "0"))), - Seq[Annotation]( - Annotation(AnnotatorType.CHUNK, 83, 86, "2021/01/01", Map("sentence" -> "0")))) - val inputFormats = Array("yyyy", "yyyy/dd/MM", "MM/yyyy", "yyyy") val outputFormat = "yyyy/MM/dd" @@ -90,7 +86,53 @@ class Date2ChunkTestSpec extends AnyFlatSpec { .transform(data) .select("multi_date_chunk.metadata") .show(false) - } + it should "be able to accept different entity names" taggedAs FastTest in { + + val data: Dataset[Row] = DataBuilder.multipleDataBuild( + Array( + """Omicron is a new variant of COVID-19, which the World Health Organization designated a variant of concern on Nov. 26, 2021/26/11.""", + """Neighbouring Austria has already locked down its population this week for at until 2021/10/12, becoming the first to reimpose such restrictions.""")) + + val inputFormats = Array("yyyy", "yyyy/dd/MM", "MM/yyyy", "yyyy") + val outputFormat = "yyyy/MM/dd" + + val date = new DateMatcher() + .setInputCols("document") + .setOutputCol("date") + .setAnchorDateYear(1900) + .setInputFormats(inputFormats) + .setOutputFormat(outputFormat) + + val multiDate = new MultiDateMatcher() + .setInputCols("document") + .setOutputCol("multi_date") + .setAnchorDateYear(1900) + .setInputFormats(inputFormats) + .setOutputFormat(outputFormat) + + val entityName = "DATUM" + val date2Chunk = new Date2Chunk() + .setInputCols("date") + .setOutputCol("date_chunk") + .setEntityName(entityName) + + val multiDate2Chunk = new Date2Chunk() + .setInputCols("multi_date") + .setOutputCol("multi_date_chunk") + .setEntityName(entityName) + + val pipeline = new Pipeline().setStages(Array(date, multiDate, date2Chunk, multiDate2Chunk)) + + val result = pipeline.fit(data).transform(data) + + val collected = Annotation.collect(result, "date_chunk", "multi_date_chunk") + + collected.foreach { annotations => + annotations.foreach { annotation => + assert(annotation.metadata("entity") == entityName) + } + } + } } From dbad9f2c111fb9ce6e0ed20f1a1d739cd25b0c04 Mon Sep 17 00:00:00 2001 From: Danilo Burbano <37355249+danilojsl@users.noreply.github.com> Date: Thu, 6 Apr 2023 13:00:01 -0500 Subject: [PATCH 03/26] SPARKNLP-784 Fix loading WordEmbeddingsModel bug when cache_folder is from S3 (#13707) --- .../johnsnowlabs/client/aws/AWSGateway.scala | 44 ++++++++++++- .../nlp/embeddings/WordEmbeddingsModel.scala | 2 +- .../nlp/pretrained/ResourceDownloader.scala | 16 ++--- .../johnsnowlabs/storage/StorageHelper.scala | 62 ++++++++++++++----- .../johnsnowlabs/storage/StorageLocator.scala | 7 +-- 5 files changed, 101 insertions(+), 30 deletions(-) diff --git a/src/main/scala/com/johnsnowlabs/client/aws/AWSGateway.scala b/src/main/scala/com/johnsnowlabs/client/aws/AWSGateway.scala index 8554cf529721..a65b29ac2685 100644 --- a/src/main/scala/com/johnsnowlabs/client/aws/AWSGateway.scala +++ b/src/main/scala/com/johnsnowlabs/client/aws/AWSGateway.scala @@ -37,6 +37,7 @@ import org.slf4j.{Logger, LoggerFactory} import scala.jdk.CollectionConverters._ import java.io.File +import java.nio.file.Files import scala.util.control.NonFatal class AWSGateway( @@ -185,7 +186,9 @@ class AWSGateway( def downloadFilesFromDirectory( bucketName: String, keyPrefix: String, - directoryPath: File): Unit = { + directoryPath: File, + isIndex: Boolean = false): Unit = { + val transferManager = TransferManagerBuilder .standard() .withS3Client(client) @@ -201,6 +204,45 @@ class AWSGateway( "Amazon service error when downloading files from S3 directory: " + e.getMessage) } transferManager.shutdownNow() + + if (isIndex) { + // Recursively rename the downloaded files to the desired directory path + def renameFiles(directory: File, keySuffix: String): Unit = { + val downloadedFiles = directory.listFiles() + for (file <- downloadedFiles) { + if (file.isDirectory()) { + // If the file is a directory, recursively rename its contents + val subDirectory = new File(directory, file.getName()) + val subKeySuffix = file.getName() + renameFiles(subDirectory, subKeySuffix) + } else { + // Otherwise, rename the file to the desired local file path + val fileName = file.getName() + val newFilePath = + new File(directoryPath, fileName.stripPrefix(keyPrefix)).getPath() + file.renameTo(new File(newFilePath)) + } + } + } + + // Rename the downloaded files to the desired local file path + val keySuffix = keyPrefix.split("/").tail.mkString("/") + renameFiles(directoryPath, keySuffix) + + // Remove all old folders + def removeAllFolders(directoryPath: File): Unit = { + val files = directoryPath.listFiles() + for (file <- files) { + if (file.isDirectory()) { + removeAllFolders(file) + Files.deleteIfExists(file.toPath()) + } + } + } + + removeAllFolders(directoryPath) + } + } private def waitForCompletion(transfer: Transfer): Unit = { diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala index 68a10f7d01d9..23ec8ff9d215 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala @@ -229,7 +229,7 @@ class WordEmbeddingsModel(override val uid: String) WordpieceEmbeddingsSentence.pack(withEmbeddings) } - def retrieveEmbeddings(token: String): (Option[Array[Float]], Array[Float]) = { + private def retrieveEmbeddings(token: String): (Option[Array[Float]], Array[Float]) = { if ($(enableInMemoryStorage)) { val zeroArray = Array.fill[Float]($(dimension))(0f) var embeddings: Option[Array[Float]] = None diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala index a54d679407d5..0a3a22bfb4b0 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala @@ -581,15 +581,17 @@ object ResourceDownloader { /** Downloads the provided S3 path to a local temporary directory and returns the location of * the folder. * - * @param path + * @param s3Path * S3 URL to the resource * @return * URI of the local path to the temporary folder of the resource */ - def downloadS3Directory(path: String): URI = { - - val (bucketName, keyPrefix) = ResourceHelper.parseS3URI(path) + def downloadS3Directory( + s3Path: String, + tempLocalPath: String = "", + isIndex: Boolean = false): URI = { + val (bucketName, keyPrefix) = ResourceHelper.parseS3URI(s3Path) val (accessKey, secretKey, sessionToken) = ConfigHelper.getHadoopS3Config val region = ConfigLoader.getConfigStringValue(ConfigHelper.awsExternalRegion) val privateS3Defined = @@ -605,10 +607,10 @@ object ResourceDownloader { new AWSGateway(credentialsType = "public") } - val tmpDirectory = SparkFiles.getRootDirectory() - awsGateway.downloadFilesFromDirectory(bucketName, keyPrefix, new File(tmpDirectory)) + val directory = if (tempLocalPath.isEmpty) SparkFiles.getRootDirectory() else tempLocalPath + awsGateway.downloadFilesFromDirectory(bucketName, keyPrefix, new File(directory), isIndex) + Paths.get(directory, keyPrefix).toUri - Paths.get(tmpDirectory, keyPrefix).toUri } } diff --git a/src/main/scala/com/johnsnowlabs/storage/StorageHelper.scala b/src/main/scala/com/johnsnowlabs/storage/StorageHelper.scala index 8d99738a6d43..4bc2ad9eb148 100644 --- a/src/main/scala/com/johnsnowlabs/storage/StorageHelper.scala +++ b/src/main/scala/com/johnsnowlabs/storage/StorageHelper.scala @@ -16,6 +16,7 @@ package com.johnsnowlabs.storage +import com.johnsnowlabs.nlp.pretrained.ResourceDownloader import org.apache.hadoop.fs.{FileSystem, FileUtil, Path} import org.apache.spark.sql.SparkSession import org.apache.spark.{SparkContext, SparkFiles} @@ -35,15 +36,14 @@ object StorageHelper { withinStorage: Boolean): RocksDBConnection = { val dbFolder = StorageHelper.resolveStorageName(database, storageRef) - val src = StorageLocator.getStorageSerializedPath( + val source = StorageLocator.getStorageSerializedPath( storageSourcePath.replaceAllLiterally("\\", "/"), dbFolder, withinStorage) val locator = StorageLocator(database, storageRef, spark) - sendToCluster( - src, + source, locator.clusterFilePath, locator.clusterFileName, locator.destinationScheme, @@ -83,40 +83,68 @@ object StorageHelper { destinationScheme: String, sparkContext: SparkContext): Unit = { destinationScheme match { - case "file" => - copyIndexToLocal( - source, - new Path(RocksDBConnection.getLocalPath(clusterFileName)), - sparkContext) + case "file" => { + val destination = new Path(RocksDBConnection.getLocalPath(clusterFileName)) + copyIndexToLocal(source, destination, sparkContext) + } case _ => copyIndexToCluster(source, clusterFilePath, sparkContext) } } - private def copyIndexToCluster(sourcePath: Path, dst: Path, spark: SparkContext): String = { + private def copyIndexToCluster( + sourcePath: Path, + dst: Path, + sparkContext: SparkContext): String = { if (!new File(SparkFiles.get(dst.getName)).exists()) { - val srcFS = sourcePath.getFileSystem(spark.hadoopConfiguration) - val dstFS = dst.getFileSystem(spark.hadoopConfiguration) + val srcFS = sourcePath.getFileSystem(sparkContext.hadoopConfiguration) + val dstFS = dst.getFileSystem(sparkContext.hadoopConfiguration) if (srcFS.getScheme == "file") { val src = sourcePath dstFS.copyFromLocalFile(false, true, src, dst) } else { - FileUtil.copy(srcFS, sourcePath, dstFS, dst, false, true, spark.hadoopConfiguration) + FileUtil.copy( + srcFS, + sourcePath, + dstFS, + dst, + false, + true, + sparkContext.hadoopConfiguration) } - spark.addFile(dst.toString, recursive = true) + sparkContext.addFile(dst.toString, recursive = true) } dst.toString } - private def copyIndexToLocal(source: Path, destination: Path, context: SparkContext): Unit = { + private def copyIndexToLocal( + source: Path, + destination: Path, + sparkContext: SparkContext): Unit = { /** if we don't do a copy, and just move, it will all fail when re-saving utilized storage * because of bad crc */ - val fs = source.getFileSystem(context.hadoopConfiguration) - if (!fs.exists(destination)) - fs.copyFromLocalFile(false, true, source, destination) + val fileSystemDestination = destination.getFileSystem(sparkContext.hadoopConfiguration) + val fileSystemSource = source.getFileSystem(sparkContext.hadoopConfiguration) + + if (fileSystemDestination.exists(destination)) { + return + } + + if (fileSystemSource.getScheme == "s3a" && fileSystemDestination.getScheme == "file") { + ResourceDownloader.downloadS3Directory( + source.toString, + destination.toString, + isIndex = true) + sparkContext.addFile(destination.toString, recursive = true) + return + } + + if (fileSystemDestination.getScheme != "s3a") { + fileSystemDestination.copyFromLocalFile(false, true, source, destination) + } } } diff --git a/src/main/scala/com/johnsnowlabs/storage/StorageLocator.scala b/src/main/scala/com/johnsnowlabs/storage/StorageLocator.scala index 1adbd0012cdc..e7dc43d5ff96 100644 --- a/src/main/scala/com/johnsnowlabs/storage/StorageLocator.scala +++ b/src/main/scala/com/johnsnowlabs/storage/StorageLocator.scala @@ -26,7 +26,7 @@ case class StorageLocator(database: String, storageRef: String, sparkSession: Sp private val clusterTmpLocation: String = { val tmpLocation = getTmpLocation - if (tmpLocation.startsWith("s3:/")) { + if (tmpLocation.matches("s3[a]?:/.*")) { tmpLocation } else { val tmpLocationPath = new Path(tmpLocation) @@ -41,15 +41,14 @@ case class StorageLocator(database: String, storageRef: String, sparkSession: Sp } val clusterFilePath: Path = { - if (!getTmpLocation.startsWith("s3:/")) { + if (!getTmpLocation.matches("s3[a]?:/.*")) { Path.mergePaths( new Path(fileSystem.getUri.toString + clusterTmpLocation), new Path("/" + clusterFileName)) } else new Path(clusterTmpLocation + "/" + clusterFileName) } - val destinationScheme: String = - if (getTmpLocation.startsWith("s3:/")) "s3" else fileSystem.getScheme + val destinationScheme: String = fileSystem.getScheme private def getTmpLocation: String = { ConfigLoader.getConfigStringValue(ConfigHelper.storageTmpDir) From bf4428d1bb507129a213897c3debe87740fa9b86 Mon Sep 17 00:00:00 2001 From: Devin Ha <33089471+DevinTDHa@users.noreply.github.com> Date: Thu, 6 Apr 2023 20:02:51 +0200 Subject: [PATCH 04/26] SPARKNLP-605: ConvNextForImageClassification (#13713) * SPARKNLP-605: ConvNextForImageClassification - Added ConvNextForImageClassification with new tests - Refactored image Preprocessor and added new config - Implemented filters with resample property for ImageResizeUtils.resizeBufferedImage (with minor performance gain) - Minor improvements for ViT and Swin * SPARKNLP-605: Docs * SPARKNLP-605: Lazy values for test --- docs/en/annotators.md | 5 +- .../ConvNextForImageClassification.md | 165 ++++++++ python/sparknlp/annotator/cv/__init__.py | 1 + .../cv/convnext_for_image_classification.py | 269 ++++++++++++ .../cv/swin_for_image_classification.py | 21 +- .../cv/vit_for_image_classification.py | 17 +- python/sparknlp/common/properties.py | 10 +- python/sparknlp/internal/__init__.py | 6 + python/test/README.md | 6 + .../convnext_for_image_classification_test.py | 105 +++++ .../cv/swin_for_image_classification_test.py | 19 +- .../cv/vit_for_image_classification_test.py | 20 +- .../ml/ai/ConvNextClassifier.scala | 68 ++++ .../johnsnowlabs/ml/ai/ViTClassifier.scala | 36 +- .../nlp/HasImageFeatureProperties.scala | 10 +- .../com/johnsnowlabs/nlp/annotator.scala | 14 +- .../cv/ConvNextForImageClassification.scala | 384 ++++++++++++++++++ .../cv/SwinForImageClassification.scala | 51 ++- .../cv/ViTForImageClassification.scala | 42 +- .../cv/feature_extractor/Preprocessor.scala | 129 ++++-- .../cv/util/transform/ImageResizeUtils.scala | 99 +++-- .../nlp/pretrained/ResourceDownloader.scala | 7 +- .../normalized_egyptian_cat.json | 2 - .../image_preprocessor/ox_cropped.png | Bin 0 -> 121340 bytes .../image_preprocessor/ox_small.JPEG | Bin 0 -> 14498 bytes .../preprocessor_config_convnext.json | 23 ++ ...nvNextForImageClassificationTestSpec.scala | 31 ++ .../cv/SwinForImageClassificationTest.scala | 7 +- .../cv/ViTImageClassificationTestSpec.scala | 62 +-- .../ImageUtilsTestSpec.scala | 129 +++--- .../com/johnsnowlabs/util/TestUtils.scala | 46 +++ 31 files changed, 1548 insertions(+), 236 deletions(-) create mode 100644 docs/en/transformer_entries/ConvNextForImageClassification.md create mode 100644 python/sparknlp/annotator/cv/convnext_for_image_classification.py create mode 100644 python/test/annotator/cv/convnext_for_image_classification_test.py create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/ConvNextClassifier.scala create mode 100644 src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala delete mode 100644 src/test/resources/image_preprocessor/normalized_egyptian_cat.json create mode 100644 src/test/resources/image_preprocessor/ox_cropped.png create mode 100644 src/test/resources/image_preprocessor/ox_small.JPEG create mode 100644 src/test/resources/image_preprocessor/preprocessor_config_convnext.json create mode 100644 src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassificationTestSpec.scala create mode 100644 src/test/scala/com/johnsnowlabs/util/TestUtils.scala diff --git a/docs/en/annotators.md b/docs/en/annotators.md index c1f55331a329..3f24ab1c5bb5 100644 --- a/docs/en/annotators.md +++ b/docs/en/annotators.md @@ -40,7 +40,6 @@ There are two types of Annotators: - `pretrained(name, language, extra_location)` -> by default, pre-trained will bring a default model, sometimes we offer more than one model, in this case, you may have to use name, language or extra location to download them. - ## Available Annotators {:.table-model-big} @@ -101,7 +100,8 @@ There are two types of Annotators: {% include templates/anno_table_entry.md path="" name="YakeKeywordExtraction" summary="Unsupervised, Corpus-Independent, Domain and Language-Independent and Single-Document keyword extraction."%} ## Available Transformers -Additionally, these transformers are available to generate embeddings. + +Additionally, these transformers are available. {:.table-model-big} |Transformer|Description|Version| @@ -118,6 +118,7 @@ Additionally, these transformers are available to generate embeddings. {% include templates/anno_table_entry.md path="./transformers" name="CamemBertEmbeddings" summary="CamemBert is based on Facebook’s RoBERTa model released in 2019."%} {% include templates/anno_table_entry.md path="./transformers" name="CamemBertForSequenceClassification" summary="amemBertForSequenceClassification can load CamemBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks."%} {% include templates/anno_table_entry.md path="./transformers" name="CamemBertForTokenClassification" summary="CamemBertForTokenClassification can load CamemBERT Models with a token classification head on top"%} +{% include templates/anno_table_entry.md path="./transformers" name="ConvNextForImageClassification" summary="ConvNextForImageClassification is an image classifier based on ConvNet models"%} {% include templates/anno_table_entry.md path="./transformers" name="DeBertaEmbeddings" summary="DeBERTa builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in RoBERTa."%} {% include templates/anno_table_entry.md path="./transformers" name="DeBertaForQuestionAnswering" summary="DeBertaForQuestionAnswering can load DeBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD."%} {% include templates/anno_table_entry.md path="./transformers" name="DistilBertEmbeddings" summary="DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base."%} diff --git a/docs/en/transformer_entries/ConvNextForImageClassification.md b/docs/en/transformer_entries/ConvNextForImageClassification.md new file mode 100644 index 000000000000..302918971619 --- /dev/null +++ b/docs/en/transformer_entries/ConvNextForImageClassification.md @@ -0,0 +1,165 @@ +{%- capture title -%} +ConvNextForImageClassification +{%- endcapture -%} + +{%- capture description -%} +ConvNextForImageClassification is an image classifier based on ConvNet models. + +The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan +Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional +model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform +them. + +Pretrained models can be loaded with `pretrained` of the companion object: + +```scala +val imageClassifier = ConvNextForImageClassification.pretrained() + .setInputCols("image_assembler") + .setOutputCol("class") +``` + +The default model is `"image_classifier_convnext_tiny_224_local"`, if no name is provided. + +For available pretrained models please see the +[Models Hub](https://nlp.johnsnowlabs.com/models?task=Image+Classification). + +Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To +see which models are compatible and how to import them see +https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended +examples, see +[ConvNextForImageClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassificationTestSpec.scala). + +**References:** + +[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) + +**Paper Abstract:** + +*The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers +(ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. +A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision +tasks such as object detection and semantic segmentation. It is the hierarchical Transformers +(e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers +practically viable as a generic vision backbone and demonstrating remarkable performance on a +wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still +largely credited to the intrinsic superiority of Transformers, rather than the inherent +inductive biases of convolutions. In this work, we reexamine the design spaces and test the +limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward +the design of a vision Transformer, and discover several key components that contribute to the +performance difference along the way. The outcome of this exploration is a family of pure +ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts +compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% +ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K +segmentation, while maintaining the simplicity and efficiency of standard ConvNets.* +{%- endcapture -%} + +{%- capture input_anno -%} +IMAGE +{%- endcapture -%} + +{%- capture output_anno -%} +CATEGORY +{%- endcapture -%} + +{%- capture python_example -%} +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline +imageDF = spark.read \ + .format("image") \ + .option("dropInvalid", value = True) \ + .load("src/test/resources/image/") +imageAssembler = ImageAssembler() \ + .setInputCol("image") \ + .setOutputCol("image_assembler") +imageClassifier = ConvNextForImageClassification \ + .pretrained() \ + .setInputCols(["image_assembler"]) \ + .setOutputCol("class") +pipeline = Pipeline().setStages([imageAssembler, imageClassifier]) +pipelineDF = pipeline.fit(imageDF).transform(imageDF) +pipelineDF \ + .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") \ + .show(truncate=False) ++-----------------+----------------------------------------------------------+ +|image_name |result | ++-----------------+----------------------------------------------------------+ +|bluetick.jpg |[bluetick] | +|chihuahua.jpg |[Chihuahua] | +|egyptian_cat.jpeg|[tabby, tabby cat] | +|hen.JPEG |[hen] | +|hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]| +|junco.JPEG |[junco, snowbird] | +|ostrich.JPEG |[ostrich, Struthio camelus] | +|ox.JPEG |[ox] | +|palace.JPEG |[palace] | +|tractor.JPEG |[thresher, thrasher, threshing machine | ++-----------------+----------------------------------------------------------+ +{%- endcapture -%} + +{%- capture scala_example -%} +import com.johnsnowlabs.nlp.annotator._ +import com.johnsnowlabs.nlp.ImageAssembler +import org.apache.spark.ml.Pipeline + +val imageDF: DataFrame = spark.read + .format("image") + .option("dropInvalid", value = true) + .load("src/test/resources/image/") + +val imageAssembler = new ImageAssembler() + .setInputCol("image") + .setOutputCol("image_assembler") + +val imageClassifier = ConvNextForImageClassification + .pretrained() + .setInputCols("image_assembler") + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier)) +val pipelineDF = pipeline.fit(imageDF).transform(imageDF) + +pipelineDF + .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") + .show(truncate = false) ++-----------------+----------------------------------------------------------+ +|image_name |result | ++-----------------+----------------------------------------------------------+ +|palace.JPEG |[palace] | +|egyptian_cat.jpeg|[tabby, tabby cat] | +|hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]| +|hen.JPEG |[hen] | +|ostrich.JPEG |[ostrich, Struthio camelus] | +|junco.JPEG |[junco, snowbird] | +|bluetick.jpg |[bluetick] | +|chihuahua.jpg |[Chihuahua] | +|tractor.JPEG |[tractor] | +|ox.JPEG |[ox] | ++-----------------+----------------------------------------------------------+ + +{%- endcapture -%} + +{%- capture api_link -%} +[ConvNextForImageClassification](/api/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification) +{%- endcapture -%} + +{%- capture python_api_link -%} +[ConvNextForImageClassification](/api/python/reference/autosummary/sparknlp/annotator/cv/convnext_for_image_classification/index.html#sparknlp.annotator.cv.convnext_for_image_classification.ConvNextForImageClassification) +{%- endcapture -%} + +{%- capture source_link -%} +[ConvNextForImageClassification](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala) +{%- endcapture -%} + +{% include templates/anno_template.md +title=title +description=description +input_anno=input_anno +output_anno=output_anno +python_example=python_example +scala_example=scala_example +api_link=api_link +python_api_link=python_api_link +source_link=source_link +%} \ No newline at end of file diff --git a/python/sparknlp/annotator/cv/__init__.py b/python/sparknlp/annotator/cv/__init__.py index ba2cd000b3cd..3df185a2df4d 100644 --- a/python/sparknlp/annotator/cv/__init__.py +++ b/python/sparknlp/annotator/cv/__init__.py @@ -13,3 +13,4 @@ # limitations under the License. from sparknlp.annotator.cv.vit_for_image_classification import * from sparknlp.annotator.cv.swin_for_image_classification import * +from sparknlp.annotator.cv.convnext_for_image_classification import * diff --git a/python/sparknlp/annotator/cv/convnext_for_image_classification.py b/python/sparknlp/annotator/cv/convnext_for_image_classification.py new file mode 100644 index 000000000000..86d1e99f4ab4 --- /dev/null +++ b/python/sparknlp/annotator/cv/convnext_for_image_classification.py @@ -0,0 +1,269 @@ +# Copyright 2017-2022 John Snow Labs +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Contains classes concerning ConvNextForImageClassification.""" + +from sparknlp.common import * + + +class ConvNextForImageClassification(AnnotatorModel, + HasBatchedAnnotateImage, + HasImageFeatureProperties, + HasEngine): + """ConvNextForImageClassification is an image classifier based on ConvNet models. + + The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan + Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional + model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform + them. + + For available pretrained models please see the + `Models Hub `__. + + Models from the HuggingFace 🤗 Transformers library are also compatible with Spark + NLP 🚀. To see which models are compatible and how to import them see + https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended + examples, see + `ConvNextForImageClassificationTestSpec `__. + + ====================== ====================== + Input Annotation types Output Annotation type + ====================== ====================== + ``IMAGE`` ``CATEGORY`` + ====================== ====================== + + **Paper Abstract:** + + *The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly + superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces + difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is + the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making + Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide + variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the + intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we + reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a + standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to + the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed + ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms + of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO + detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. * + + References + ---------- + + `A ConvNet for the 2020s `__ + + Parameters + ---------- + doResize + Whether to resize the input to a certain size + doNormalize + Whether to normalize the input with mean and standard deviation + featureExtractorType + Name of model's architecture for feature extraction + imageMean + The sequence of means for each channel, to be used when normalizing images + imageStd + The sequence of standard deviations for each channel, to be used when normalizing images + resample + An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BILINEAR` or + `PIL.Image.BICUBIC`. Only has an effect if do_resize is set to True. + size + Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is + provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True. + doRescale + Whether to rescale the image values by rescaleFactor + rescaleFactor + Factor to scale the image values + cropPct + Percentage of the resized image to crop + configProtoBytes + ConfigProto from tensorflow, serialized into byte array. + + Examples + -------- + >>> import sparknlp + >>> from sparknlp.base import * + >>> from sparknlp.annotator import * + >>> from pyspark.ml import Pipeline + >>> imageDF = spark.read \\ + ... .format("image") \\ + ... .option("dropInvalid", value = True) \\ + ... .load("src/test/resources/image/") + >>> imageAssembler = ImageAssembler() \\ + ... .setInputCol("image") \\ + ... .setOutputCol("image_assembler") + >>> imageClassifier = ConvNextForImageClassification \\ + ... .pretrained() \\ + ... .setInputCols(["image_assembler"]) \\ + ... .setOutputCol("class") + >>> pipeline = Pipeline().setStages([imageAssembler, imageClassifier]) + >>> pipelineDF = pipeline.fit(imageDF).transform(imageDF) + >>> pipelineDF \\ + ... .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") \\ + ... .show(truncate=False) + +-----------------+----------------------------------------------------------+ + |image_name |result | + +-----------------+----------------------------------------------------------+ + |bluetick.jpg |[bluetick] | + |chihuahua.jpg |[Chihuahua] | + |egyptian_cat.jpeg|[tabby, tabby cat] | + |hen.JPEG |[hen] | + |hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]| + |junco.JPEG |[junco, snowbird] | + |ostrich.JPEG |[ostrich, Struthio camelus] | + |ox.JPEG |[ox] | + |palace.JPEG |[palace] | + |tractor.JPEG |[thresher, thrasher, threshing machine | + +-----------------+----------------------------------------------------------+ + """ + name = "ConvNextForImageClassification" + + inputAnnotatorTypes = [AnnotatorType.IMAGE] + + outputAnnotatorType = AnnotatorType.CATEGORY + + configProtoBytes = Param(Params._dummy(), + "configProtoBytes", + "ConfigProto from tensorflow, serialized into byte array. Get with " + "config_proto.SerializeToString()", + TypeConverters.toListInt) + + doRescale = Param(Params._dummy(), "doRescale", + "Whether to rescale the image values by rescaleFactor.", + TypeConverters.toBoolean) + + rescaleFactor = Param(Params._dummy(), "rescaleFactor", + "Factor to scale the image values", + TypeConverters.toFloat) + + cropPct = Param(Params._dummy(), "cropPct", + "Percentage of the resized image to crop", + TypeConverters.toFloat) + + def setDoRescale(self, value): + """Sets Whether to rescale the image values by rescaleFactor, by default `True`. + + Parameters + ---------- + value : Boolean + Whether to rescale the image values by rescaleFactor. + """ + return self._set(doRescale=value) + + def setRescaleFactor(self, value): + """Sets Factor to scale the image values, by default `1/255.0`. + + Parameters + ---------- + value : Boolean + Whether to rescale the image values by rescaleFactor. + """ + return self._set(rescaleFactor=value) + + def setCropPct(self, value): + """Determines rescale and crop percentage for images smaller than the configured size, by default `224 / 256`. + + If the image size is smaller than the specified size, the smaller edge of the image will be + matched to `int(size / cropPct)`. Afterwards the image is cropped to `(size, size)`. + + Parameters + ---------- + value : Float + Percentage of the resized image to crop + """ + return self._set(cropPct=value) + + def getClasses(self): + """ + Returns labels used to train this model + """ + return self._call_java("getClasses") + + def setConfigProtoBytes(self, b): + """Sets configProto from tensorflow, serialized into byte array. + + Parameters + ---------- + b : List[int] + ConfigProto from tensorflow, serialized into byte array + """ + return self._set(configProtoBytes=b) + + @keyword_only + def __init__(self, + classname="com.johnsnowlabs.nlp.annotators.cv.ConvNextForImageClassification", + java_model=None): + super(ConvNextForImageClassification, self).__init__( + classname=classname, + java_model=java_model + ) + self._setDefault( + batchSize=2, + doNormalize=True, + doRescale=True, + doResize=True, + imageMean=[0.485, 0.456, 0.406], + imageStd=[0.229, 0.224, 0.225], + resample=3, + size=224, + rescaleFactor=1 / 255.0, + cropPct=224 / 256.0 + ) + + @staticmethod + def loadSavedModel(folder, spark_session): + """Loads a locally saved model. + + Parameters + ---------- + folder : str + Folder of the saved model + spark_session : pyspark.sql.SparkSession + The current SparkSession + + Returns + ------- + ConvNextForImageClassification + The restored model + """ + from sparknlp.internal import _ConvNextForImageClassification + jModel = _ConvNextForImageClassification(folder, + spark_session._jsparkSession)._java_obj + return ConvNextForImageClassification(java_model=jModel) + + @staticmethod + def pretrained(name="image_classifier_convnext_tiny_224_local", lang="en", + remote_loc=None): + """Downloads and loads a pretrained model. + + Parameters + ---------- + name : str, optional + Name of the pretrained model, by default + "image_classifier_convnext_tiny_224_local" + lang : str, optional + Language of the pretrained model, by default "en" + remote_loc : str, optional + Remote address of the resource, by default None. Will use + Spark NLPs repositories otherwise. + + Returns + ------- + ConvNextForImageClassification + The restored model + """ + from sparknlp.pretrained import ResourceDownloader + return ResourceDownloader.downloadModel(ConvNextForImageClassification, name, lang, + remote_loc) diff --git a/python/sparknlp/annotator/cv/swin_for_image_classification.py b/python/sparknlp/annotator/cv/swin_for_image_classification.py index d4a2cff2ce28..388ef4b52858 100644 --- a/python/sparknlp/annotator/cv/swin_for_image_classification.py +++ b/python/sparknlp/annotator/cv/swin_for_image_classification.py @@ -86,7 +86,26 @@ class SwinForImageClassification(AnnotatorModel, Parameters ---------- - + doResize + Whether to resize the input to a certain size + doNormalize + Whether to normalize the input with mean and standard deviation + featureExtractorType + Name of model's architecture for feature extraction + imageMean + The sequence of means for each channel, to be used when normalizing images + imageStd + The sequence of standard deviations for each channel, to be used when normalizing images + resample + An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BILINEAR` or + `PIL.Image.BICUBIC`. Only has an effect if do_resize is set to True. + size + Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is + provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True. + doRescale + Whether to rescale the image values by rescaleFactor + rescaleFactor + Factor to scale the image values configProtoBytes ConfigProto from tensorflow, serialized into byte array. diff --git a/python/sparknlp/annotator/cv/vit_for_image_classification.py b/python/sparknlp/annotator/cv/vit_for_image_classification.py index df3dfbbff7ab..623420f681eb 100644 --- a/python/sparknlp/annotator/cv/vit_for_image_classification.py +++ b/python/sparknlp/annotator/cv/vit_for_image_classification.py @@ -77,7 +77,22 @@ class ViTForImageClassification(AnnotatorModel, Parameters ---------- - + doResize + Whether to resize the input to a certain size + doNormalize + Whether to normalize the input with mean and standard deviation + featureExtractorType + Name of model's architecture for feature extraction + imageMean + The sequence of means for each channel, to be used when normalizing images + imageStd + The sequence of standard deviations for each channel, to be used when normalizing images + resample + An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BILINEAR` or + `PIL.Image.BICUBIC`. Only has an effect if do_resize is set to True. + size + Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is + provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True. configProtoBytes ConfigProto from tensorflow, serialized into byte array. diff --git a/python/sparknlp/common/properties.py b/python/sparknlp/common/properties.py index 855f8929db0d..d9eb3d8bcbe6 100644 --- a/python/sparknlp/common/properties.py +++ b/python/sparknlp/common/properties.py @@ -187,9 +187,8 @@ class HasImageFeatureProperties: TypeConverters.toListFloat) resample = Param(Params._dummy(), "resample", - "An optional resampling filter. This can be one of PIL.Image.NEAREST, PIL.Image.BOX, " - "PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC or PIL.Image.LANCZOS. Only has an " - "effect if do_resize is set to True", + "An optional resampling filter. This can be one of PIL.Image.NEAREST, PIL.Image.BILINEAR or " + "PIL.Image.BICUBIC. Only has an effect if do_resize is set to True.", TypeConverters.toInt) size = Param(Params._dummy(), "size", @@ -254,9 +253,8 @@ def setResample(self, value): Parameters ---------- value : int - An optional resampling filter. This can be one of PIL.Image.NEAREST, - PIL.Image.BOX, PIL.Image.BILINEAR PIL.Image.HAMMING, PIL.Image.BICUBIC or PIL.Image.LANCZOS. Only has an - effect if do_resize is set to True + Resampling filter for resizing. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BILINEAR` or + `PIL.Image.BICUBIC`. Only has an effect if `do_resize` is set to `True`. """ return self._set(resample=value) diff --git a/python/sparknlp/internal/__init__.py b/python/sparknlp/internal/__init__.py index 4d609d8cb0b7..d33c9d2fd26c 100644 --- a/python/sparknlp/internal/__init__.py +++ b/python/sparknlp/internal/__init__.py @@ -452,6 +452,12 @@ def __init__(self, path, jspark): "com.johnsnowlabs.nlp.annotators.cv.SwinForImageClassification.loadSavedModel", path, jspark) +class _ConvNextForImageClassification(ExtendedJavaWrapper): + def __init__(self, path, jspark): + super(_ConvNextForImageClassification, self).__init__( + "com.johnsnowlabs.nlp.annotators.cv.ConvNextForImageClassification.loadSavedModel", path, jspark) + + class _Wav2Vec2ForCTC(ExtendedJavaWrapper): def __init__(self, path, jspark): super(_Wav2Vec2ForCTC, self).__init__( diff --git a/python/test/README.md b/python/test/README.md index d4b0f339a198..67555542153d 100644 --- a/python/test/README.md +++ b/python/test/README.md @@ -34,6 +34,12 @@ tests that contain "NerDL" we can run these tests like so: pytest -v -k "NerDL" ``` +### JetBrains IDEs + +To run individual tests in a JetBrains IDE (i.e. IntelliJ or PyCharm) there might be issues when using to interface to +run the tests. To fix this, go to _Project (Alt+1)_, choose the `python` folder and mark the directory with _Mark +Directory as > Test Sources Root_. + # Tests with unittest Third party app integration still uses the old unittests, as these require additional dependencies that are not needed for the other tests. diff --git a/python/test/annotator/cv/convnext_for_image_classification_test.py b/python/test/annotator/cv/convnext_for_image_classification_test.py new file mode 100644 index 000000000000..73c2b4c59ad7 --- /dev/null +++ b/python/test/annotator/cv/convnext_for_image_classification_test.py @@ -0,0 +1,105 @@ +# Copyright 2017-2022 John Snow Labs +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import unittest + +import pytest + +from sparknlp.annotator import * +from sparknlp.base import * +from test.util import SparkSessionForTest + + +class ConvNextForImageClassificationTestSetUp(unittest.TestCase): + gold_standards = { + "bluetick.jpg": "bluetick", + "chihuahua.jpg": "Chihuahua", + "egyptian_cat.jpeg": "tabby, tabby cat", + "hen.JPEG": "hen", + "hippopotamus.JPEG": "hippopotamus, hippo, river horse, Hippopotamus amphibius", + "junco.JPEG": "junco, snowbird", + "ostrich.JPEG": "ostrich, Struthio camelus", + "ox.JPEG": "ox", + "palace.JPEG": "palace", + "tractor.JPEG": "thresher, thrasher, threshing machine", + } + + def setUp(self): + self.images_path = os.getcwd() + "/../src/test/resources/image/" + self.data = SparkSessionForTest.spark.read.format("image") \ + .load(path=self.images_path) + + image_assembler = ImageAssembler() \ + .setInputCol("image") \ + .setOutputCol("image_assembler") + + imageClassifier = ConvNextForImageClassification \ + .pretrained() \ + .setInputCols("image_assembler") \ + .setOutputCol("class") + + pipeline = Pipeline(stages=[ + image_assembler, + imageClassifier, + ]) + + self.model = pipeline.fit(self.data) + + +@pytest.mark.slow +class ConvNextForImageClassificationTestSpec(ConvNextForImageClassificationTestSetUp, unittest.TestCase): + def setUp(self): + super().setUp() + + def runTest(self): + result = self.model.transform(self.data).select("image.origin", "class.result").collect() + + for row in result: + file_name = row["origin"].rsplit("/", 1)[-1] + self.assertEqual(self.gold_standards[file_name], row["result"][0]) + + +@pytest.mark.slow +class LightConvNextForImageClassificationOneImageTestSpec(ConvNextForImageClassificationTestSetUp, unittest.TestCase): + + def setUp(self): + super().setUp() + + def runTest(self): + light_pipeline = LightPipeline(self.model) + + annotations_result = light_pipeline.fullAnnotateImage(self.images_path + "hippopotamus.JPEG") + + self.assertEqual(len(annotations_result), 1) + for result in annotations_result: + self.assertTrue(len(result["image_assembler"]) > 0) + self.assertTrue(len(result["class"]) > 0) + + +@pytest.mark.slow +class LightConvNextForImageClassificationTestSpec(ConvNextForImageClassificationTestSetUp, unittest.TestCase): + + def setUp(self): + super().setUp() + + def runTest(self): + light_pipeline = LightPipeline(self.model) + images = [self.images_path + "hippopotamus.JPEG", self.images_path + "egyptian_cat.jpeg"] + + annotations_result = light_pipeline.fullAnnotateImage(images) + + self.assertEqual(len(annotations_result), len(images)) + for result in annotations_result: + self.assertTrue(len(result["image_assembler"]) > 0) + self.assertTrue(len(result["class"]) > 0) diff --git a/python/test/annotator/cv/swin_for_image_classification_test.py b/python/test/annotator/cv/swin_for_image_classification_test.py index f7d4bbba0285..12b379964e87 100644 --- a/python/test/annotator/cv/swin_for_image_classification_test.py +++ b/python/test/annotator/cv/swin_for_image_classification_test.py @@ -22,6 +22,18 @@ class SwinForImageClassificationTestSetUp(unittest.TestCase): + gold_standards = { + "bluetick.jpg": "bluetick", + "chihuahua.jpg": "Chihuahua", + "egyptian_cat.jpeg": "tabby, tabby cat", + "hen.JPEG": "hen", + "hippopotamus.JPEG": "hippopotamus, hippo, river horse, Hippopotamus amphibius", + "junco.JPEG": "junco, snowbird", + "ostrich.JPEG": "ostrich, Struthio camelus", + "ox.JPEG": "ox", + "palace.JPEG": "palace", + "tractor.JPEG": "tractor", + } def setUp(self): self.images_path = os.getcwd() + "/../src/test/resources/image/" self.data = SparkSessionForTest.spark.read.format("image") \ @@ -50,10 +62,11 @@ def setUp(self): super().setUp() def runTest(self): + result = self.model.transform(self.data).select("image.origin", "class.result").collect() - result_df = self.model.transform(self.data) - - self.assertTrue(result_df.select("class").count() > 0) + for row in result: + file_name = row["origin"].rsplit("/", 1)[-1] + self.assertEqual(self.gold_standards[file_name], row["result"][0]) @pytest.mark.slow diff --git a/python/test/annotator/cv/vit_for_image_classification_test.py b/python/test/annotator/cv/vit_for_image_classification_test.py index 29a49c4054ab..be7cb5e1d350 100644 --- a/python/test/annotator/cv/vit_for_image_classification_test.py +++ b/python/test/annotator/cv/vit_for_image_classification_test.py @@ -22,6 +22,19 @@ class ViTForImageClassificationTestSetUp(unittest.TestCase): + gold_standards = { + "bluetick.jpg": "bluetick", + "chihuahua.jpg": "Chihuahua", + "egyptian_cat.jpeg": "Egyptian cat", + "hen.JPEG": "hen", + "hippopotamus.JPEG": "hippopotamus, hippo, river horse, Hippopotamus amphibius", + "junco.JPEG": "junco, snowbird", + "ostrich.JPEG": "ostrich, Struthio camelus", + "ox.JPEG": "ox", + "palace.JPEG": "palace", + "tractor.JPEG": "tractor", + } + def setUp(self): self.images_path = os.getcwd() + "/../src/test/resources/image/" self.data = SparkSessionForTest.spark.read.format("image") \ @@ -50,10 +63,11 @@ def setUp(self): super().setUp() def runTest(self): + result = self.model.transform(self.data).select("image.origin", "class.result").collect() - result_df = self.model.transform(self.data) - - self.assertTrue(result_df.select("class").count() > 0) + for row in result: + file_name = row["origin"].rsplit("/", 1)[-1] + self.assertEqual(self.gold_standards[file_name], row["result"][0]) @pytest.mark.slow diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/ConvNextClassifier.scala b/src/main/scala/com/johnsnowlabs/ml/ai/ConvNextClassifier.scala new file mode 100644 index 000000000000..523122b93ea9 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/ConvNextClassifier.scala @@ -0,0 +1,68 @@ +/* + * Copyright 2017-2022 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai + +import com.johnsnowlabs.ml.tensorflow.TensorflowWrapper +import com.johnsnowlabs.nlp._ +import com.johnsnowlabs.nlp.annotators.cv.feature_extractor.Preprocessor +import com.johnsnowlabs.nlp.annotators.cv.util.io.ImageIOUtils +import com.johnsnowlabs.nlp.annotators.cv.util.transform.ImageResizeUtils + +private[johnsnowlabs] class ConvNextClassifier( + tensorflowWrapper: TensorflowWrapper, + configProtoBytes: Option[Array[Byte]] = None, + tags: Map[String, BigInt], + preprocessor: Preprocessor, + signatures: Option[Map[String, String]] = None) + extends ViTClassifier(tensorflowWrapper, configProtoBytes, tags, preprocessor, signatures) { + + override def encode( + annotations: Array[AnnotationImage], + preprocessor: Preprocessor): Array[Array[Array[Array[Float]]]] = { + annotations.map { annot => + val bufferedImage = ImageIOUtils.byteToBufferedImage( + bytes = annot.result, + w = annot.width, + h = annot.height, + nChannels = annot.nChannels) + + val resizedImage = + if (preprocessor.crop_pct.isDefined && preprocessor.size < 384) + ImageResizeUtils.resizeAndCenterCropImage( + bufferedImage, + requestedSize = preprocessor.size, + cropPct = preprocessor.crop_pct.get, + resample = preprocessor.resample) + else + ImageResizeUtils.resizeBufferedImage( + width = preprocessor.size, + height = preprocessor.size, + resample = preprocessor.resample)(bufferedImage) + + val normalizedImage = ImageResizeUtils.normalizeAndConvertBufferedImage( + img = resizedImage, + mean = preprocessor.image_mean, + std = preprocessor.image_std, + doNormalize = preprocessor.do_normalize, + doRescale = preprocessor.do_rescale, + rescaleFactor = preprocessor.rescale_factor) + + normalizedImage + } + } + +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/ViTClassifier.scala b/src/main/scala/com/johnsnowlabs/ml/ai/ViTClassifier.scala index a90480913c85..6a7e81171627 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/ViTClassifier.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/ViTClassifier.scala @@ -29,10 +29,7 @@ private[johnsnowlabs] class ViTClassifier( val tensorflowWrapper: TensorflowWrapper, configProtoBytes: Option[Array[Byte]] = None, tags: Map[String, BigInt], - imageMean: Array[Double], - imageStd: Array[Double], - resample: Int, - size: Int, + preprocessor: Preprocessor, signatures: Option[Map[String, String]] = None) extends Serializable { @@ -43,15 +40,6 @@ private[johnsnowlabs] class ViTClassifier( val image = ImageIOUtils.loadImage(getClass.getResourceAsStream("/image/ox.JPEG")) val bytes = ImageIOUtils.bufferedImageToByte(image.get) - val preprocessor = - Preprocessor( - do_normalize = true, - do_resize = true, - "ViTFeatureExtractor", - imageMean, - imageStd, - resample, - size) val images = Array(AnnotationImage("image", "ox.JPEG", 265, 360, 3, 16, bytes, Map("image" -> "0"))) val encoded = encode(images, preprocessor) @@ -171,17 +159,23 @@ private[johnsnowlabs] class ViTClassifier( h = annot.height, nChannels = annot.nChannels) - val resizedImage = + val resizedImage = if (preprocessor.do_resize) { ImageResizeUtils.resizeBufferedImage( width = preprocessor.size, height = preprocessor.size, - Some(annot.nChannels))(bufferedImage) - - ImageResizeUtils.normalizeBufferedImage( - img = resizedImage, - mean = preprocessor.image_mean, - std = preprocessor.image_std, - rescaleFactor = preprocessor.rescale_factor) + preprocessor.resample)(bufferedImage) + } else bufferedImage + + val normalizedImage = + ImageResizeUtils.normalizeAndConvertBufferedImage( + img = resizedImage, + mean = preprocessor.image_mean, + std = preprocessor.image_std, + doNormalize = preprocessor.do_normalize, + doRescale = preprocessor.do_rescale, + rescaleFactor = preprocessor.rescale_factor) + + normalizedImage } batchProcessedImages diff --git a/src/main/scala/com/johnsnowlabs/nlp/HasImageFeatureProperties.scala b/src/main/scala/com/johnsnowlabs/nlp/HasImageFeatureProperties.scala index b49ed019f0be..b5a5f5fb91a7 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/HasImageFeatureProperties.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/HasImageFeatureProperties.scala @@ -149,7 +149,15 @@ trait HasImageFeatureProperties extends ParamsAndFeaturesWritable { def getImageStd: Array[Double] = $(imageStd) /** @group getParam */ - def getResample: Int = $(resample) + def getResample: Int = $(resample) match { + // Match to AffineTransformOp + case 0 => 1 // NEAREST + case 2 => 2 // BILINEAR + case 3 => 3 // BICUBIC + case otherValue: Int => + throw new IllegalArgumentException( + s"Invalid value for resampling ($otherValue). Only Nearest Neighbour (0), Bilinear (2) or Bicubic (3) filters are currently supported.") + } /** @group getParam */ def getSize: Int = $(size) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala index f4b121683df7..480ab6b0be7b 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala @@ -29,12 +29,7 @@ import com.johnsnowlabs.nlp.annotators.coref.{ ReadSpanBertCorefTensorflowModel, ReadablePretrainedSpanBertCorefModel } -import com.johnsnowlabs.nlp.annotators.cv.{ - ReadSwinForImageDLModel, - ReadViTForImageDLModel, - ReadablePretrainedSwinForImageModel, - ReadablePretrainedViTForImageModel -} +import com.johnsnowlabs.nlp.annotators.cv._ import com.johnsnowlabs.nlp.annotators.er.ReadablePretrainedEntityRuler import com.johnsnowlabs.nlp.annotators.ld.dl.{ ReadLanguageDetectorDLTensorflowModel, @@ -645,6 +640,13 @@ package object annotator { extends ReadablePretrainedSwinForImageModel with ReadSwinForImageDLModel + type ConvNextForImageClassification = + com.johnsnowlabs.nlp.annotators.cv.ConvNextForImageClassification + + object ConvNextForImageClassification + extends ReadablePretrainedConvNextForImageModel + with ReadConvNextForImageDLModel + type CamemBertForQuestionAnswering = com.johnsnowlabs.nlp.annotators.classifier.dl.CamemBertForQuestionAnswering diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala new file mode 100644 index 000000000000..bfb5128ad002 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala @@ -0,0 +1,384 @@ +/* + * Copyright 2017-2022 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp.annotators.cv + +import com.johnsnowlabs.ml.ai.ConvNextClassifier +import com.johnsnowlabs.ml.tensorflow.{ReadTensorflowModel, TensorflowWrapper} +import com.johnsnowlabs.ml.util.LoadExternalModel.{ + loadJsonStringAsset, + modelSanityCheck, + notSupportedEngineError +} +import com.johnsnowlabs.ml.util.ModelEngine +import com.johnsnowlabs.nlp._ +import com.johnsnowlabs.nlp.annotators.cv.feature_extractor.Preprocessor +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.ml.param.DoubleParam +import org.apache.spark.ml.util.Identifiable +import org.apache.spark.sql.SparkSession +import org.json4s._ +import org.json4s.jackson.JsonMethods._ + +/** ConvNextForImageClassification is an image classifier based on ConvNet models. + * + * The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan + * Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional + * model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform + * them. + * + * Pretrained models can be loaded with `pretrained` of the companion object: + * {{{ + * val imageClassifier = ConvNextForImageClassification.pretrained() + * .setInputCols("image_assembler") + * .setOutputCol("class") + * }}} + * The default model is `"image_classifier_convnext_tiny_224_local"`, if no name is provided. + * + * For available pretrained models please see the + * [[https://nlp.johnsnowlabs.com/models?task=Image+Classification Models Hub]]. + * + * Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To + * see which models are compatible and how to import them see + * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended + * examples, see + * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassificationTestSpec.scala ConvNextForImageClassificationTestSpec]]. + * + * '''References:''' + * + * [[https://arxiv.org/abs/2201.03545 A ConvNet for the 2020s]] + * + * '''Paper Abstract:''' + * + * ''The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers + * (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. + * A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision + * tasks such as object detection and semantic segmentation. It is the hierarchical Transformers + * (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers + * practically viable as a generic vision backbone and demonstrating remarkable performance on a + * wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still + * largely credited to the intrinsic superiority of Transformers, rather than the inherent + * inductive biases of convolutions. In this work, we reexamine the design spaces and test the + * limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward + * the design of a vision Transformer, and discover several key components that contribute to the + * performance difference along the way. The outcome of this exploration is a family of pure + * ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts + * compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% + * ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K + * segmentation, while maintaining the simplicity and efficiency of standard ConvNets. '' + * + * ==Example== + * {{{ + * import com.johnsnowlabs.nlp.annotator._ + * import com.johnsnowlabs.nlp.ImageAssembler + * import org.apache.spark.ml.Pipeline + * + * val imageDF: DataFrame = spark.read + * .format("image") + * .option("dropInvalid", value = true) + * .load("src/test/resources/image/") + * + * val imageAssembler = new ImageAssembler() + * .setInputCol("image") + * .setOutputCol("image_assembler") + * + * val imageClassifier = ConvNextForImageClassification + * .pretrained() + * .setInputCols("image_assembler") + * .setOutputCol("class") + * + * val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier)) + * val pipelineDF = pipeline.fit(imageDF).transform(imageDF) + * + * pipelineDF + * .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") + * .show(truncate = false) + * +-----------------+----------------------------------------------------------+ + * |image_name |result | + * +-----------------+----------------------------------------------------------+ + * |palace.JPEG |[palace] | + * |egyptian_cat.jpeg|[tabby, tabby cat] | + * |hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]| + * |hen.JPEG |[hen] | + * |ostrich.JPEG |[ostrich, Struthio camelus] | + * |junco.JPEG |[junco, snowbird] | + * |bluetick.jpg |[bluetick] | + * |chihuahua.jpg |[Chihuahua] | + * |tractor.JPEG |[tractor] | + * |ox.JPEG |[ox] | + * +-----------------+----------------------------------------------------------+ + * }}} + * + * @param uid + * required uid for storing annotator to disk + * @groupname anno Annotator types + * @groupdesc anno + * Required input and expected output annotator types + * @groupname Ungrouped Members + * @groupname param Parameters + * @groupname setParam Parameter setters + * @groupname getParam Parameter getters + * @groupname Ungrouped Members + * @groupprio param 1 + * @groupprio anno 2 + * @groupprio Ungrouped 3 + * @groupprio setParam 4 + * @groupprio getParam 5 + * @groupdesc param + * A list of (hyper-)parameter keys this annotator can take. Users can set and get the + * parameter values through setters and getters, respectively. + */ +class ConvNextForImageClassification(override val uid: String) + extends SwinForImageClassification(uid) { + + /** Annotator reference id. Used to identify elements in metadata or to refer to this annotator + * type + */ + def this() = this(Identifiable.randomUID("ConvNextForImageClassification")) + + /** Determines rescale and crop percentage for images smaller than the configured size (Default: + * `224 / 256d`). + * + * If the image size is smaller than the specified size, the smaller edge of the image will be + * matched to `int(size / cropPct)`. Afterwards the image is cropped to `(size, size)`. + * + * @group param + */ + val cropPct = + new DoubleParam(this, "cropPct", "Percentage of the resized image to crop") + + /** @group setParam */ + def setCropPct(value: Double): this.type = set(this.cropPct, value) + + /** @group getParam */ + def getCropPct: Double = $(cropPct) + + setDefault( + batchSize -> 2, + doNormalize -> true, + doRescale -> true, + doResize -> true, + imageMean -> Array(0.485d, 0.456d, 0.406d), + imageStd -> Array(0.229d, 0.224d, 0.225d), + resample -> 3, + size -> 224, + rescaleFactor -> 1 / 255d, + cropPct -> 224 / 256d) + + private var _model: Option[Broadcast[ConvNextClassifier]] = None + + /** @group getParam */ + override def getModelIfNotSet: ConvNextClassifier = _model.get.value + + override def setModelIfNotSet( + spark: SparkSession, + tensorflow: TensorflowWrapper, + preprocessor: Preprocessor): ConvNextForImageClassification.this.type = { + if (_model.isEmpty) { + + _model = Some( + spark.sparkContext.broadcast( + new ConvNextClassifier( + tensorflow, + configProtoBytes = getConfigProtoBytes, + tags = $$(labels), + preprocessor = preprocessor, + signatures = getSignatures))) + } + this + } + + /** Takes a document and annotations and produces new annotations of this annotator's annotation + * type + * + * @param batchedAnnotations + * Annotations that correspond to inputAnnotationCols generated by previous annotators if any + * @return + * any number of annotations processed for every input annotation. Not necessary one to one + * relationship + */ + override def batchAnnotate( + batchedAnnotations: Seq[Array[AnnotationImage]]): Seq[Seq[Annotation]] = { + // Zip annotations to the row it belongs to + val imagesWithRow = batchedAnnotations.zipWithIndex + .flatMap { case (annotations, i) => annotations.map(x => (x, i)) } + + val noneEmptyImages = imagesWithRow.map(_._1).filter(_.result.nonEmpty).toArray + + val allAnnotations = + if (noneEmptyImages.nonEmpty) { + getModelIfNotSet.predict( + images = noneEmptyImages, + batchSize = $(batchSize), + preprocessor = Preprocessor( + do_normalize = getDoNormalize, + do_resize = getDoResize, + feature_extractor_type = getFeatureExtractorType, + image_mean = getImageMean, + image_std = getImageStd, + resample = getResample, + size = getSize, + do_rescale = getDoRescale, + rescale_factor = getRescaleFactor, + crop_pct = Option(getCropPct))) + } else { + Seq.empty[Annotation] + } + + // Group resulting annotations by rows. If there are not sentences in a given row, return empty sequence + batchedAnnotations.indices.map(rowIndex => { + val rowAnnotations = allAnnotations + // zip each annotation with its corresponding row index + .zip(imagesWithRow) + // select the sentences belonging to the current row + .filter(_._2._2 == rowIndex) + // leave the annotation only + .map(_._1) + + if (rowAnnotations.nonEmpty) + rowAnnotations + else + Seq.empty[Annotation] + }) + + } + + override def onWrite(path: String, spark: SparkSession): Unit = { + writeTensorflowModelV2( + path, + spark, + getModelIfNotSet.tensorflowWrapper, + "_image_classification", + ConvNextForImageClassification.tfFile, + configProtoBytes = getConfigProtoBytes) + } + +} + +trait ReadablePretrainedConvNextForImageModel + extends ParamsAndFeaturesReadable[ConvNextForImageClassification] + with HasPretrained[ConvNextForImageClassification] { + override val defaultModelName: Some[String] = Some("image_classifier_convnext_tiny_224_local") + + /** Java compliant-overrides */ + override def pretrained(): ConvNextForImageClassification = super.pretrained() + + override def pretrained(name: String): ConvNextForImageClassification = super.pretrained(name) + + override def pretrained(name: String, lang: String): ConvNextForImageClassification = + super.pretrained(name, lang) + + override def pretrained( + name: String, + lang: String, + remoteLoc: String): ConvNextForImageClassification = super.pretrained(name, lang, remoteLoc) +} + +trait ReadConvNextForImageDLModel extends ReadTensorflowModel { + this: ParamsAndFeaturesReadable[ConvNextForImageClassification] => + + override val tfFile: String = "image_classification_convnext_tensorflow" + + def readTensorflow( + instance: ConvNextForImageClassification, + path: String, + spark: SparkSession): Unit = { + + val tf = readTensorflowModel(path, spark, "_image_classification_tf") + + val preprocessor = Preprocessor( + do_normalize = instance.getDoNormalize, + do_resize = instance.getDoRescale, + feature_extractor_type = "ConvNextFeatureExtractor", + image_mean = instance.getImageMean, + image_std = instance.getImageStd, + resample = instance.getResample, + do_rescale = instance.getDoRescale, + rescale_factor = instance.getRescaleFactor, + size = instance.getSize, + crop_pct = Option(instance.getCropPct)) + + instance.setModelIfNotSet(spark, tf, preprocessor) + + } + + addReader(readTensorflow) + + def loadSavedModel(modelPath: String, spark: SparkSession): ConvNextForImageClassification = { + + val (localModelPath, detectedEngine) = modelSanityCheck(modelPath) + + // TODO: sometimes results in [String, BigInt] where BigInt is actually a string + val labelJsonContent = loadJsonStringAsset(localModelPath, "labels.json") + val labelJsonMap = + parse(labelJsonContent, useBigIntForLong = true).values + .asInstanceOf[Map[String, BigInt]] + + val preprocessorConfigJsonContent = + loadJsonStringAsset(localModelPath, "preprocessor_config.json") + val preprocessorConfig = + Preprocessor.loadPreprocessorConfig(preprocessorConfigJsonContent) + + require( + preprocessorConfig.size >= 384 || preprocessorConfig.crop_pct.nonEmpty, + "Property \'crop_pct\' should be defined, if size < 384.") + val cropPct = preprocessorConfig.crop_pct.get + + val annotatorModel = new ConvNextForImageClassification() + .setLabels(labelJsonMap) + .setDoNormalize(preprocessorConfig.do_normalize) + .setDoResize(preprocessorConfig.do_resize) + .setFeatureExtractorType(preprocessorConfig.feature_extractor_type) + .setImageMean(preprocessorConfig.image_mean) + .setImageStd(preprocessorConfig.image_std) + .setResample(preprocessorConfig.resample) + .setSize(preprocessorConfig.size) + .setDoRescale(preprocessorConfig.do_rescale) + .setRescaleFactor(preprocessorConfig.rescale_factor) + .setCropPct(cropPct) + + annotatorModel.set(annotatorModel.engine, detectedEngine) + + detectedEngine match { + case ModelEngine.tensorflow => + val (wrapper, signatures) = + TensorflowWrapper.read(localModelPath, zipped = false, useBundle = true) + + val _signatures = signatures match { + case Some(s) => s + case None => throw new Exception("Cannot load signature definitions from model!") + } + + /** the order of setSignatures is important if we use getSignatures inside + * setModelIfNotSet + */ + annotatorModel + .setSignatures(_signatures) + .setModelIfNotSet(spark, wrapper, preprocessorConfig) + case _ => + throw new Exception(notSupportedEngineError) + } + + annotatorModel + } +} + +/** This is the companion object of [[ConvNextForImageClassification]]. Please refer to that class + * for the documentation. + */ +object ConvNextForImageClassification + extends ReadablePretrainedConvNextForImageModel + with ReadConvNextForImageDLModel diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala index 44fb3027c10d..21ce7a0d175f 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala @@ -169,13 +169,13 @@ class SwinForImageClassification(override val uid: String) def setDoRescale(value: Boolean): this.type = set(this.doRescale, value) /** @group getParam */ - private def getDoRescale: Boolean = $(doRescale) + def getDoRescale: Boolean = $(doRescale) /** @group setParam */ def setRescaleFactor(value: Double): this.type = set(this.rescaleFactor, value) /** @group getParam */ - private def getRescaleFactor: Double = $(rescaleFactor) + def getRescaleFactor: Double = $(rescaleFactor) setDefault( batchSize -> 2, @@ -212,15 +212,15 @@ class SwinForImageClassification(override val uid: String) images = noneEmptyImages, batchSize = $(batchSize), preprocessor = Preprocessor( - getDoResize, - getDoNormalize, - getFeatureExtractorType, - getImageMean, - getImageStd, - getResample, - getSize, - getDoRescale, - getRescaleFactor)) + do_normalize = getDoNormalize, + do_resize = getDoResize, + feature_extractor_type = getFeatureExtractorType, + image_mean = getImageMean, + image_std = getImageStd, + resample = getResample, + size = getSize, + do_rescale = getDoRescale, + rescale_factor = getRescaleFactor)) } else { Seq.empty[Annotation] } @@ -244,7 +244,6 @@ class SwinForImageClassification(override val uid: String) } override def onWrite(path: String, spark: SparkSession): Unit = { - super.onWrite(path, spark) writeTensorflowModelV2( path, spark, @@ -287,13 +286,19 @@ trait ReadSwinForImageDLModel extends ReadTensorflowModel { spark: SparkSession): Unit = { val tf = readTensorflowModel(path, spark, "_image_classification_tf") - instance.setModelIfNotSet( - spark, - tf, - instance.getImageMean, - instance.getImageStd, - instance.getResample, - instance.getSize) + + val preprocessor = Preprocessor( + do_normalize = instance.getDoNormalize, + do_resize = instance.getDoRescale, + feature_extractor_type = "SwinFeatureExtractor", + image_mean = instance.getImageMean, + image_std = instance.getImageStd, + resample = instance.getResample, + do_rescale = instance.getDoRescale, + rescale_factor = instance.getRescaleFactor, + size = instance.getSize) + + instance.setModelIfNotSet(spark, tf, preprocessor) } addReader(readTensorflow) @@ -343,13 +348,7 @@ trait ReadSwinForImageDLModel extends ReadTensorflowModel { */ annotatorModel .setSignatures(_signatures) - .setModelIfNotSet( - spark, - wrapper, - preprocessorConfig.image_mean, - preprocessorConfig.image_std, - preprocessorConfig.resample, - preprocessorConfig.size) + .setModelIfNotSet(spark, wrapper, preprocessorConfig) case _ => throw new Exception(notSupportedEngineError) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala index d4f80e85c688..768cda90ae54 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala @@ -230,10 +230,7 @@ class ViTForImageClassification(override val uid: String) def setModelIfNotSet( spark: SparkSession, tensorflow: TensorflowWrapper, - imageMean: Array[Double], - imageStd: Array[Double], - resample: Int, - size: Int): this.type = { + preprocessor: Preprocessor): this.type = { if (_model.isEmpty) { _model = Some( @@ -242,10 +239,7 @@ class ViTForImageClassification(override val uid: String) tensorflow, configProtoBytes = getConfigProtoBytes, tags = $$(labels), - imageMean = imageMean, - imageStd = imageStd, - resample = resample, - size = size, + preprocessor = preprocessor, signatures = getSignatures))) } this @@ -277,13 +271,13 @@ class ViTForImageClassification(override val uid: String) images = noneEmptyImages, batchSize = $(batchSize), preprocessor = Preprocessor( - getDoResize, - getDoNormalize, - getFeatureExtractorType, - getImageMean, - getImageStd, - getResample, - getSize)) + do_normalize = getDoNormalize, + do_resize = getDoResize, + feature_extractor_type = getFeatureExtractorType, + image_mean = getImageMean, + image_std = getImageStd, + resample = getResample, + size = getSize)) } else { Seq.empty[Annotation] } @@ -346,13 +340,17 @@ trait ReadViTForImageDLModel extends ReadTensorflowModel { def readModel(instance: ViTForImageClassification, path: String, spark: SparkSession): Unit = { val tf = readTensorflowModel(path, spark, "_image_classification_tf", initAllTables = false) - instance.setModelIfNotSet( - spark, - tf, + + val preprocessor = Preprocessor( + do_normalize = true, + do_resize = true, + "ViTFeatureExtractor", instance.getImageMean, instance.getImageStd, instance.getResample, instance.getSize) + + instance.setModelIfNotSet(spark, tf, preprocessor) } addReader(readModel) @@ -400,13 +398,7 @@ trait ReadViTForImageDLModel extends ReadTensorflowModel { */ annotatorModel .setSignatures(_signatures) - .setModelIfNotSet( - spark, - wrapper, - preprocessorConfig.image_mean, - preprocessorConfig.image_std, - preprocessorConfig.resample, - preprocessorConfig.size) + .setModelIfNotSet(spark, wrapper, preprocessorConfig) case _ => throw new Exception(notSupportedEngineError) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/Preprocessor.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/Preprocessor.scala index 21d0ee569609..f043f8450d1e 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/Preprocessor.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/Preprocessor.scala @@ -17,9 +17,35 @@ package com.johnsnowlabs.nlp.annotators.cv.feature_extractor import com.johnsnowlabs.util.JsonParser -import org.json4s.jackson.JsonMethods import org.json4s.{JNothing, JValue} +/** Case class represting an image pre-processor. Instances should be initialized with + * loadPreprocessorConfig. + * + * @param do_normalize + * Whether to normalize the image by subtracting `mean` and dividing by `std` + * @param do_resize + * Whether to resize the image to `size` + * @param feature_extractor_type + * Name of the feature extractor + * @param image_mean + * Array of means, one value for each color channel + * @param image_std + * Array of standard deviations, one value for each color channel + * @param resample + * Integer representing an image filter to be used for resizing/resampling. Corresponds to + * constants defined in PIL (either PIL.Image.NEAREST, PIL.Image.BILINEAR or PIL.Image.BICUBIC + * supported). + * @param size + * Size of the image after processing + * @param do_rescale + * Whether to rescale color values to rescale_factor + * @param rescale_factor + * Factor to rescale color values by + * @param crop_pct + * Percentage to crop the image. If set, first scales the image, then crops it to arrive at + * `size` + */ private[johnsnowlabs] case class Preprocessor( do_normalize: Boolean = true, do_resize: Boolean = true, @@ -29,7 +55,8 @@ private[johnsnowlabs] case class Preprocessor( resample: Int, size: Int, do_rescale: Boolean = true, - rescale_factor: Double = 1 / 255.0d) + rescale_factor: Double = 1 / 255.0d, + crop_pct: Option[Double] = None) private[johnsnowlabs] case class PreprocessorConfig( do_normalize: Boolean, @@ -41,7 +68,8 @@ private[johnsnowlabs] case class PreprocessorConfig( resample: Int, size: Any, do_rescale: Option[Boolean], - rescale_factor: Option[Double]) + rescale_factor: Option[Double], + crop_pct: Option[Double]) private[johnsnowlabs] object Preprocessor { def apply( @@ -70,6 +98,13 @@ private[johnsnowlabs] object Preprocessor { } } + /** Loads in initializes a Preprocessor from a json string. + * + * @param preprocessorConfigJsonContent + * Json contents in a String + * @return + * Loaded Preprocessor + */ def loadPreprocessorConfig(preprocessorConfigJsonContent: String): Preprocessor = { val preprocessorJsonErrorMsg = @@ -83,53 +118,71 @@ private[johnsnowlabs] object Preprocessor { | "resample": int, | "size": int, | ["do_rescale": bool], - | ["rescale_factor": double] + | ["rescale_factor": double], + | ["crop_pct": double] |} |""".stripMargin + def parseSize(config: PreprocessorConfig) = { + config.size match { + case sizeMap: Map[String, BigInt] if sizeMap.contains("width") => + val width = sizeMap("width") + require( + width == sizeMap("height"), + "Different sizes for width and height are currently not supported.") + width.toInt + case sizeMap: Map[String, BigInt] if sizeMap.contains("shortest_edge") => + // ConvNext case: Size of the output image after `resize` has been applied + sizeMap("shortest_edge").toInt + case sizeInt: BigInt => sizeInt.toInt + case _ => + throw new IllegalArgumentException( + "Unsupported format for size. Should either be int or dict with entries \'width\' and \'height\' or \'shortest_edge\'") + } + } + + def parseExtractorType(config: PreprocessorConfig) = { + config.feature_extractor_type.getOrElse({ + config.image_processor_type + .getOrElse(throw new IllegalArgumentException( + "Either \'feature_extractor_type\' or \'image_processor_type\' should be set.")) + .replace("ImageProcessor", "FeatureExtractor") + }) + } + + def parseRescaleFactor(config: PreprocessorConfig) = { + if (config.do_rescale.isDefined) config.rescale_factor.getOrElse { + throw new IllegalArgumentException( + "Value do_rescale is true but no rescale_factor found in config.") + } + else 1 / 255.0d + } + val preprocessorConfig = try { val config = JsonParser.parseObject[PreprocessorConfig](preprocessorConfigJsonContent) // json4s parses BigInt by default - val size: Int = config.size match { - case sizeMap: Map[String, BigInt] => - val width = sizeMap("width") - require( - width == sizeMap("height"), - "Different sizes for width and height are currently not supported.") - width.toInt - case sizeInt: BigInt => sizeInt.toInt - case _ => - throw new IllegalArgumentException( - "Unsupported format for size. Should either be int or dict with entries \'width\' and \'height\'") - } - - val extractorType = config.feature_extractor_type.getOrElse({ - config.image_processor_type - .getOrElse(throw new IllegalArgumentException( - "Either \'feature_extractor_type\' or \'image_processor_type\' should be set.")) - .replace("ImageProcessor", "FeatureExtractor") - }) - - val doRescale = config.do_rescale.getOrElse(false) - - val rescaleFactor: Double = if (doRescale) config.rescale_factor.getOrElse { - throw new IllegalArgumentException( - "Value do_rescale is true but no rescale_factor found in config.") - } - else 1 / 255.0d // Default value + val size: Int = parseSize(config) + + val extractorType = parseExtractorType(config) + + val rescaleFactor: Double = + parseRescaleFactor(config) // Default value + + val doRescale = config.do_rescale.getOrElse(true) Preprocessor( do_normalize = config.do_normalize, do_resize = config.do_resize, - extractorType, - config.image_mean, - config.image_std, - config.resample, - size, - doRescale, - rescaleFactor) + feature_extractor_type = extractorType, + image_mean = config.image_mean, + image_std = config.image_std, + resample = config.resample, + size = size, + do_rescale = doRescale, + rescale_factor = rescaleFactor, + crop_pct = config.crop_pct) } catch { case e: Exception => println(s"$preprocessorJsonErrorMsg \n error: ${e.getMessage}") diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/util/transform/ImageResizeUtils.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/util/transform/ImageResizeUtils.scala index d2661c2d01a2..c393fc30e010 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/util/transform/ImageResizeUtils.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/util/transform/ImageResizeUtils.scala @@ -16,36 +16,40 @@ package com.johnsnowlabs.nlp.annotators.cv.util.transform -import com.johnsnowlabs.nlp.annotators.cv.util.io.ImageIOUtils - +import java.awt.Color import java.awt.geom.AffineTransform import java.awt.image.{AffineTransformOp, BufferedImage} -import java.awt.{Color, Image} import scala.collection.mutable.ArrayBuffer private[johnsnowlabs] object ImageResizeUtils { - def resizeBufferedImage(width: Int, height: Int, channels: Option[Int])( + /** Resized a BufferedImage with specified width, height and filter. + * + * @param width + * Target width of the image + * @param height + * Target height of the image + * @param resample + * Transformation/Filter to apply, either `AffineTransformOp.TYPE_NEAREST_NEIGHBOR`(1), + * `AffineTransformOp.TYPE_BILINEAR`(2), `AffineTransformOp.TYPE_BICUBIC`(3) + * @param image + * Image to resize + * @return + * Resized BufferedImage + */ + def resizeBufferedImage(width: Int, height: Int, resample: Int)( image: BufferedImage): BufferedImage = { - val imgType = channels.map(ImageIOUtils.convertChannelsToType).getOrElse(image.getType) - if (image.getWidth == width && - image.getHeight == height && - image.getType == imgType) { - return image - } + val scaleX = width / image.getWidth.toDouble + val scaleY = height / image.getHeight.toDouble + + val transform = AffineTransform.getScaleInstance(scaleX, scaleY) + val transformOp = new AffineTransformOp(transform, resample) + val result = transformOp.filter(image, null) // Creates new BufferedImage - // SCALE_AREA_AVERAGING performs slower than SCALE_DEFAULT - val resizedImage = image.getScaledInstance(width, height, Image.SCALE_DEFAULT) - val bufferedImage = new BufferedImage(width, height, imgType) - val graphic = bufferedImage.createGraphics() - // scalastyle:ignore null - graphic.drawImage(resizedImage, 0, 0, null) - graphic.dispose() - bufferedImage + result } - // TODO implement doNormalize = false to only return Array[Array[Array[Float]]] without normalizing /** @param img * The image in BufferedImage * @param mean @@ -56,11 +60,14 @@ private[johnsnowlabs] object ImageResizeUtils { * Factor to rescale the image values by * @return */ - def normalizeBufferedImage( + def normalizeAndConvertBufferedImage( img: BufferedImage, mean: Array[Double], std: Array[Double], - rescaleFactor: Double = 1 / 255.0d): Array[Array[Array[Float]]] = { + doNormalize: Boolean, + doRescale: Boolean, + rescaleFactor: Double): Array[Array[Array[Float]]] = { + val data = Array(ArrayBuffer[Array[Float]](), ArrayBuffer[Array[Float]](), ArrayBuffer[Array[Float]]()) for (y <- 0 until img.getHeight) { @@ -73,10 +80,18 @@ private[johnsnowlabs] object ImageResizeUtils { // Creating a Color object from pixel value val color = new Color(pixel, true) - // Retrieving the R G B values and Normalizing them - val red = ((color.getRed * rescaleFactor) - mean.head) / std.head - val green = ((color.getGreen * rescaleFactor) - mean(1)) / std(1) - val blue = ((color.getBlue * rescaleFactor) - mean(2)) / std(2) + // Retrieving the R G B values, rescaling and normalizing them + val rescaledRed = if (doRescale) color.getRed * rescaleFactor else color.getRed + val rescaledGreen = if (doRescale) color.getGreen * rescaleFactor else color.getGreen + val rescaledBlue = if (doRescale) color.getBlue * rescaleFactor else color.getBlue + + val (red, green, blue) = if (doNormalize) { + val normR = (rescaledRed - mean.head) / std.head + val normG = (rescaledGreen - mean(1)) / std(1) + val normB = (rescaledBlue - mean(2)) / std(2) + (normR, normG, normB) + } else (rescaledRed, rescaledGreen, rescaledBlue) + RedList += red.toFloat GreenList += green.toFloat BlueList += blue.toFloat @@ -125,4 +140,38 @@ private[johnsnowlabs] object ImageResizeUtils { height: Int): BufferedImage = { bufferedImage.getSubimage(x, y, width, height) } + + /** Resizes and crops an image, intended for smaller images. + * + * The image is resized based on a percentage. The smaller edge will be resized to + * `requestedSize / cropPct` and then cropped to `(requestedSize, requestedSize)`. + */ + def resizeAndCenterCropImage( + img: BufferedImage, + requestedSize: Int, + resample: Int, + cropPct: Double): BufferedImage = { + + val width = img.getWidth() + val height = img.getHeight() + + val (shortEdge, longEdge) = if (width <= height) (width, height) else (height, width) + + val sizeForCrop = requestedSize / cropPct + val newShortEdge = sizeForCrop.toInt + val newLongEdge = (sizeForCrop * (longEdge / shortEdge.toFloat)).toInt + + val (resizeWidth, resizeHeight) = + if (width <= height) (newShortEdge, newLongEdge) else (newLongEdge, newShortEdge) + + // Resize the Image with the new calculated size + val resizedImage = + resizeBufferedImage(resizeWidth, resizeHeight, resample)(img) + + // Crop at the center of the image + val cropLeft = (resizeWidth - requestedSize).abs / 2 + val cropTop = (resizeHeight - requestedSize).abs / 2 + + cropBufferedImage(resizedImage, cropLeft, cropTop, requestedSize, requestedSize) + } } diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala index 0a3a22bfb4b0..87a326209d92 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala @@ -21,7 +21,11 @@ import com.johnsnowlabs.nlp.annotators._ import com.johnsnowlabs.nlp.annotators.audio.{HubertForCTC, Wav2Vec2ForCTC} import com.johnsnowlabs.nlp.annotators.classifier.dl._ import com.johnsnowlabs.nlp.annotators.coref.SpanBertCorefModel -import com.johnsnowlabs.nlp.annotators.cv.{SwinForImageClassification, ViTForImageClassification} +import com.johnsnowlabs.nlp.annotators.cv.{ + ConvNextForImageClassification, + SwinForImageClassification, + ViTForImageClassification +} import com.johnsnowlabs.nlp.annotators.er.EntityRulerModel import com.johnsnowlabs.nlp.annotators.ld.dl.LanguageDetectorDL import com.johnsnowlabs.nlp.annotators.ner.crf.NerCrfModel @@ -708,6 +712,7 @@ object PythonResourceDownloader { "SpanBertCorefModel" -> SpanBertCorefModel, "ViTForImageClassification" -> ViTForImageClassification, "SwinForImageClassification" -> SwinForImageClassification, + "ConvNextForImageClassification" -> ConvNextForImageClassification, "Wav2Vec2ForCTC" -> Wav2Vec2ForCTC, "HubertForCTC" -> HubertForCTC, "CamemBertForTokenClassification" -> CamemBertForTokenClassification, diff --git a/src/test/resources/image_preprocessor/normalized_egyptian_cat.json b/src/test/resources/image_preprocessor/normalized_egyptian_cat.json deleted file mode 100644 index 68c506c6cdbd..000000000000 --- a/src/test/resources/image_preprocessor/normalized_egyptian_cat.json +++ /dev/null @@ -1,2 +0,0 @@ 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zt1%NJ&==V792p4%R?=w!@S!V1nm4QEo#cLwq2P%2I+WCpAV|}WDZEz6vHJ~G271re z*Eh!dxW1 zs;WX(V>+ifX6eFJq;WzOw?09tM9+i?hKcv@LNQT%*3TBw0Dcz;@b@CAH%sIGElo|m zva2}ec_taL?=#F>a^x(TY zGKHWDnsKH2`nJb27KtK*+mMQ{Ne+8=T@v4aXAy%c0(+>mfI9pn$v4)y4XwGpSScxg z6}S}NzN)r;JI1sfEqRg6_Up9L8=LB+yx=f3bt(t1P^CeH>A?19DB3@$u&i*Stw$10 zkZ;{qRh7}om6EDDgwNa+OXS%EDl+LjDm%Wqd(2rGoiP=|rSiAn{hIqOkuZLnlH%!8 m9c`AC2A@}jRjWHl8g39N{JWE$o}P}OuHjD-8{fyenEwUqaWs(t literal 0 HcmV?d00001 diff --git a/src/test/resources/image_preprocessor/preprocessor_config_convnext.json b/src/test/resources/image_preprocessor/preprocessor_config_convnext.json new file mode 100644 index 000000000000..6b3ef5e2d5a4 --- /dev/null +++ b/src/test/resources/image_preprocessor/preprocessor_config_convnext.json @@ -0,0 +1,23 @@ +{ + "crop_pct": 0.875, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "feature_extractor_type": "ConvNextFeatureExtractor", + "image_mean": [ + 0.485, + 0.456, + 0.406 + ], + "image_processor_type": "ConvNextFeatureExtractor", + "image_std": [ + 0.229, + 0.224, + 0.225 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 224 + } +} diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassificationTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassificationTestSpec.scala new file mode 100644 index 000000000000..50c4bec7ab81 --- /dev/null +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassificationTestSpec.scala @@ -0,0 +1,31 @@ +package com.johnsnowlabs.nlp.annotators.cv + +import org.scalatest.flatspec.AnyFlatSpec + +class ConvNextForImageClassificationTestSpec + extends AnyFlatSpec + with ViTForImageClassificationBehaviors { + + behavior of "ConvNextForImageClassification" + + lazy val goldStandards: Map[String, String] = + Map( + "bluetick.jpg" -> "bluetick", + "chihuahua.jpg" -> "Chihuahua", + "egyptian_cat.jpeg" -> "tabby, tabby cat", + "hen.JPEG" -> "hen", + "hippopotamus.JPEG" -> "hippopotamus, hippo, river horse, Hippopotamus amphibius", + "junco.JPEG" -> "junco, snowbird", + "ostrich.JPEG" -> "ostrich, Struthio camelus", + "ox.JPEG" -> "ox", + "palace.JPEG" -> "palace", + "tractor.JPEG" -> "thresher, thrasher, threshing machine") + + private lazy val model: ConvNextForImageClassification = + ConvNextForImageClassification.pretrained() + it should behave like + behaviorsViTForImageClassification[ConvNextForImageClassification]( + ConvNextForImageClassification.load, + model, + goldStandards) +} diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassificationTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassificationTest.scala index 3b98e522e9a5..d837356346e4 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassificationTest.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassificationTest.scala @@ -1,13 +1,12 @@ package com.johnsnowlabs.nlp.annotators.cv -import com.johnsnowlabs.nlp.util.io.ResourceHelper import org.scalatest.flatspec.AnyFlatSpec class SwinForImageClassificationTest extends AnyFlatSpec with ViTForImageClassificationBehaviors { behavior of "SwinForImageClassificationTest" - val goldStandards: Map[String, String] = + lazy val goldStandards: Map[String, String] = Map( "hen.JPEG" -> "hen", "chihuahua.jpg" -> "Chihuahua", @@ -20,8 +19,10 @@ class SwinForImageClassificationTest extends AnyFlatSpec with ViTForImageClassif "bluetick.jpg" -> "bluetick", "palace.JPEG" -> "palace") + private lazy val model: SwinForImageClassification = SwinForImageClassification.pretrained() it should behave like behaviorsViTForImageClassification[SwinForImageClassification]( - SwinForImageClassification.pretrained(), + SwinForImageClassification.load, + model, goldStandards) } diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala index 94cd69904031..da5441f820c5 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala @@ -38,8 +38,9 @@ trait ViTForImageClassificationBehaviors { this: AnyFlatSpec => .setOutputCol("image_assembler") def behaviorsViTForImageClassification[M <: ViTForImageClassification]( - vitClassifier: => ViTForImageClassification, - expectedPredictions: => Map[String, String]): Unit = { + loadModelFunction: String => M, + vitClassifier: ViTForImageClassification, + expectedPredictions: Map[String, String]): Unit = { def setUpImageClassifierPipeline(): Pipeline = { val imageClassifier: ViTForImageClassification = vitClassifier @@ -55,20 +56,7 @@ trait ViTForImageClassificationBehaviors { this: AnyFlatSpec => val pipeline = setUpImageClassifierPipeline() val pipelineDF = pipeline.fit(imageDF).transform(imageDF) - val predictedResults = pipelineDF - .select("class.result", "image.origin") - .rdd - .flatMap(row => - Map( - row.getAs[mutable.WrappedArray[String]](0)(0) -> - row.getString(1).split("/").last)) - .collect() - - predictedResults.foreach { x => - val goldValue = expectedPredictions(x._2) - val predictValue = x._1 - assert(goldValue === predictValue) - } + assertPredictions(pipelineDF, expectedPredictions) } @@ -79,28 +67,33 @@ trait ViTForImageClassificationBehaviors { this: AnyFlatSpec => val pipelineDF = pipelineModel.transform(imageDF) pipelineDF.take(1) + val classifierClass = vitClassifier.getClass.toString.split("\\.").last + val tmpSavedFolder = s"./tmp_$classifierClass" pipelineModel.stages.last .asInstanceOf[M] .write .overwrite() - .save("./tmp_ViTModel") + .save(tmpSavedFolder) // load the saved ViTForImageClassification model - val loadedViTModel = ViTForImageClassification.load("./tmp_ViTModel") + val loadedViTModel = loadModelFunction(tmpSavedFolder) val loadedPipeline = new Pipeline().setStages(Array(imageAssembler, loadedViTModel)) val loadedPipelineModel = loadedPipeline.fit(imageDF) val loadedPipelineModelDF = loadedPipelineModel.transform(imageDF) + assertPredictions(loadedPipelineModelDF, expectedPredictions) + loadedPipelineModelDF .select("class.result", "image_assembler.origin") .show(3, truncate = 120) // save the whole pipeline - loadedPipelineModelDF.write.mode("overwrite").parquet("./tmp_vit_pipeline") + val tmpPipelinePath = tmpSavedFolder + "_pipeline" + loadedPipelineModelDF.write.mode("overwrite").parquet(tmpPipelinePath) // load the whole pipeline - val loadedProcessedPipelineDF = ResourceHelper.spark.read.parquet("./tmp_vit_pipeline") + val loadedProcessedPipelineDF = ResourceHelper.spark.read.parquet(tmpPipelinePath) loadedProcessedPipelineDF .select("class.result", "image_assembler.origin") .show(3, truncate = 120) @@ -117,8 +110,7 @@ trait ViTForImageClassificationBehaviors { this: AnyFlatSpec => it should "benchmark" taggedAs SlowTest in { - val imageClassifier: ViTForImageClassification = ViTForImageClassification - .pretrained() + val imageClassifier: ViTForImageClassification = vitClassifier .setInputCols("image_assembler") .setOutputCol("class") @@ -202,13 +194,32 @@ trait ViTForImageClassificationBehaviors { this: AnyFlatSpec => } } + + private def assertPredictions[M <: ViTForImageClassification]( + pipelineDF: DataFrame, + expectedPredictions: Map[String, String]): Unit = { + val predictedResults = pipelineDF + .select("class.result", "image.origin") + .rdd + .flatMap(row => + Map( + row.getAs[mutable.WrappedArray[String]](0)(0) -> + row.getString(1).split("/").last)) + .collect() + + predictedResults.foreach { x => + val goldValue = expectedPredictions(x._2) + val predictValue = x._1 + assert(goldValue === predictValue) + } + } } class ViTImageClassificationTestSpec extends AnyFlatSpec with ViTForImageClassificationBehaviors { behavior of "ViTForImageClassification" - val goldStandards: Map[String, String] = + lazy val goldStandards: Map[String, String] = Map( "palace.JPEG" -> "palace", "egyptian_cat.jpeg" -> "Egyptian cat", @@ -221,8 +232,11 @@ class ViTImageClassificationTestSpec extends AnyFlatSpec with ViTForImageClassif "tractor.JPEG" -> "tractor", "ox.JPEG" -> "ox") + private lazy val model: ViTForImageClassification = ViTForImageClassification.pretrained() + it should behave like behaviorsViTForImageClassification[ViTForImageClassification]( - ViTForImageClassification.pretrained(), + ViTForImageClassification.load, + model, goldStandards) } diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/ImageUtilsTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/ImageUtilsTestSpec.scala index 0da501649078..6609817309c7 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/ImageUtilsTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/feature_extractor/ImageUtilsTestSpec.scala @@ -18,30 +18,26 @@ package com.johnsnowlabs.nlp.annotators.cv.feature_extractor import com.johnsnowlabs.nlp.annotators.cv.util.io.ImageIOUtils import com.johnsnowlabs.nlp.annotators.cv.util.transform.ImageResizeUtils -import com.johnsnowlabs.nlp.util.io.ResourceHelper import com.johnsnowlabs.tags.FastTest +import com.johnsnowlabs.util.TestUtils.{assertPixels, readFile} import com.johnsnowlabs.util.{Benchmark, JsonParser} import org.apache.spark.ml.image.ImageSchema import org.json4s.JsonAST.{JInt, JObject, JString} import org.json4s.jackson.JsonMethods._ import org.scalatest.flatspec.AnyFlatSpec +import java.awt.Color import java.awt.color.ColorSpace import java.awt.image.BufferedImage import java.io.File -import scala.io.Source +import javax.imageio.ImageIO class ImageUtilsTestSpec extends AnyFlatSpec { - def readJson(path: String): String = { - val stream = ResourceHelper.getResourceStream(new File(path).getAbsolutePath) - Source.fromInputStream(stream).mkString - } - val preprocessorConfigPath = "src/test/resources/image_preprocessor/preprocessor_config.json" - val preprocessorConfigJsonContent: String = readJson(preprocessorConfigPath) + val preprocessorConfigJsonContent: String = readFile(preprocessorConfigPath) val preprocessorConfig: Preprocessor = JsonParser.parseObject[Preprocessor](preprocessorConfigJsonContent) @@ -64,42 +60,48 @@ class ImageUtilsTestSpec extends AnyFlatSpec { ImageResizeUtils.resizeBufferedImage( width = preprocessorConfig.size, height = preprocessorConfig.size, - Some(nChannels))(imageBufferedImage) + resample = preprocessorConfig.resample)(imageBufferedImage) "ImageResizeUtils" should "resize and normalize an image" taggedAs FastTest in { - Benchmark.measure(iterations = 10, forcePrint = true, description = "Time to load image") { + Benchmark.measure(iterations = 1, forcePrint = true, description = "Time to load image") { ImageIOUtils.loadImage("src/test/resources/image/egyptian_cat.jpeg") } Benchmark.measure( - iterations = 10, + iterations = 1000, forcePrint = true, description = "Time to resizeBufferedImage an image") { ImageResizeUtils.resizeBufferedImage( width = preprocessorConfig.size, height = preprocessorConfig.size, - Some(nChannels))(imageBufferedImage) + resample = preprocessorConfig.resample)(imageBufferedImage) } Benchmark.measure( - iterations = 10, + iterations = 1, forcePrint = true, description = "Time to normalize the resized image") { - ImageResizeUtils.normalizeBufferedImage( + ImageResizeUtils.normalizeAndConvertBufferedImage( resizedImage, preprocessorConfig.image_mean, - preprocessorConfig.image_std) + preprocessorConfig.image_std, + preprocessorConfig.do_normalize, + preprocessorConfig.do_rescale, + preprocessorConfig.rescale_factor) } Benchmark.measure( - iterations = 10, + iterations = 1, forcePrint = true, description = "Time to normalize with 0.0d") { - ImageResizeUtils.normalizeBufferedImage( + ImageResizeUtils.normalizeAndConvertBufferedImage( resizedImage, Array(0.0d, 0.0d, 0.0d), - Array(0.0d, 0.0d, 0.0d)) + Array(0.0d, 0.0d, 0.0d), + preprocessorConfig.do_normalize, + preprocessorConfig.do_rescale, + preprocessorConfig.rescale_factor) } } @@ -112,21 +114,6 @@ class ImageUtilsTestSpec extends AnyFlatSpec { assert(preprocessorConfig.image_mean sameElements Array(0.5d, 0.5d, 0.5d)) } - "ImageResizeUtils" should "read swin preprocessor_config.json file" taggedAs FastTest in { - val jsonPath = "src/test/resources/image_preprocessor/preprocessor_config_swin.json" - val preprocessorConfig = - Preprocessor.loadPreprocessorConfig(readJson(jsonPath)) - - assert(preprocessorConfig.do_normalize) - assert(preprocessorConfig.do_rescale) - assert(preprocessorConfig.do_resize) - assert(preprocessorConfig.feature_extractor_type == "ViTFeatureExtractor") - assert(preprocessorConfig.image_mean sameElements Array(0.485d, 0.456d, 0.406d)) - assert(preprocessorConfig.image_std sameElements Array(0.229d, 0.224d, 0.225d)) - assert(preprocessorConfig.resample == 3) - assert(preprocessorConfig.rescale_factor == 0.00392156862745098) - } - // Some models don't have feature_extractor_type in the config json "ImageResizeUtils" should "rename image processor if feature_extractor_type not available" taggedAs FastTest in { val json = parse(preprocessorConfigJsonContent) transformField { @@ -167,7 +154,7 @@ class ImageUtilsTestSpec extends AnyFlatSpec { } val jsonPath = "src/test/resources/image_preprocessor/preprocessor_config_swin.json" - val swinContent = readJson(jsonPath) + val swinContent = readFile(jsonPath) // height and width not identical val swinJson = parse(swinContent) @@ -187,30 +174,76 @@ class ImageUtilsTestSpec extends AnyFlatSpec { "ImageResizeUtils" should "normalize an image correctly with custom rescale_factor" taggedAs FastTest in { val jsonPath = "src/test/resources/image_preprocessor/preprocessor_config_swin.json" val preprocessorConfig = - Preprocessor.loadPreprocessorConfig(readJson(jsonPath)) + Preprocessor.loadPreprocessorConfig(readFile(jsonPath)) val normalized = ImageResizeUtils - .normalizeBufferedImage( + .normalizeAndConvertBufferedImage( resizedImage, preprocessorConfig.image_mean, preprocessorConfig.image_std, + preprocessorConfig.do_normalize, + preprocessorConfig.do_rescale, preprocessorConfig.rescale_factor) - val expectedValues = - JsonParser.parseArray[Array[Array[Float]]]( - readJson("src/test/resources/image_preprocessor/normalized_egyptian_cat.json")) + def normalize(color: Int, mean: Double, std: Double): Float = { + (((color * preprocessorConfig.rescale_factor) - mean) / std).toFloat + } + + (0 until resizedImage.getWidth).zip(0 until resizedImage.getHeight).map { case (x, y) => + val originalColor = new Color(resizedImage.getRGB(x, y)) + val red = normalized(0) + + assert( + normalize( + originalColor.getRed, + preprocessorConfig.image_mean(0), + preprocessorConfig.image_std(0)) == red(x)(y)) + } + + } + + "ImageResizeUtils" should "resize and crop image" taggedAs FastTest in { + val preprocessorJsonPath = + "src/test/resources/image_preprocessor/preprocessor_config_convnext.json" + + val preprocessor = + Preprocessor.loadPreprocessorConfig(readFile(preprocessorJsonPath)) - val channels = normalized.length - val width = normalized.head.length - val height = normalized.head.head.length + val img = ImageIOUtils.loadImage("src/test/resources/image/ox.JPEG").get - (0 until channels).foreach { channel => - (0 until width).foreach { w => - (0 until height).foreach { h => - assert(normalized(channel)(w)(h) == expectedValues(channel)(w)(h)) - } - } + val processedImage = + ImageResizeUtils.resizeAndCenterCropImage( + img, + preprocessor.size, + preprocessor.resample, + preprocessor.crop_pct.get) + + assert(processedImage.getWidth == 224) + assert(processedImage.getHeight == 224) + + // Use PNG so no compression is applied + val expectedCropped = + ImageIO.read(new File("src/test/resources/image_preprocessor/ox_cropped.png")) + + (0 until processedImage.getWidth).zip(0 until processedImage.getHeight).map { case (x, y) => + assert( + expectedCropped.getRGB(x, y) == processedImage.getRGB(x, y), + s"Pixel did not match for coordinates ($x, $y)") } + + // Case: Image is too small for size + val smallImg = + ImageIOUtils.loadImage("src/test/resources/image_preprocessor/ox_small.JPEG").get + + val processedSmallImage = + ImageResizeUtils.resizeAndCenterCropImage( + smallImg, + preprocessor.size, + preprocessor.resample, + preprocessor.crop_pct.get) + + assert(processedSmallImage.getWidth == 224) + assert(processedSmallImage.getHeight == 224) } } diff --git a/src/test/scala/com/johnsnowlabs/util/TestUtils.scala b/src/test/scala/com/johnsnowlabs/util/TestUtils.scala new file mode 100644 index 000000000000..d935896a8e71 --- /dev/null +++ b/src/test/scala/com/johnsnowlabs/util/TestUtils.scala @@ -0,0 +1,46 @@ +package com.johnsnowlabs.util + +import com.johnsnowlabs.nlp.util.io.ResourceHelper + +import scala.io.Source + +private[johnsnowlabs] object TestUtils { + + def readFile(path: String): String = { + val stream = ResourceHelper.getResourceStream(path) + Source.fromInputStream(stream).mkString + } + + def assertPixels( + values: Array[Array[Array[Float]]], + expectedValues: Array[Array[Array[Float]]], + error: Option[Double] = None): Unit = { + val channels = values.length + val width = values.head.length + val height = values.head.head.length + + assert(expectedValues.length == channels) + assert(expectedValues.head.length == width) + assert(expectedValues.head.head.length == height) + + (0 until channels).foreach { channel => + (0 until width).foreach { w => + (0 until height).foreach { h => + val pixelVal = values(channel)(w)(h) + val expectedPixelVal = expectedValues(channel)(w)(h) + error match { + case Some(err) => + assert( + (pixelVal - expectedPixelVal).abs < err, + s"Value does not match even with error: ($pixelVal, $expectedPixelVal) for $channel, $w, $h") + case None => + assert( + pixelVal == expectedPixelVal, + s"Value does not match: ($pixelVal, $expectedPixelVal) for $channel, $w, $h") + } + + } + } + } + } +} From b6b8cc66c4b5c0c7afc6f3d16782255432f7a0fe Mon Sep 17 00:00:00 2001 From: Danilo Burbano <37355249+danilojsl@users.noreply.github.com> Date: Thu, 6 Apr 2023 13:03:46 -0500 Subject: [PATCH 05/26] SPARKNLP-785 Fix WordEmbeddingsModel bug whit LightPipeline (#13715) --- .../nlp/embeddings/WordEmbeddingsModel.scala | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala index 23ec8ff9d215..f32c7dace570 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala @@ -188,7 +188,15 @@ class WordEmbeddingsModel(override val uid: String) private var memoryStorage: Option[Broadcast[Map[BytesKey, Array[Byte]]]] = None - def getInMemoryStorage: Map[BytesKey, Array[Byte]] = memoryStorage.get.value + private def getInMemoryStorage: Map[BytesKey, Array[Byte]] = { + memoryStorage.map(_.value).getOrElse { + if ($(enableInMemoryStorage)) { + getReader(Database.EMBEDDINGS).exportStorageToMap() + } else { + Map.empty + } + } + } override def beforeAnnotate(dataset: Dataset[_]): Dataset[_] = { if (this.memoryStorage.isEmpty && $(enableInMemoryStorage)) { From 51098594bde986b0da38fc70ee56a2470dc44592 Mon Sep 17 00:00:00 2001 From: Devin Ha <33089471+DevinTDHa@users.noreply.github.com> Date: Thu, 6 Apr 2023 20:04:08 +0200 Subject: [PATCH 06/26] [skip test] SPARKNLP-783: Python 3.6 deprecated in Spark 3.2 (#13724) --- docs/en/install.md | 6 +++++- python/docs/getting_started/index.rst | 6 ++++-- python/sparknlp/__init__.py | 5 +++++ 3 files changed, 14 insertions(+), 3 deletions(-) diff --git a/docs/en/install.md b/docs/en/install.md index 809ec03e09ef..629db45c8e73 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -35,7 +35,11 @@ spark-shell --jars spark-nlp-assembly-4.3.2.jar ## Python -Spark NLP supports Python 3.6.x and above depending on your major PySpark version. +Spark NLP supports Python 3.7.x and above depending on your major PySpark version. + +**NOTE**: Since Spark version 3.2, Python 3.6 is deprecated. If you are using this +python version, consider sticking to lower versions of Spark. + #### Quick Install Let's create a new Conda environment to manage all the dependencies there. You can use Python Virtual Environment if you prefer or not have any environment. diff --git a/python/docs/getting_started/index.rst b/python/docs/getting_started/index.rst index abdc264f78f7..65c6d38cf0c7 100644 --- a/python/docs/getting_started/index.rst +++ b/python/docs/getting_started/index.rst @@ -54,10 +54,12 @@ Requirements Spark NLP is built on top of Apache Spark `3.x`. For using Spark NLP you need: * Java 8 -* Apache Spark ``3.1.x`` (or ``3.0.x``, or ``2.4.x``, or ``2.3.x``) +* Apache Spark (from ``2.3.x`` to ``3.3.x``) * Python ``3.8.x`` if you are using PySpark ``3.x`` - * Python ``3.6.x`` and ``3.7.x`` if you are using PySpark ``2.3.x`` or ``2.4.x`` + * **NOTE**: Since Spark version 3.2, Python 3.6 is deprecated. If you are using this + python version, consider sticking to lower versions of Spark. + * For Python ``3.6.x`` and ``3.7.x`` we recommend PySpark ``2.3.x`` or ``2.4.x`` It is recommended to have basic knowledge of the framework and a working environment before using Spark NLP. Please refer to `Spark documentation `_ to get started with Spark. diff --git a/python/sparknlp/__init__.py b/python/sparknlp/__init__.py index 964c63fd5c2c..ede23d2fd859 100644 --- a/python/sparknlp/__init__.py +++ b/python/sparknlp/__init__.py @@ -117,6 +117,11 @@ def start(gpu=False, output_level : int, optional Output level for logs, by default 1 + Notes + ----- + Since Spark version 3.2, Python 3.6 is deprecated. If you are using this + python version, consider sticking to lower versions of Spark. + Returns ------- :class:`SparkSession` From 79d5976153884c72fa70d93ffbed80ca8a82018f Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Thu, 6 Apr 2023 20:04:28 +0200 Subject: [PATCH 07/26] SPARKNLP-763 Implementing ZeroShot Text Classification for BERT and DistilBERT based on NLI (#13727) * SPARKNLP-763 Fix a typo Signed-off-by: Maziyar Panahi * SPARKNLP-763 add unfinished traits Signed-off-by: Maziyar Panahi * SPARKNLP-763 Create a new BertForZeroShotClassification annotator Signed-off-by: Maziyar Panahi * SPARKNLP-763 Create a new HasCandidateLabelsProperties Signed-off-by: Maziyar Panahi * SPARKNLP-763 Implement predict sequence with NLI, new tokenize from strings, and new tag ZeroShot Signed-off-by: Maziyar Panahi * SPARKNLP-763 Clean up the code Signed-off-by: Maziyar Panahi * SPARKNLP-763 Add BertForZeroShotClassification to annotator [skip test] Signed-off-by: Maziyar Panahi * SPARKNLP-763 Add BertForZeroShotClassification to ResourceDownloader [skip test] Signed-off-by: Maziyar Panahi * SPARKNLP-763 Implement BertForZeroShotClassification in Python [skip test] Signed-off-by: Maziyar Panahi * SPARKNLP-763 Add unit tests for BertForZeroShotClassification Signed-off-by: Maziyar Panahi * change default model to bert_base_cased_zero_shot_classifier_xnli * SPARKNLP-763 Fix Scaladoc and Pydoc Signed-off-by: Maziyar Panahi * SPARKNLP-763 Fix Update unit test in Scala Signed-off-by: Maziyar Panahi --------- Signed-off-by: Maziyar Panahi --- .../annotator/classifier_dl/__init__.py | 1 + .../bert_for_zero_shot_classification.py | 221 +++++++++ python/sparknlp/common/properties.py | 44 ++ python/sparknlp/internal/__init__.py | 6 + .../bert_for_zero_shot_classification_test.py | 52 ++ .../ml/ai/AlbertClassification.scala | 11 + .../ml/ai/BertClassification.scala | 90 ++++ .../ml/ai/CamemBertClassification.scala | 11 + .../ml/ai/DeBertaClassification.scala | 11 + .../ml/ai/DistilBertClassification.scala | 11 + .../ml/ai/RoBertaClassification.scala | 11 + .../ml/ai/XXXForClassification.scala | 103 +++- .../ml/ai/XlmRoBertaClassification.scala | 11 + .../ml/ai/XlnetClassification.scala | 11 + .../nlp/HasCandidateLabelsProperties.scala | 49 ++ .../com/johnsnowlabs/nlp/annotator.scala | 7 + .../dl/BertForSequenceClassification.scala | 2 +- .../dl/BertForZeroShotClassification.scala | 454 ++++++++++++++++++ .../nlp/pretrained/ResourceDownloader.scala | 3 +- ...ertForZeroShotClassificationTestSpec.scala | 186 +++++++ 20 files changed, 1292 insertions(+), 3 deletions(-) create mode 100755 python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py create mode 100644 python/test/annotator/classifier_dl/bert_for_zero_shot_classification_test.py create mode 100644 src/main/scala/com/johnsnowlabs/nlp/HasCandidateLabelsProperties.scala create mode 100644 src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala create mode 100644 src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala diff --git a/python/sparknlp/annotator/classifier_dl/__init__.py b/python/sparknlp/annotator/classifier_dl/__init__.py index ff2cd6f90cbe..8ba241ed0ba4 100644 --- a/python/sparknlp/annotator/classifier_dl/__init__.py +++ b/python/sparknlp/annotator/classifier_dl/__init__.py @@ -43,3 +43,4 @@ from sparknlp.annotator.classifier_dl.tapas_for_question_answering import * from sparknlp.annotator.classifier_dl.camembert_for_sequence_classification import * from sparknlp.annotator.classifier_dl.camembert_for_question_answering import * +from sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification import * diff --git a/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py b/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py new file mode 100755 index 000000000000..532fad53510d --- /dev/null +++ b/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py @@ -0,0 +1,221 @@ +# Copyright 2017-2022 John Snow Labs +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Contains classes for BertForSequenceClassification.""" + +from sparknlp.common import * + + +class BertForZeroShotClassification(AnnotatorModel, + HasCaseSensitiveProperties, + HasBatchedAnnotate, + HasClassifierActivationProperties, + HasCandidateLabelsProperties, + HasEngine): + """BertForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language + inference) tasks. Equivalent of `BertForSequenceClassification` models, but these models don't require a hardcoded + number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more + flexible. + + Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis + pair and passed to the pretrained model. + + Pretrained models can be loaded with :meth:`.pretrained` of the companion + object: + + >>> sequenceClassifier = BertForZeroShotClassification.pretrained() \\ + ... .setInputCols(["token", "document"]) \\ + ... .setOutputCol("label") + + The default model is ``"bert_base_cased_zero_shot_classifier_xnli"``, if no name is + provided. + + For available pretrained models please see the `Models Hub + `__. + + To see which models are compatible and how to import them see + `Import Transformers into Spark NLP 🚀 + `_. + + ====================== ====================== + Input Annotation types Output Annotation type + ====================== ====================== + ``DOCUMENT, TOKEN`` ``CATEGORY`` + ====================== ====================== + + Parameters + ---------- + batchSize + Batch size. Large values allows faster processing but requires more + memory, by default 8 + caseSensitive + Whether to ignore case in tokens for embeddings matching, by default + True + configProtoBytes + ConfigProto from tensorflow, serialized into byte array. + maxSentenceLength + Max sentence length to process, by default 128 + coalesceSentences + Instead of 1 class per sentence (if inputCols is `sentence`) output 1 + class per document by averaging probabilities in all sentences, by + default False + activation + Whether to calculate logits via Softmax or Sigmoid, by default + `"softmax"`. + + Examples + -------- + >>> import sparknlp + >>> from sparknlp.base import * + >>> from sparknlp.annotator import * + >>> from pyspark.ml import Pipeline + >>> documentAssembler = DocumentAssembler() \\ + ... .setInputCol("text") \\ + ... .setOutputCol("document") + >>> tokenizer = Tokenizer() \\ + ... .setInputCols(["document"]) \\ + ... .setOutputCol("token") + >>> sequenceClassifier = BertForZeroShotClassification.pretrained() \\ + ... .setInputCols(["token", "document"]) \\ + ... .setOutputCol("label") \\ + ... .setCaseSensitive(True) + >>> pipeline = Pipeline().setStages([ + ... documentAssembler, + ... tokenizer, + ... sequenceClassifier + ... ]) + >>> data = spark.createDataFrame([["I loved this movie when I was a child.", "It was pretty boring."]]).toDF("text") + >>> result = pipeline.fit(data).transform(data) + >>> result.select("label.result").show(truncate=False) + +------+ + |result| + +------+ + |[pos] | + |[neg] | + +------+ + """ + name = "BertForZeroShotClassification" + + inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN] + + outputAnnotatorType = AnnotatorType.CATEGORY + + maxSentenceLength = Param(Params._dummy(), + "maxSentenceLength", + "Max sentence length to process", + typeConverter=TypeConverters.toInt) + + configProtoBytes = Param(Params._dummy(), + "configProtoBytes", + "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()", + TypeConverters.toListInt) + + coalesceSentences = Param(Params._dummy(), "coalesceSentences", + "Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences.", + TypeConverters.toBoolean) + + def getClasses(self): + """ + Returns labels used to train this model + """ + return self._call_java("getClasses") + + def setConfigProtoBytes(self, b): + """Sets configProto from tensorflow, serialized into byte array. + + Parameters + ---------- + b : List[int] + ConfigProto from tensorflow, serialized into byte array + """ + return self._set(configProtoBytes=b) + + def setMaxSentenceLength(self, value): + """Sets max sentence length to process, by default 128. + + Parameters + ---------- + value : int + Max sentence length to process + """ + return self._set(maxSentenceLength=value) + + def setCoalesceSentences(self, value): + """Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences. + Due to max sequence length limit in almost all transformer models such as BERT (512 tokens), this parameter helps feeding all the sentences + into the model and averaging all the probabilities for the entire document instead of probabilities per sentence. (Default: true) + + Parameters + ---------- + value : bool + If the output of all sentences will be averaged to one output + """ + return self._set(coalesceSentences=value) + + @keyword_only + def __init__(self, classname="com.johnsnowlabs.nlp.annotators.classifier.dl.BertForZeroShotClassification", + java_model=None): + super(BertForZeroShotClassification, self).__init__( + classname=classname, + java_model=java_model + ) + self._setDefault( + batchSize=8, + maxSentenceLength=128, + caseSensitive=True, + coalesceSentences=False, + activation="softmax" + ) + + @staticmethod + def loadSavedModel(folder, spark_session): + """Loads a locally saved model. + + Parameters + ---------- + folder : str + Folder of the saved model + spark_session : pyspark.sql.SparkSession + The current SparkSession + + Returns + ------- + BertForZeroShotClassification + The restored model + """ + from sparknlp.internal import _BertZeroShotClassifierLoader + jModel = _BertZeroShotClassifierLoader(folder, spark_session._jsparkSession)._java_obj + return BertForZeroShotClassification(java_model=jModel) + + @staticmethod + def pretrained(name="bert_base_cased_zero_shot_classifier_xnli", lang="en", remote_loc=None): + """Downloads and loads a pretrained model. + + Parameters + ---------- + name : str, optional + Name of the pretrained model, by default + "bert_base_cased_zero_shot_classifier_xnli" + lang : str, optional + Language of the pretrained model, by default "en" + remote_loc : str, optional + Optional remote address of the resource, by default None. Will use + Spark NLPs repositories otherwise. + + Returns + ------- + BertForZeroShotClassification + The restored model + """ + from sparknlp.pretrained import ResourceDownloader + return ResourceDownloader.downloadModel(BertForZeroShotClassification, name, lang, remote_loc) diff --git a/python/sparknlp/common/properties.py b/python/sparknlp/common/properties.py index d9eb3d8bcbe6..79db876d7e24 100644 --- a/python/sparknlp/common/properties.py +++ b/python/sparknlp/common/properties.py @@ -389,3 +389,47 @@ def getEngine(self): Deep Learning engine used for this model" """ return self.getOrDefault(self.engine) + + +class HasCandidateLabelsProperties: + candidateLabels = Param(Params._dummy(), "candidateLabels", + "Deep Learning engine used for this model", + typeConverter=TypeConverters.toListString) + + contradictionIdParam = Param(Params._dummy(), "contradictionIdParam", + "contradictionIdParam", + typeConverter=TypeConverters.toInt) + + entailmentIdParam = Param(Params._dummy(), "entailmentIdParam", + "contradictionIdParam", + typeConverter=TypeConverters.toInt) + + def setCandidateLabels(self, v): + """Sets candidateLabels. + + Parameters + ---------- + v : list[string] + candidateLabels + """ + return self._set(candidateLabels=v) + + def setContradictionIdParam(self, v): + """Sets contradictionIdParam. + + Parameters + ---------- + v : int + contradictionIdParam + """ + return self._set(contradictionIdParam=v) + + def setEntailmentIdParam(self, v): + """Sets entailmentIdParam. + + Parameters + ---------- + v : int + entailmentIdParam + """ + return self._set(entailmentIdParam=v) diff --git a/python/sparknlp/internal/__init__.py b/python/sparknlp/internal/__init__.py index d33c9d2fd26c..b22fb5caa597 100644 --- a/python/sparknlp/internal/__init__.py +++ b/python/sparknlp/internal/__init__.py @@ -503,3 +503,9 @@ class _RobertaQAToZeroShotNerLoader(ExtendedJavaWrapper): def __init__(self, path): super(_RobertaQAToZeroShotNerLoader, self).__init__( "com.johnsnowlabs.nlp.annotators.ner.dl.ZeroShotNerModel.load", path) + + +class _BertZeroShotClassifierLoader(ExtendedJavaWrapper): + def __init__(self, path, jspark): + super(_BertZeroShotClassifierLoader, self).__init__( + "com.johnsnowlabs.nlp.annotators.classifier.dl.BertForZeroShotClassification.loadSavedModel", path, jspark) diff --git a/python/test/annotator/classifier_dl/bert_for_zero_shot_classification_test.py b/python/test/annotator/classifier_dl/bert_for_zero_shot_classification_test.py new file mode 100644 index 000000000000..e7f9f76f7f23 --- /dev/null +++ b/python/test/annotator/classifier_dl/bert_for_zero_shot_classification_test.py @@ -0,0 +1,52 @@ +# Copyright 2017-2022 John Snow Labs +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import unittest +import pytest + +from sparknlp.annotator import * +from sparknlp.base import * +from test.util import SparkContextForTest + + +@pytest.mark.slow +class BertForZeroShotClassificationTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + self.text = "I have a problem with my iphone that needs to be resolved asap!!" + self.inputDataset = self.spark.createDataFrame([[self.text]]) \ + .toDF("text") + + def runTest(self): + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + + tokenizer = Tokenizer().setInputCols("document").setOutputCol("token") + + zero_shot_classifier = BertForZeroShotClassification \ + .pretrained() \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") \ + .setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) + + pipeline = Pipeline(stages=[ + document_assembler, + tokenizer, + zero_shot_classifier + ]) + + model = pipeline.fit(self.inputDataset) + model.transform(self.inputDataset).show() + light_pipeline = LightPipeline(model) + annotations_result = light_pipeline.fullAnnotate(self.text) diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala index b3c0e0a593d5..3f4c78f92272 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala @@ -71,6 +71,11 @@ private[johnsnowlabs] class AlbertClassification( sentenceTokenPieces } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ??? + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -223,6 +228,12 @@ private[johnsnowlabs] class AlbertClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = ??? + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { val tensors = new TensorResources() diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/BertClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/BertClassification.scala index cecb81608083..83ebcb740827 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/BertClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/BertClassification.scala @@ -79,6 +79,24 @@ private[johnsnowlabs] class BertClassification( } } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = { + + val basicTokenizer = new BasicTokenizer(caseSensitive) + val encoder = new WordpieceEncoder(vocabulary) + + val labelsToSentences = candidateLabels.map { s => Sentence(s, 0, s.length - 1, 0) } + + labelsToSentences.map(label => { + val tokens = basicTokenizer.tokenize(label) + val wordpieceTokens = tokens.flatMap(token => encoder.encode(token)).take(maxSeqLength) + WordpieceTokenizedSentence(wordpieceTokens) + }) + + } + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -246,6 +264,78 @@ private[johnsnowlabs] class BertClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = { + val tensors = new TensorResources() + + val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max + val batchLength = batch.length + + val tokenBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength) + val maskBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength) + val segmentBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength) + + // [nb of encoded sentences , maxSentenceLength] + val shape = Array(batch.length.toLong, maxSentenceLength) + + batch.zipWithIndex + .foreach { case (sentence, idx) => + val offset = idx * maxSentenceLength + tokenBuffers.offset(offset).write(sentence) + maskBuffers.offset(offset).write(sentence.map(x => if (x == 0) 0 else 1)) + val sentenceEndTokenIndex = sentence.indexOf(sentenceEndTokenId) + segmentBuffers + .offset(offset) + .write( + sentence.indices + .map(i => + if (i < sentenceEndTokenIndex) 0 + else if (i == sentenceEndTokenIndex) 1 + else 1) + .toArray) + } + + val session = tensorflowWrapper.getTFSessionWithSignature( + configProtoBytes = configProtoBytes, + savedSignatures = signatures, + initAllTables = false) + val runner = session.runner + + val tokenTensors = tensors.createIntBufferTensor(shape, tokenBuffers) + val maskTensors = tensors.createIntBufferTensor(shape, maskBuffers) + val segmentTensors = tensors.createIntBufferTensor(shape, segmentBuffers) + + runner + .feed( + _tfBertSignatures.getOrElse(ModelSignatureConstants.InputIds.key, "missing_input_id_key"), + tokenTensors) + .feed( + _tfBertSignatures + .getOrElse(ModelSignatureConstants.AttentionMask.key, "missing_input_mask_key"), + maskTensors) + .feed( + _tfBertSignatures + .getOrElse(ModelSignatureConstants.TokenTypeIds.key, "missing_segment_ids_key"), + segmentTensors) + .fetch(_tfBertSignatures + .getOrElse(ModelSignatureConstants.LogitsOutput.key, "missing_logits_key")) + + val outs = runner.run().asScala + val rawScores = TensorResources.extractFloats(outs.head) + + outs.foreach(_.close()) + tensors.clearSession(outs) + tensors.clearTensors() + + val dim = rawScores.length / batchLength + rawScores + .grouped(dim) + .toArray + } + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { val tensors = new TensorResources() diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala index b6c9e02fc564..7a7b80a40adc 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala @@ -81,6 +81,11 @@ private[johnsnowlabs] class CamemBertClassification( sentenceTokenPieces } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ??? + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -223,6 +228,12 @@ private[johnsnowlabs] class CamemBertClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = ??? + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { val tensors = new TensorResources() diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/DeBertaClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/DeBertaClassification.scala index 7a16944625da..c56b34e5e5ef 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/DeBertaClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/DeBertaClassification.scala @@ -71,6 +71,11 @@ private[johnsnowlabs] class DeBertaClassification( sentenceTokenPieces } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ??? + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -223,6 +228,12 @@ private[johnsnowlabs] class DeBertaClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = ??? + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { val tensors = new TensorResources() diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/DistilBertClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/DistilBertClassification.scala index 13097f5260c1..e4f816f64074 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/DistilBertClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/DistilBertClassification.scala @@ -80,6 +80,11 @@ private[johnsnowlabs] class DistilBertClassification( } } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ??? + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -235,6 +240,12 @@ private[johnsnowlabs] class DistilBertClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = ??? + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { val tensors = new TensorResources() diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/RoBertaClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/RoBertaClassification.scala index 2cfae20113b4..26ebf3812bf4 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/RoBertaClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/RoBertaClassification.scala @@ -80,6 +80,11 @@ private[johnsnowlabs] class RoBertaClassification( } } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ??? + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -229,6 +234,12 @@ private[johnsnowlabs] class RoBertaClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = ??? + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { val tensors = new TensorResources() diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/XXXForClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/XXXForClassification.scala index 3333be19e818..9354f124f06f 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/XXXForClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/XXXForClassification.scala @@ -124,6 +124,96 @@ private[johnsnowlabs] trait XXXForClassification { } + def predictSequenceWithZeroShot( + tokenizedSentences: Seq[TokenizedSentence], + sentences: Seq[Sentence], + candidateLabels: Array[String], + entailmentId: Int, + contradictionId: Int, + batchSize: Int, + maxSentenceLength: Int, + caseSensitive: Boolean, + coalesceSentences: Boolean = false, + tags: Map[String, Int], + activation: String = ActivationFunction.softmax): Seq[Annotation] = { + + val wordPieceTokenizedSentences = + tokenizeWithAlignment(tokenizedSentences, maxSentenceLength, caseSensitive) + + val candidateLabelsKeyValue = candidateLabels.zipWithIndex.toMap + val contradiction_id: Int = if (entailmentId == 0) contradictionId else 0 + + val labelsTokenized = + tokenizeSeqString(candidateLabels, maxSentenceLength, caseSensitive) + + /*Run calculation by batches*/ + wordPieceTokenizedSentences + .zip(sentences) + .zipWithIndex + .grouped(batchSize) + .flatMap { batch => + val tokensBatch = batch.map(x => (x._1._1, x._2)) + + /* Start internal batching for zero shot */ + val encodedTokensLabels = tokensBatch.map { sent => + labelsTokenized.flatMap(labels => + encodeSequence(Seq(sent._1), Seq(labels), maxSentenceLength)) + } + + val logits = encodedTokensLabels.map { encodedSeq => + tagZeroShotSequence(encodedSeq, entailmentId, contradictionId, activation) + } + + val multiClassScores = + logits.map(scores => calculateSoftmax(scores.map(x => x(entailmentId)))).toArray + val multiLabelScores = + logits + .map(scores => + scores + .map(x => calculateSoftmax(Array(x(contradiction_id), x(entailmentId)))) + .map(_.last)) + .toArray + + activation match { + case ActivationFunction.softmax => + if (coalesceSentences) { + val scores = multiClassScores.transpose.map(_.sum / multiClassScores.length) + val label = scoresToLabelForSequenceClassifier(candidateLabelsKeyValue, scores) + val meta = constructMetaForSequenceClassifier(candidateLabelsKeyValue, scores) + Array(constructAnnotationForSequenceClassifier(sentences.head, label, meta)) + } else { + sentences.zip(multiClassScores).map { case (sentence, scores) => + val label = scoresToLabelForSequenceClassifier(candidateLabelsKeyValue, scores) + val meta = constructMetaForSequenceClassifier(candidateLabelsKeyValue, scores) + constructAnnotationForSequenceClassifier(sentence, label, meta) + } + } + + case ActivationFunction.sigmoid => + if (coalesceSentences) { + val scores = multiLabelScores.transpose.map(_.sum / multiLabelScores.length) + val labels = scores.zipWithIndex + .filter(x => x._1 > 0.5) + .flatMap(x => candidateLabelsKeyValue.filter(_._2 == x._2)) + val meta = constructMetaForSequenceClassifier(candidateLabelsKeyValue, scores) + labels.map(label => + constructAnnotationForSequenceClassifier(sentences.head, label._1, meta)) + } else { + sentences.zip(multiLabelScores).flatMap { case (sentence, scores) => + val labels = scores.zipWithIndex + .filter(x => x._1 > 0.5) + .flatMap(x => candidateLabelsKeyValue.filter(_._2 == x._2)) + val meta = constructMetaForSequenceClassifier(candidateLabelsKeyValue, scores) + labels.map(label => + constructAnnotationForSequenceClassifier(sentence, label._1, meta)) + } + } + } + } + .toSeq + + } + def scoresToLabelForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): String = { tags.find(_._2 == scores.zipWithIndex.maxBy(_._1)._2).map(_._1).getOrElse("NA") } @@ -212,6 +302,11 @@ private[johnsnowlabs] trait XXXForClassification { maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -264,6 +359,12 @@ private[johnsnowlabs] trait XXXForClassification { def tagSequence(batch: Seq[Array[Int]], activation: String): Array[Array[Float]] + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) /** Calculate softmax from returned logits @@ -276,7 +377,7 @@ private[johnsnowlabs] trait XXXForClassification { exp.map(x => x / exp.sum).map(_.toFloat) } - /** Calcuate sigmoid from returned logits + /** Calculate sigmoid from returned logits * @param scores * logits output from output layer * @return diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/XlmRoBertaClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/XlmRoBertaClassification.scala index b667aa8161fa..4b83f45ba11f 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/XlmRoBertaClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/XlmRoBertaClassification.scala @@ -71,6 +71,11 @@ private[johnsnowlabs] class XlmRoBertaClassification( sentenceTokenPieces } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ??? + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -212,6 +217,12 @@ private[johnsnowlabs] class XlmRoBertaClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = ??? + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { val tensors = new TensorResources() diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/XlnetClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/XlnetClassification.scala index a4f12fbbeb79..242ba9e47e62 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/XlnetClassification.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/XlnetClassification.scala @@ -72,6 +72,11 @@ private[johnsnowlabs] class XlnetClassification( sentenceTokenPieces } + def tokenizeSeqString( + candidateLabels: Seq[String], + maxSeqLength: Int, + caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ??? + def tokenizeDocument( docs: Seq[Annotation], maxSeqLength: Int, @@ -221,6 +226,12 @@ private[johnsnowlabs] class XlnetClassification( batchScores } + def tagZeroShotSequence( + batch: Seq[Array[Int]], + entailmentId: Int, + contradictionId: Int, + activation: String): Array[Array[Float]] = ??? + def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = { (Array.empty[Array[Float]], Array.empty[Array[Float]]) } diff --git a/src/main/scala/com/johnsnowlabs/nlp/HasCandidateLabelsProperties.scala b/src/main/scala/com/johnsnowlabs/nlp/HasCandidateLabelsProperties.scala new file mode 100644 index 000000000000..17334a89b3f2 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/nlp/HasCandidateLabelsProperties.scala @@ -0,0 +1,49 @@ +/* + * Copyright 2017-2022 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp + +import org.apache.spark.ml.param.{StringArrayParam, IntParam} + +trait HasCandidateLabelsProperties extends ParamsAndFeaturesWritable { + + /** Candidate labels for classification, you can set candidateLabels dynamically during the + * runtime + * + * @group param + */ + val candidateLabels: StringArrayParam = new StringArrayParam( + this, + "candidateLabels", + "Candidate labels for classification, you can set candidateLabels dynamically during the runtime") + + /** @group getParam */ + def getCandidateLabels: Array[String] = $(candidateLabels) + + /** @group setParam */ + def setCandidateLabels(value: Array[String]): this.type = set(candidateLabels, value) + + /** @group param */ + val entailmentIdParam = new IntParam(this, "entailmentIdParam", "") + + /** @group param */ + val contradictionIdParam = new IntParam(this, "contradictionIdParam", "") + + setDefault( + candidateLabels -> Array("urgent", "not_urgent"), + contradictionIdParam -> 0, + entailmentIdParam -> 2) +} diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala index 480ab6b0be7b..6ba09e517050 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala @@ -679,4 +679,11 @@ package object annotator { object Chunk2Doc extends DefaultParamsReadable[Chunk2Doc] + type BertForZeroShotClassification = + com.johnsnowlabs.nlp.annotators.classifier.dl.BertForZeroShotClassification + + object BertForZeroShotClassification + extends ReadablePretrainedBertForZeroShotModel + with ReadBertForZeroShotDLModel + } diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala index 22dab206130f..f5d97fce5fcc 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala @@ -95,7 +95,7 @@ import java.io.File * }}} * * @see - * [[BertForSequenceClassification]] for sequnece-level classification + * [[BertForSequenceClassification]] for sequence-level classification * @see * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala new file mode 100644 index 000000000000..b3c05c9d7e7f --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala @@ -0,0 +1,454 @@ +/* + * Copyright 2017-2022 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp.annotators.classifier.dl + +import com.johnsnowlabs.ml.ai.BertClassification +import com.johnsnowlabs.ml.tensorflow._ +import com.johnsnowlabs.ml.util.LoadExternalModel.{ + loadSentencePieceAsset, + loadTextAsset, + modelSanityCheck, + notSupportedEngineError +} +import com.johnsnowlabs.ml.util.ModelEngine +import com.johnsnowlabs.nlp._ +import com.johnsnowlabs.nlp.annotators.common._ +import com.johnsnowlabs.nlp.serialization.MapFeature +import com.johnsnowlabs.nlp.util.io.{ExternalResource, ReadAs, ResourceHelper} +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.ml.param.{BooleanParam, IntArrayParam, IntParam} +import org.apache.spark.ml.util.Identifiable +import org.apache.spark.sql.SparkSession + +import java.io.File + +/** BertForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural + * language inference) tasks. Equivalent of `BertForSequenceClassification` models, but these + * models don't require a hardcoded number of potential classes, they can be chosen at runtime. + * It usually means it's slower but it is much more flexible. + * + * Any combination of sequences and labels can be passed and each combination will be posed as a + * premise/hypothesis pair and passed to the pretrained model. + * + * Pretrained models can be loaded with `pretrained` of the companion object: + * {{{ + * val sequenceClassifier = BertForZeroShotClassification.pretrained() + * .setInputCols("token", "document") + * .setOutputCol("label") + * }}} + * The default model is `"bert_base_cased_zero_shot_classifier_xnli"`, if no name is provided. + * + * For available pretrained models please see the + * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * + * To see which models are compatible and how to import them see + * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended + * examples, see + * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala BertForZeroShotClassification]]. + * + * ==Example== + * {{{ + * import spark.implicits._ + * import com.johnsnowlabs.nlp.base._ + * import com.johnsnowlabs.nlp.annotator._ + * import org.apache.spark.ml.Pipeline + * + * val documentAssembler = new DocumentAssembler() + * .setInputCol("text") + * .setOutputCol("document") + * + * val tokenizer = new Tokenizer() + * .setInputCols("document") + * .setOutputCol("token") + * + * val sequenceClassifier = BertForZeroShotClassification.pretrained() + * .setInputCols("token", "document") + * .setOutputCol("label") + * .setCaseSensitive(true) + * + * val pipeline = new Pipeline().setStages(Array( + * documentAssembler, + * tokenizer, + * sequenceClassifier + * )) + * + * val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text") + * val result = pipeline.fit(data).transform(data) + * + * result.select("label.result").show(false) + * +------+ + * |result| + * +------+ + * |[pos] | + * |[neg] | + * +------+ + * }}} + * + * @see + * [[BertForZeroShotClassification]] for sequence-level classification + * @see + * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * transformer based classifiers + * @param uid + * required uid for storing annotator to disk + * @groupname anno Annotator types + * @groupdesc anno + * Required input and expected output annotator types + * @groupname Ungrouped Members + * @groupname param Parameters + * @groupname setParam Parameter setters + * @groupname getParam Parameter getters + * @groupname Ungrouped Members + * @groupprio param 1 + * @groupprio anno 2 + * @groupprio Ungrouped 3 + * @groupprio setParam 4 + * @groupprio getParam 5 + * @groupdesc param + * A list of (hyper-)parameter keys this annotator can take. Users can set and get the + * parameter values through setters and getters, respectively. + */ +class BertForZeroShotClassification(override val uid: String) + extends AnnotatorModel[BertForZeroShotClassification] + with HasBatchedAnnotate[BertForZeroShotClassification] + with WriteTensorflowModel + with HasCaseSensitiveProperties + with HasClassifierActivationProperties + with HasEngine + with HasCandidateLabelsProperties { + + /** Annotator reference id. Used to identify elements in metadata or to refer to this annotator + * type + */ + def this() = this(Identifiable.randomUID("BERT_FOR_ZERO_SHOT_CLASSIFICATION")) + + /** Input Annotator Types: DOCUMENT, TOKEN + * + * @group anno + */ + override val inputAnnotatorTypes: Array[String] = + Array(AnnotatorType.DOCUMENT, AnnotatorType.TOKEN) + + /** Output Annotator Types: CATEGORY + * + * @group anno + */ + override val outputAnnotatorType: AnnotatorType = AnnotatorType.CATEGORY + + /** @group setParam */ + def sentenceStartTokenId: Int = { + $$(vocabulary)("[CLS]") + } + + /** @group setParam */ + def sentenceEndTokenId: Int = { + $$(vocabulary)("[SEP]") + } + + /** Vocabulary used to encode the words to ids with WordPieceEncoder + * + * @group param + */ + val vocabulary: MapFeature[String, Int] = new MapFeature(this, "vocabulary") + + /** @group setParam */ + def setVocabulary(value: Map[String, Int]): this.type = { + if (get(vocabulary).isEmpty) + set(vocabulary, value) + this + } + + /** Labels used to decode predicted IDs back to string tags + * + * @group param + */ + val labels: MapFeature[String, Int] = new MapFeature(this, "labels") + + /** @group setParam */ + def setLabels(value: Map[String, Int]): this.type = { + if (get(labels).isEmpty) + set(labels, value) + this + } + + /** Returns labels used to train this model */ + def getClasses: Array[String] = { + $$(labels).keys.toArray + } + + /** Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document + * by averaging probabilities in all sentences (Default: `false`). + * + * Due to max sequence length limit in almost all transformer models such as BERT (512 tokens), + * this parameter helps feeding all the sentences into the model and averaging all the + * probabilities for the entire document instead of probabilities per sentence. + * + * @group param + */ + val coalesceSentences = new BooleanParam( + this, + "coalesceSentences", + "If sets to true the output of all sentences will be averaged to one output instead of one output per sentence. Default to true.") + + /** @group setParam */ + def setCoalesceSentences(value: Boolean): this.type = set(coalesceSentences, value) + + /** @group getParam */ + def getCoalesceSentences: Boolean = $(coalesceSentences) + + /** ConfigProto from tensorflow, serialized into byte array. Get with + * `config_proto.SerializeToString()` + * + * @group param + */ + val configProtoBytes = new IntArrayParam( + this, + "configProtoBytes", + "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()") + + /** @group setParam */ + def setConfigProtoBytes(bytes: Array[Int]): BertForZeroShotClassification.this.type = + set(this.configProtoBytes, bytes) + + /** @group getParam */ + def getConfigProtoBytes: Option[Array[Byte]] = get(this.configProtoBytes).map(_.map(_.toByte)) + + /** Max sentence length to process (Default: `128`) + * + * @group param + */ + val maxSentenceLength = + new IntParam(this, "maxSentenceLength", "Max sentence length to process") + + /** @group setParam */ + def setMaxSentenceLength(value: Int): this.type = { + require( + value <= 512, + "BERT models do not support sequences longer than 512 because of trainable positional embeddings.") + require(value >= 1, "The maxSentenceLength must be at least 1") + set(maxSentenceLength, value) + this + } + + /** @group getParam */ + def getMaxSentenceLength: Int = $(maxSentenceLength) + + /** It contains TF model signatures for the laded saved model + * + * @group param + */ + val signatures = new MapFeature[String, String](model = this, name = "signatures") + + /** @group setParam */ + def setSignatures(value: Map[String, String]): this.type = { + if (get(signatures).isEmpty) + set(signatures, value) + this + } + + /** @group getParam */ + def getSignatures: Option[Map[String, String]] = get(this.signatures) + + private var _model: Option[Broadcast[BertClassification]] = None + + /** @group setParam */ + def setModelIfNotSet( + spark: SparkSession, + tensorflowWrapper: TensorflowWrapper): BertForZeroShotClassification = { + if (_model.isEmpty) { + _model = Some( + spark.sparkContext.broadcast( + new BertClassification( + tensorflowWrapper, + sentenceStartTokenId, + sentenceEndTokenId, + configProtoBytes = getConfigProtoBytes, + tags = $$(labels), + signatures = getSignatures, + $$(vocabulary)))) + } + + this + } + + /** @group getParam */ + def getModelIfNotSet: BertClassification = _model.get.value + + /** Whether to lowercase tokens or not (Default: `true`). + * + * @group setParam + */ + override def setCaseSensitive(value: Boolean): this.type = { + if (get(caseSensitive).isEmpty) + set(this.caseSensitive, value) + this + } + + setDefault( + batchSize -> 8, + maxSentenceLength -> 128, + caseSensitive -> true, + coalesceSentences -> false) + + /** takes a document and annotations and produces new annotations of this annotator's annotation + * type + * + * @param batchedAnnotations + * Annotations that correspond to inputAnnotationCols generated by previous annotators if any + * @return + * any number of annotations processed for every input annotation. Not necessary one to one + * relationship + */ + override def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]] = { + batchedAnnotations.map(annotations => { + val sentences = SentenceSplit.unpack(annotations).toArray + val tokenizedSentences = TokenizedWithSentence.unpack(annotations).toArray + + if (tokenizedSentences.nonEmpty) { + getModelIfNotSet.predictSequenceWithZeroShot( + tokenizedSentences, + sentences, + $(candidateLabels), + $(entailmentIdParam), + $(contradictionIdParam), + $(batchSize), + $(maxSentenceLength), + $(caseSensitive), + $(coalesceSentences), + $$(labels), + $(activation)) + + } else { + Seq.empty[Annotation] + } + }) + } + + override def onWrite(path: String, spark: SparkSession): Unit = { + super.onWrite(path, spark) + writeTensorflowModelV2( + path, + spark, + getModelIfNotSet.tensorflowWrapper, + "_bert_classification", + BertForZeroShotClassification.tfFile, + configProtoBytes = getConfigProtoBytes) + } + +} + +trait ReadablePretrainedBertForZeroShotModel + extends ParamsAndFeaturesReadable[BertForZeroShotClassification] + with HasPretrained[BertForZeroShotClassification] { + override val defaultModelName: Some[String] = Some("bert_base_cased_zero_shot_classifier_xnli") + + /** Java compliant-overrides */ + override def pretrained(): BertForZeroShotClassification = super.pretrained() + + override def pretrained(name: String): BertForZeroShotClassification = super.pretrained(name) + + override def pretrained(name: String, lang: String): BertForZeroShotClassification = + super.pretrained(name, lang) + + override def pretrained( + name: String, + lang: String, + remoteLoc: String): BertForZeroShotClassification = super.pretrained(name, lang, remoteLoc) +} + +trait ReadBertForZeroShotDLModel extends ReadTensorflowModel { + this: ParamsAndFeaturesReadable[BertForZeroShotClassification] => + + override val tfFile: String = "bert_classification_tensorflow" + + def readModel( + instance: BertForZeroShotClassification, + path: String, + spark: SparkSession): Unit = { + + val tf = readTensorflowModel(path, spark, "_bert_classification_tf", initAllTables = false) + instance.setModelIfNotSet(spark, tf) + } + + addReader(readModel) + + def loadSavedModel(modelPath: String, spark: SparkSession): BertForZeroShotClassification = { + + val (localModelPath, detectedEngine) = modelSanityCheck(modelPath) + + val vocabs = loadTextAsset(localModelPath, "vocab.txt").zipWithIndex.toMap + val labels = loadTextAsset(localModelPath, "labels.txt").zipWithIndex.toMap + + val entailmentIds = labels.filter(x => x._1.toLowerCase().startsWith("entail")).values.toArray + val contradictionIds = + labels.filter(x => x._1.toLowerCase().startsWith("contradict")).values.toArray + + require( + entailmentIds.length == 1 && contradictionIds.length == 1, + s"""This annotator supports classifiers trained on NLI datasets. You must have only at least 2 or maximum 3 labels in your dataset: + + example with 3 labels: 'contradict', 'neutral', 'entailment' + example with 2 labels: 'contradict', 'entailment' + + You can modify assets/labels.txt file to match the above format. + + Current labels: ${labels.keys.mkString(", ")} + """) + + val annotatorModel = new BertForZeroShotClassification() + .setVocabulary(vocabs) + .setLabels(labels) + .setCandidateLabels(labels.keys.toArray) + + /* set the entailment id */ + annotatorModel.set(annotatorModel.entailmentIdParam, entailmentIds.head) + /* set the contradiction id */ + annotatorModel.set(annotatorModel.contradictionIdParam, contradictionIds.head) + /* set the engine */ + annotatorModel.set(annotatorModel.engine, detectedEngine) + + detectedEngine match { + case ModelEngine.tensorflow => + val (wrapper, signatures) = + TensorflowWrapper.read(localModelPath, zipped = false, useBundle = true) + + val _signatures = signatures match { + case Some(s) => s + case None => throw new Exception("Cannot load signature definitions from model!") + } + + /** the order of setSignatures is important if we use getSignatures inside + * setModelIfNotSet + */ + annotatorModel + .setSignatures(_signatures) + .setModelIfNotSet(spark, wrapper) + + case _ => + throw new Exception(notSupportedEngineError) + } + + annotatorModel + } +} + +/** This is the companion object of [[BertForZeroShotClassification]]. Please refer to that class + * for the documentation. + */ +object BertForZeroShotClassification + extends ReadablePretrainedBertForZeroShotModel + with ReadBertForZeroShotDLModel diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala index 87a326209d92..9b016c15be42 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala @@ -720,7 +720,8 @@ object PythonResourceDownloader { "TapasForQuestionAnswering" -> TapasForQuestionAnswering, "CamemBertForSequenceClassification" -> CamemBertForSequenceClassification, "CamemBertForQuestionAnswering" -> CamemBertForQuestionAnswering, - "ZeroShotNerModel" -> ZeroShotNerModel) + "ZeroShotNerModel" -> ZeroShotNerModel, + "BertForZeroShotClassification" -> BertForZeroShotClassification) // List pairs of types such as the one with key type can load a pretrained model from the value type val typeMapper: Map[String, String] = Map("ZeroShotNerModel" -> "RoBertaForQuestionAnswering") diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala new file mode 100644 index 000000000000..dbd181263442 --- /dev/null +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala @@ -0,0 +1,186 @@ +/* + * Copyright 2017-2022 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp.annotators.classifier.dl + +import com.johnsnowlabs.nlp.annotators.Tokenizer +import com.johnsnowlabs.nlp.base.DocumentAssembler +import com.johnsnowlabs.nlp.training.CoNLL +import com.johnsnowlabs.nlp.util.io.ResourceHelper +import com.johnsnowlabs.tags.SlowTest +import com.johnsnowlabs.util.Benchmark +import org.apache.spark.ml.{Pipeline, PipelineModel} +import org.apache.spark.sql.functions.{col, explode, size} +import org.scalatest.flatspec.AnyFlatSpec + +class BertForZeroShotClassificationTestSpec extends AnyFlatSpec { + + import ResourceHelper.spark.implicits._ + + val candidateLabels = + Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology") + + "BertForSBertForZeroShotClassification" should "correctly load custom model with extracted signatures" taggedAs SlowTest in { + + val ddd = Seq( + "I have a problem with my iphone that needs to be resolved asap!!", + "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.", + "I have a phone and I love it!", + "I really want to visit Germany and I am planning to go there next year.", + "Let's watch some movies tonight! I am in the mood for a horror movie.", + "Have you watched the match yesterday? It was a great game!", + "We need to harry up and get to the airport. We are going to miss our flight!") + .toDF("text") + + val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + + val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + + val tokenClassifier = BertForZeroShotClassification + .pretrained() + .setInputCols(Array("token", "document")) + .setOutputCol("multi_class") + .setCaseSensitive(true) + .setCoalesceSentences(true) + .setCandidateLabels(candidateLabels) + + val pipeline = new Pipeline().setStages( + Array(document, tokenizer, tokenClassifier)) + + val pipelineModel = pipeline.fit(ddd) + val pipelineDF = pipelineModel.transform(ddd) + + pipelineDF.select("multi_class").show(20, false) + pipelineDF.select("document.result", "multi_class.result").show(20, false) + pipelineDF + .withColumn("doc_size", size(col("document"))) + .withColumn("label_size", size(col("multi_class"))) + .where(col("doc_size") =!= col("label_size")) + .select("doc_size", "label_size", "document.result", "multi_class.result") + .show(20, false) + + val totalDocs = pipelineDF.select(explode($"document.result")).count.toInt + val totalLabels = pipelineDF.select(explode($"multi_class.result")).count.toInt + + println(s"total tokens: $totalDocs") + println(s"total embeddings: $totalLabels") + + assert(totalDocs == totalLabels) + } + + "BertForZeroShotClassification" should "be saved and loaded correctly" taggedAs SlowTest in { + + import ResourceHelper.spark.implicits._ + + val ddd = Seq( + "John Lenon was born in London and lived in Paris. My name is Sarah and I live in London", + "Rare Hendrix song draft sells for almost $17,000.", + "EU rejects German call to boycott British lamb .", + "TORONTO 1996-08-21").toDF("text") + + val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + + val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + + val tokenClassifier = BertForZeroShotClassification + .pretrained() + .setInputCols(Array("token", "document")) + .setOutputCol("label") + .setCaseSensitive(true) + .setCoalesceSentences(true) + .setCandidateLabels(candidateLabels) + .setBatchSize(2) + + val pipeline = new Pipeline().setStages(Array(document, tokenizer, tokenClassifier)) + + val pipelineModel = pipeline.fit(ddd) + val pipelineDF = pipelineModel.transform(ddd) + + pipelineDF.select("label.result").show(false) + + Benchmark.time("Time to save BertForZeroShotClassification pipeline model") { + pipelineModel.write.overwrite().save("./tmp_bertfornli_pipeline") + } + + Benchmark.time("Time to save BertForZeroShotClassification model") { + pipelineModel.stages.last + .asInstanceOf[BertForZeroShotClassification] + .write + .overwrite() + .save("./tmp_bertforsnli_model") + } + + val loadedPipelineModel = PipelineModel.load("./tmp_bertfornli_pipeline") + loadedPipelineModel.transform(ddd).select("label.result").show(false) + + val loadedSequenceModel = BertForZeroShotClassification.load("./tmp_bertforsnli_model") + println(loadedSequenceModel.getClasses.mkString("Array(", ", ", ")")) + + } + + "BertForZeroShotClassification" should "benchmark test" taggedAs SlowTest in { + + val conll = CoNLL(explodeSentences = false) + val training_data = + conll + .readDataset(ResourceHelper.spark, "src/test/resources/conll2003/eng.train") + .repartition(12) + + val tokenClassifier = BertForZeroShotClassification + .pretrained() + .setInputCols(Array("token", "sentence")) + .setOutputCol("class") + .setCaseSensitive(true) + .setCoalesceSentences(true) + .setCandidateLabels(candidateLabels) + .setBatchSize(2) + + val pipeline = new Pipeline() + .setStages(Array(tokenClassifier)) + + val pipelineDF = pipeline.fit(training_data).transform(training_data).cache() + Benchmark.time("Time to save pipeline results") { + pipelineDF.write.mode("overwrite").parquet("./tmp_nli_classifier") + } + + pipelineDF.select("class").show(2, false) + pipelineDF.select("sentence.result", "class.result").show(2, false) + + // only works if it's softmax - one lable per row + pipelineDF + .withColumn("doc_size", size(col("sentence"))) + .withColumn("label_size", size(col("class"))) + .where(col("doc_size") =!= col("label_size")) + .select("doc_size", "label_size", "sentence.result", "class.result") + .show(20, false) + + val totalDocs = pipelineDF.select(explode($"sentence.result")).count.toInt + val totalLabels = pipelineDF.select(explode($"class.result")).count.toInt + + println(s"total docs: $totalDocs") + println(s"total classes: $totalLabels") + + assert(totalDocs == totalLabels) + } +} From de6ae11cf26d4da3b19cd80e46708826d1da9f41 Mon Sep 17 00:00:00 2001 From: Prabod Rathnayaka Date: Fri, 7 Apr 2023 04:05:55 +1000 Subject: [PATCH 08/26] Sparknlp 534 Introducing BART Transformer for text-to-text generation tasks like translation and summarization (#13731) * WIP: Added Bart transformer scala files * WIP: Added BART tokenizer test and BART is locally working * WIP: Added BART tokenizer test and BART is locally working * WIP: Added Beam Hypothesis and Beam Scorer implementations * WIP: Added Logit Processors * WIP: Added Beam Search implementation * WIP: Completed Beam Search implementation WIP: Added Generate method for text generation * WIP: fixed a bug in Beam search algorithm WIP: Generate method for text generation * WIP: changed BartTransformer methods to include beam size and added description * WIP: changed BartTransformer test methods * WIP: fixed errors in BeamSearch * WIP: Updated to use separate encoder decoder model * WIP: Changed model to handle the int64 version of the model weights * WIP: Added python API implementation * Pass session and encoder state as a parameter Clean up unnecessary code * Update TopK Logit Warper Logic * Code clean up * Update Tests * Update documentation * Update documentation and python tests * Update python tests * SPARKNLP-534 move BartTokenizer to the Bart backend Signed-off-by: Maziyar Panahi * SPARKNLP-534 Fix the copyright year Signed-off-by: Maziyar Panahi * SPARKNLP-534 Add BartTransformer to annotator and ResourceDownloader Signed-off-by: Maziyar Panahi * SPARKNLP-534 Fix BartTransformer in annotator Signed-off-by: Maziyar Panahi --------- Signed-off-by: Maziyar Panahi Co-authored-by: Maziyar Panahi --- python/sparknlp/annotator/seq2seq/__init__.py | 1 + .../annotator/seq2seq/bart_transformer.py | 402 +++++++++ python/sparknlp/internal/__init__.py | 6 + .../seq2seq/bart_transformer_test.py | 280 ++++++ .../scala/com/johnsnowlabs/ml/ai/Bart.scala | 853 ++++++++++++++++++ .../ml/ai/util/Generation/Generate.scala | 279 ++++++ .../ml/ai/util/Generation/Logit/Logit.scala | 31 + .../Logit/LogitProcess/LogitProcessor.scala | 19 + .../MinLengthLogitProcessor.scala | 45 + .../NoRepeatNgramsLogitProcessor.scala | 95 ++ .../RepetitionPenaltyLogitProcessor.scala | 48 + .../Generation/Logit/LogitProcessorList.scala | 52 ++ .../Logit/LogitWarper/LogitWarper.scala | 38 + .../LogitWarper/TemperatureLogitWarper.scala | 31 + .../Logit/LogitWarper/TopKLogitWarper.scala | 60 ++ .../Logit/LogitWarper/TopPLogitWarper.scala | 66 ++ .../Generation/Search/BeamHypotheses.scala | 85 ++ .../util/Generation/Search/BeamScorer.scala | 43 + .../Generation/Search/BeamSearchScorer.scala | 176 ++++ .../com/johnsnowlabs/nlp/annotator.scala | 10 +- .../annotators/seq2seq/BartTransformer.scala | 608 +++++++++++++ .../tokenizer/bpe/BartTokenizer.scala | 31 + .../tokenizer/bpe/BpeSpecialTokens.scala | 9 + .../tokenizer/bpe/BpeTokenizer.scala | 10 +- .../nlp/pretrained/ResourceDownloader.scala | 8 +- .../nlp/annotators/seq2seq/BartTestSpec.scala | 206 +++++ .../tokenizer/bpe/BartTokenizerTestSpec.scala | 92 ++ 27 files changed, 3580 insertions(+), 4 deletions(-) create mode 100755 python/sparknlp/annotator/seq2seq/bart_transformer.py create mode 100644 python/test/annotator/seq2seq/bart_transformer_test.py create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/LogitProcessor.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/LogitWarper.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala create mode 100644 src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala create mode 100644 src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala create mode 100644 src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizer.scala create mode 100644 src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala create mode 100644 src/test/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizerTestSpec.scala diff --git a/python/sparknlp/annotator/seq2seq/__init__.py b/python/sparknlp/annotator/seq2seq/__init__.py index d0916bf48b95..f1bbfdac8453 100644 --- a/python/sparknlp/annotator/seq2seq/__init__.py +++ b/python/sparknlp/annotator/seq2seq/__init__.py @@ -16,3 +16,4 @@ from sparknlp.annotator.seq2seq.gpt2_transformer import * from sparknlp.annotator.seq2seq.marian_transformer import * from sparknlp.annotator.seq2seq.t5_transformer import * +from sparknlp.annotator.seq2seq.bart_transformer import * diff --git a/python/sparknlp/annotator/seq2seq/bart_transformer.py b/python/sparknlp/annotator/seq2seq/bart_transformer.py new file mode 100755 index 000000000000..f717cc4bf049 --- /dev/null +++ b/python/sparknlp/annotator/seq2seq/bart_transformer.py @@ -0,0 +1,402 @@ +# Copyright 2017-2023 John Snow Labs +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Contains classes for the BartTransformer.""" + +from sparknlp.common import * + + +class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): + """BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, + Translation, and Comprehension Transformer + + The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art + language generation model that was introduced by Facebook AI in 2019. It is based on the + transformer architecture and is designed to handle a wide range of natural language processing + tasks such as text generation, summarization, and machine translation. + + BART is unique in that it is both bidirectional and auto-regressive, meaning that it can + generate text both from left-to-right and from right-to-left. This allows it to capture + contextual information from both past and future tokens in a sentence,resulting in more + accurate and natural language generation. + + The model was trained on a large corpus of text data using a combination of unsupervised and + supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the + model is first trained on a large unlabeled corpus of text, and then fine-tuned on specific + downstream tasks. + + BART has achieved state-of-the-art performance on a wide range of NLP tasks, including + summarization, question-answering, and language translation. Its ability to handle multiple + tasks and its high performance on each of these tasks make it a versatile and valuable tool + for natural language processing applications. + + + Pretrained models can be loaded with :meth:`.pretrained` of the companion + object: + + >>> t5 = BartTransformer.pretrained() \\ + ... .setTask("summarize:") \\ + ... .setInputCols(["document"]) \\ + ... .setOutputCol("summaries") + + + The default model is ``"bart_large_cnn"``, if no name is provided. For available + pretrained models please see the `Models Hub + `__. + + For extended examples of usage, see the `Examples + `__. + + ====================== ====================== + Input Annotation types Output Annotation type + ====================== ====================== + ``DOCUMENT`` ``DOCUMENT`` + ====================== ====================== + + Parameters + ---------- + configProtoBytes + ConfigProto from tensorflow, serialized into byte array. + task + Transformer's task, e.g. ``summarize:`` + minOutputLength + Minimum length of the sequence to be generated + maxOutputLength + Maximum length of output text + doSample + Whether or not to use sampling; use greedy decoding otherwise + temperature + The value used to module the next token probabilities + topK + The number of highest probability vocabulary tokens to keep for + top-k-filtering + BeamSize + The number of beam size for beam search + topP + Top cumulative probability for vocabulary tokens + + If set to float < 1, only the most probable tokens with probabilities + that add up to ``topP`` or higher are kept for generation. + repetitionPenalty + The parameter for repetition penalty. 1.0 means no penalty. + noRepeatNgramSize + If set to int > 0, all ngrams of that size can only occur once + ignoreTokenIds + A list of token ids which are ignored in the decoder's output + + Notes + ----- + This is a very computationally expensive module especially on larger + sequence. The use of an accelerator such as GPU is recommended. + + References + ---------- + - `Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension + `__ + - https://github.com/pytorch/fairseq + + **Paper Abstract:** + *We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. + BART is trained by (1) corrupting text with an arbitrary noising function, and (2) + learning a model to reconstruct the original text. It uses a standard Tranformer-based + neural machine translation architecture which, despite its simplicity, can be seen as + generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), + and many other more recent pretraining schemes. We evaluate a number of noising approaches, + finding the best performance by both randomly shuffling the order of the original sentences + and using a novel in-filling scheme, where spans of text are replaced with a single mask token. + BART is particularly effective when fine tuned for text generation but also works well for + comprehension tasks. It matches the performance of RoBERTa with comparable training resources + on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, + question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides + a 1.1 BLEU increase over a back-translation system for machine translation, with only target + language pretraining. We also report ablation experiments that replicate other pretraining + schemes within the BART framework, to better measure which factors most influence end-task performance.* + + Examples + -------- + >>> import sparknlp + >>> from sparknlp.base import * + >>> from sparknlp.annotator import * + >>> from pyspark.ml import Pipeline + >>> documentAssembler = DocumentAssembler() \\ + ... .setInputCol("text") \\ + ... .setOutputCol("documents") + >>> bart = BartTransformer.pretrained("bart_large_cnn") \\ + ... .setTask("summarize:") \\ + ... .setInputCols(["documents"]) \\ + ... .setMaxOutputLength(200) \\ + ... .setOutputCol("summaries") + >>> pipeline = Pipeline().setStages([documentAssembler, bart]) + >>> data = spark.createDataFrame([[ + ... "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a " + + ... "downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness" + + ... " of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this " + + ... "paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework " + + ... "that converts all text-based language problems into a text-to-text format. Our systematic study compares " + + ... "pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens " + + ... "of language understanding tasks. By combining the insights from our exploration with scale and our new " + + ... "Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering " + + ... "summarization, question answering, text classification, and more. To facilitate future work on transfer " + + ... "learning for NLP, we release our data set, pre-trained models, and code." + ... ]]).toDF("text") + >>> result = pipeline.fit(data).transform(data) + >>> result.select("summaries.result").show(truncate=False) + +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + |result | + +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + |[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, methodologies, and practice .]| + --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + """ + + name = "BartTransformer" + + inputAnnotatorTypes = [AnnotatorType.DOCUMENT] + + outputAnnotatorType = AnnotatorType.DOCUMENT + + configProtoBytes = Param(Params._dummy(), + "configProtoBytes", + "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()", + TypeConverters.toListInt) + + task = Param(Params._dummy(), "task", "Transformer's task, e.g. summarize>", typeConverter=TypeConverters.toString) + + minOutputLength = Param(Params._dummy(), "minOutputLength", "Minimum length of the sequence to be generated", + typeConverter=TypeConverters.toInt) + + maxOutputLength = Param(Params._dummy(), "maxOutputLength", "Maximum length of output text", + typeConverter=TypeConverters.toInt) + + doSample = Param(Params._dummy(), "doSample", "Whether or not to use sampling; use greedy decoding otherwise", + typeConverter=TypeConverters.toBoolean) + + temperature = Param(Params._dummy(), "temperature", "The value used to module the next token probabilities", + typeConverter=TypeConverters.toFloat) + + topK = Param(Params._dummy(), "topK", + "The number of highest probability vocabulary tokens to keep for top-k-filtering", + typeConverter=TypeConverters.toInt) + + topP = Param(Params._dummy(), "topP", + "If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation", + typeConverter=TypeConverters.toFloat) + + repetitionPenalty = Param(Params._dummy(), "repetitionPenalty", + "The parameter for repetition penalty. 1.0 means no penalty. See `this paper `__ for more details", + typeConverter=TypeConverters.toFloat) + + noRepeatNgramSize = Param(Params._dummy(), "noRepeatNgramSize", + "If set to int > 0, all ngrams of that size can only occur once", + typeConverter=TypeConverters.toInt) + + ignoreTokenIds = Param(Params._dummy(), "ignoreTokenIds", + "A list of token ids which are ignored in the decoder's output", + typeConverter=TypeConverters.toListInt) + + beamSize = Param(Params._dummy(), "beamSize", + "The Number of beams for beam search.", + typeConverter=TypeConverters.toInt) + + def setIgnoreTokenIds(self, value): + """A list of token ids which are ignored in the decoder's output. + + Parameters + ---------- + value : List[int] + The words to be filtered out + """ + return self._set(ignoreTokenIds=value) + + def setConfigProtoBytes(self, b): + """Sets configProto from tensorflow, serialized into byte array. + + Parameters + ---------- + b : List[int] + ConfigProto from tensorflow, serialized into byte array + """ + return self._set(configProtoBytes=b) + + def setTask(self, value): + """Sets the transformer's task, e.g. ``summarize:``. + + Parameters + ---------- + value : str + The transformer's task + """ + return self._set(task=value) + + def setMinOutputLength(self, value): + """Sets minimum length of the sequence to be generated. + + Parameters + ---------- + value : int + Minimum length of the sequence to be generated + """ + return self._set(minOutputLength=value) + + def setMaxOutputLength(self, value): + """Sets maximum length of output text. + + Parameters + ---------- + value : int + Maximum length of output text + """ + return self._set(maxOutputLength=value) + + def setDoSample(self, value): + """Sets whether or not to use sampling, use greedy decoding otherwise. + + Parameters + ---------- + value : bool + Whether or not to use sampling; use greedy decoding otherwise + """ + return self._set(doSample=value) + + def setTemperature(self, value): + """Sets the value used to module the next token probabilities. + + Parameters + ---------- + value : float + The value used to module the next token probabilities + """ + return self._set(temperature=value) + + def setTopK(self, value): + """Sets the number of highest probability vocabulary tokens to keep for + top-k-filtering. + + Parameters + ---------- + value : int + Number of highest probability vocabulary tokens to keep + """ + return self._set(topK=value) + + def setTopP(self, value): + """Sets the top cumulative probability for vocabulary tokens. + + If set to float < 1, only the most probable tokens with probabilities + that add up to ``topP`` or higher are kept for generation. + + Parameters + ---------- + value : float + Cumulative probability for vocabulary tokens + """ + return self._set(topP=value) + + def setRepetitionPenalty(self, value): + """Sets the parameter for repetition penalty. 1.0 means no penalty. + + Parameters + ---------- + value : float + The repetition penalty + + References + ---------- + See `Ctrl: A Conditional Transformer Language Model For Controllable + Generation `__ for more details. + """ + return self._set(repetitionPenalty=value) + + def setNoRepeatNgramSize(self, value): + """Sets size of n-grams that can only occur once. + + If set to int > 0, all ngrams of that size can only occur once. + + Parameters + ---------- + value : int + N-gram size can only occur once + """ + return self._set(noRepeatNgramSize=value) + + def setBeamSize(self, value): + """Sets the number of beam size for beam search + + Parameters + ---------- + value : int + Number of beam size for beam search + """ + return self._set(beamSize=value) + + @keyword_only + def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.BartTransformer", java_model=None): + super(BartTransformer, self).__init__( + classname=classname, + java_model=java_model + ) + self._setDefault( + task="", + minOutputLength=0, + maxOutputLength=20, + doSample=False, + temperature=1.0, + topK=50, + topP=1.0, + repetitionPenalty=1.0, + noRepeatNgramSize=0, + ignoreTokenIds=[], + batchSize=1, + beamSize=4 + ) + + @staticmethod + def loadSavedModel(folder, spark_session): + """Loads a locally saved model. + + Parameters + ---------- + folder : str + Folder of the saved model + spark_session : pyspark.sql.SparkSession + The current SparkSession + + Returns + ------- + BartTransformer + The restored model + """ + from sparknlp.internal import _BartLoader + jModel = _BartLoader(folder, spark_session._jsparkSession)._java_obj + return BartTransformer(java_model=jModel) + + @staticmethod + def pretrained(name="t5_small", lang="en", remote_loc=None): + """Downloads and loads a pretrained model. + + Parameters + ---------- + name : str, optional + Name of the pretrained model, by default "t5_small" + lang : str, optional + Language of the pretrained model, by default "en" + remote_loc : str, optional + Optional remote address of the resource, by default None. Will use + Spark NLPs repositories otherwise. + + Returns + ------- + BartTransformer + The restored model + """ + from sparknlp.pretrained import ResourceDownloader + return ResourceDownloader.downloadModel(BartTransformer, name, lang, remote_loc) diff --git a/python/sparknlp/internal/__init__.py b/python/sparknlp/internal/__init__.py index b22fb5caa597..1dd1b5de4905 100644 --- a/python/sparknlp/internal/__init__.py +++ b/python/sparknlp/internal/__init__.py @@ -220,6 +220,12 @@ def __init__(self, path, jspark): "com.johnsnowlabs.nlp.annotators.seq2seq.T5Transformer.loadSavedModel", path, jspark) +class _BartLoader(ExtendedJavaWrapper): + def __init__(self, path, jspark): + super(_BartLoader, self).__init__( + "com.johnsnowlabs.nlp.annotators.seq2seq.BartTransformer.loadSavedModel", path, jspark) + + class _USELoader(ExtendedJavaWrapper): def __init__(self, path, jspark, loadsp): super(_USELoader, self).__init__("com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder.loadSavedModel", diff --git a/python/test/annotator/seq2seq/bart_transformer_test.py b/python/test/annotator/seq2seq/bart_transformer_test.py new file mode 100644 index 000000000000..955b6b9041c9 --- /dev/null +++ b/python/test/annotator/seq2seq/bart_transformer_test.py @@ -0,0 +1,280 @@ +# Copyright 2017-2023 John Snow Labs +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import unittest + +import pytest + +from sparknlp.annotator import * +from sparknlp.base import * +from test.util import SparkContextForTest + + +@pytest.mark.slow +class BartTransformerQATestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + data = self.spark.createDataFrame([ + [1, """PG&E stated it scheduled the blackouts in response to forecasts for high winds + amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were + scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow. + """.strip().replace("\n", " ")], + ]).toDF("id", "text") + + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + bart = BartTransformer.pretrained("bart_large_cnn") \ + .setInputCols(["documents"]) \ + .setOutputCol("answers") + + pipeline = Pipeline().setStages([document_assembler, bart]) + results = pipeline.fit(data).transform(data) + + results.select("questions.result", "answers.result").show(truncate=False) + + +@pytest.mark.slow +class BartTransformerSummaryTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + data = self.spark.createDataFrame([ + [1, """ + Heat oven to 200C/180C fan/gas 6. Line each hole of a 12-hole muffin tin with a thin strip of baking + parchment across the middle that’s long enough so the ends stick out a centimetre or two – use a dab of + butter to stick in place. Roll out two thirds of the pastry on a lightly floured surface and stamp out + 12 x 10cm circles (you may need to re-roll trimmings). Press a circle into each hole to line. + + Sprinkle 1 tsp of breadcrumbs into the base of each pie. Tip the rest of the crumbs into a mixing bowl. + Squeeze in the sausage meat, discarding the skins, along with the bacon, mace, pepper, sage and just a + little salt. Get your hands in and mash and squish everything together until the breadcrumbs have just + about disappeared. Divide mixture between the holes, packing in firmly and shaping to a dome in the middle. + + Roll out the remaining pastry and stamp out 12 x 7cm circles. Brush with a little egg and add a top to + each pie, egg-side down to stick, carefully pressing pastry edges together to seal. Brush with more egg + (don’t throw away leftovers) and sprinkle with sesame seeds. Bake for 30 mins until golden then carefully + remove the pies from the tin, using the parchment ends to help you lift them out. Sit on a parchment lined + baking tray, brush all round the sides with more egg and put back in the oven for 8 mins. Cool completely + then eat with piccalilli, or your favourite pickle. + """.strip().replace("\n", " ")]]).toDF("id", "text") + + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") + + pipeline = Pipeline().setStages([document_assembler, bart]) + results = pipeline.fit(data).transform(data) + + results.select("summaries.result").show(truncate=False) + + +@pytest.mark.slow +class BartTransformerSummaryWithRepetitionPenaltyTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + data = self.spark.createDataFrame([ + [1, """Preheat the oven to 220°C/ fan200°C/gas 7. Trim the lamb fillet of fat and cut into slices the thickness + of a chop. Cut the kidneys in half and snip out the white core. Melt a knob of dripping or 2 tablespoons + of vegetable oil in a heavy large pan. Fry the lamb fillet in batches for 3-4 minutes, turning once, until + browned. Set aside. Fry the kidneys and cook for 1-2 minutes, turning once, until browned. Set aside. + Wipe the pan with kitchen paper, then add the butter. Add the onions and fry for about 10 minutes until + softened. Sprinkle in the flour and stir well for 1 minute. Gradually pour in the stock, stirring all the + time to avoid lumps. Add the herbs. Stir the lamb and kidneys into the onions. Season well. Transfer to a + large 2.5-litre casserole. Slice the peeled potatoes thinly and arrange on top in overlapping rows. Brush + with melted butter and season. Cover and bake for 30 minutes. Reduce the oven temperature to 160°C + /fan140°C/gas 3 and cook for a further 2 hours. Then increase the oven temperature to 200°C/ fan180°C/gas 6, + uncover, and brush the potatoes with more butter. Cook uncovered for 15-20 minutes, or until golden. + """.strip().replace("\n", " ")]]).toDF("id", "text") + + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setMaxOutputLength(50) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") \ + .setRepetitionPenalty(2) + + pipeline = Pipeline().setStages([document_assembler, bart]) + results = pipeline.fit(data).transform(data) + + results.select("summaries.result").show(truncate=False) + + +@pytest.mark.slow +class BartTransformerSummaryWithSamplingAndDeactivatedTopKTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + data = self.spark.createDataFrame([ + [1, """Preheat the oven to 220°C/ fan200°C/gas 7. Trim the lamb fillet of fat and cut into slices the thickness + of a chop. Cut the kidneys in half and snip out the white core. Melt a knob of dripping or 2 tablespoons + of vegetable oil in a heavy large pan. Fry the lamb fillet in batches for 3-4 minutes, turning once, until + browned. Set aside. Fry the kidneys and cook for 1-2 minutes, turning once, until browned. Set aside. + Wipe the pan with kitchen paper, then add the butter. Add the onions and fry for about 10 minutes until + softened. Sprinkle in the flour and stir well for 1 minute. Gradually pour in the stock, stirring all the + time to avoid lumps. Add the herbs. Stir the lamb and kidneys into the onions. Season well. Transfer to a + large 2.5-litre casserole. Slice the peeled potatoes thinly and arrange on top in overlapping rows. Brush + with melted butter and season. Cover and bake for 30 minutes. Reduce the oven temperature to 160°C + /fan140°C/gas 3 and cook for a further 2 hours. Then increase the oven temperature to 200°C/ fan180°C/gas 6, + uncover, and brush the potatoes with more butter. Cook uncovered for 15-20 minutes, or until golden. + """.strip().replace("\n", " ")]]).toDF("id", "text") + + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setMaxOutputLength(50) \ + .setDoSample(True) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") \ + .setTopK(0) + + pipeline = Pipeline().setStages([document_assembler, bart]) + results = pipeline.fit(data).transform(data) + + results.select("summaries.result").show(truncate=False) + + +@pytest.mark.slow +class BartTransformerSummaryWithSamplingAndTemperatureTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + data = self.spark.createDataFrame([ + [1, """Preheat the oven to 220°C/ fan200°C/gas 7. Trim the lamb fillet of fat and cut into slices the thickness + of a chop. Cut the kidneys in half and snip out the white core. Melt a knob of dripping or 2 tablespoons + of vegetable oil in a heavy large pan. Fry the lamb fillet in batches for 3-4 minutes, turning once, until + browned. Set aside. Fry the kidneys and cook for 1-2 minutes, turning once, until browned. Set aside. + Wipe the pan with kitchen paper, then add the butter. Add the onions and fry for about 10 minutes until + softened. Sprinkle in the flour and stir well for 1 minute. Gradually pour in the stock, stirring all the + time to avoid lumps. Add the herbs. Stir the lamb and kidneys into the onions. Season well. Transfer to a + large 2.5-litre casserole. Slice the peeled potatoes thinly and arrange on top in overlapping rows. Brush + with melted butter and season. Cover and bake for 30 minutes. Reduce the oven temperature to 160°C + /fan140°C/gas 3 and cook for a further 2 hours. Then increase the oven temperature to 200°C/ fan180°C/gas 6, + uncover, and brush the potatoes with more butter. Cook uncovered for 15-20 minutes, or until golden. + """.strip().replace("\n", " ")]]).toDF("id", "text") + + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setMaxOutputLength(50) \ + .setDoSample(True) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") \ + .setTopK(50) \ + .setTemperature(0.7) + + pipeline = Pipeline().setStages([document_assembler, bart]) + results = pipeline.fit(data).transform(data) + + results.select("summaries.result").show(truncate=False) + + +@pytest.mark.slow +class BartTransformerSummaryWithSamplingAndTopPTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + data = self.spark.createDataFrame([ + [1, """Preheat the oven to 220°C/ fan200°C/gas 7. Trim the lamb fillet of fat and cut into slices the thickness + of a chop. Cut the kidneys in half and snip out the white core. Melt a knob of dripping or 2 tablespoons + of vegetable oil in a heavy large pan. Fry the lamb fillet in batches for 3-4 minutes, turning once, until + browned. Set aside. Fry the kidneys and cook for 1-2 minutes, turning once, until browned. Set aside. + Wipe the pan with kitchen paper, then add the butter. Add the onions and fry for about 10 minutes until + softened. Sprinkle in the flour and stir well for 1 minute. Gradually pour in the stock, stirring all the + time to avoid lumps. Add the herbs. Stir the lamb and kidneys into the onions. Season well. Transfer to a + large 2.5-litre casserole. Slice the peeled potatoes thinly and arrange on top in overlapping rows. Brush + with melted butter and season. Cover and bake for 30 minutes. Reduce the oven temperature to 160°C + /fan140°C/gas 3 and cook for a further 2 hours. Then increase the oven temperature to 200°C/ fan180°C/gas 6, + uncover, and brush the potatoes with more butter. Cook uncovered for 15-20 minutes, or until golden. + """.strip().replace("\n", " ")]]).toDF("id", "text") + + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + bart = BartTransformer.pretrained("bart_large_cnn")\ + .setTask("summarize:") \ + .setMaxOutputLength(50) \ + .setDoSample(True) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") \ + .setTopK(0) \ + .setTopP(0.7) + + pipeline = Pipeline().setStages([document_assembler, bart]) + results = pipeline.fit(data).transform(data) + + results.select("summaries.result").show(truncate=False) + + +@pytest.mark.slow +class BartTransformerSummaryWithSamplingTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + data = self.spark.createDataFrame([ + [1, """Preheat the oven to 220°C/ fan200°C/gas 7. Trim the lamb fillet of fat and cut into slices the thickness + of a chop. Cut the kidneys in half and snip out the white core. Melt a knob of dripping or 2 tablespoons + of vegetable oil in a heavy large pan. Fry the lamb fillet in batches for 3-4 minutes, turning once, until + browned. Set aside. Fry the kidneys and cook for 1-2 minutes, turning once, until browned. Set aside. + Wipe the pan with kitchen paper, then add the butter. Add the onions and fry for about 10 minutes until + softened. Sprinkle in the flour and stir well for 1 minute. Gradually pour in the stock, stirring all the + time to avoid lumps. Add the herbs. Stir the lamb and kidneys into the onions. Season well. Transfer to a + large 2.5-litre casserole. Slice the peeled potatoes thinly and arrange on top in overlapping rows. Brush + with melted butter and season. Cover and bake for 30 minutes. Reduce the oven temperature to 160°C + /fan140°C/gas 3 and cook for a further 2 hours. Then increase the oven temperature to 200°C/ fan180°C/gas 6, + uncover, and brush the potatoes with more butter. Cook uncovered for 15-20 minutes, or until golden. + """.strip().replace("\n", " ")]]).toDF("id", "text") + + document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setMaxOutputLength(50) \ + .setDoSample(True) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") + + pipeline = Pipeline().setStages([document_assembler, bart]) + results = pipeline.fit(data).transform(data) + + results.select("summaries.result").show(truncate=False) + diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala b/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala new file mode 100644 index 000000000000..9ba7c6b8ffe5 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala @@ -0,0 +1,853 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai + +import com.johnsnowlabs.ml.ai.util.Generation.Generate +import com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess.{ + MinLengthLogitProcessor, + NoRepeatNgramsLogitProcessor, + RepetitionPenaltyLogitProcessor +} +import com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcessorList +import com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper.{ + TemperatureLogitWarper, + TopKLogitWarper, + TopPLogitWarper +} +import com.johnsnowlabs.ml.ai.util.Generation.Search.BeamSearchScorer +import com.johnsnowlabs.ml.tensorflow.sign.{ModelSignatureConstants, ModelSignatureManager} +import com.johnsnowlabs.ml.tensorflow.{TensorResources, TensorflowWrapper} +import com.johnsnowlabs.nlp.annotators.tokenizer.bpe.{BpeTokenizer, BartTokenizer} +import com.johnsnowlabs.nlp.annotators.common.SentenceSplit +import com.johnsnowlabs.nlp.{Annotation, AnnotatorType} +import org.tensorflow.{Session, Tensor} + +import scala.collection.JavaConverters._ +import scala.collection.mutable + +/** This class is used to run Bart model for For Sequence Batches of WordpieceTokenizedSentence. + * Input for this model must be tokenized with a SentencePieceModel, + * + * @param tensorflow + * BART Model wrapper with TensorFlowWrapper + * @param bpeTokenizer + * BART Byte-Pair Encoder model with BPEWrapper + * @param configProtoBytes + * Configuration for TensorFlow session + */ + +private[johnsnowlabs] class Bart( + val tensorflow: TensorflowWrapper, + configProtoBytes: Option[Array[Byte]] = None, + signatures: Option[Map[String, String]] = None, + merges: Map[(String, String), Int], + vocabulary: Map[String, Int]) + extends Serializable + with Generate { + + private val _tfBartSignatures: Map[String, String] = + signatures.getOrElse(ModelSignatureManager.apply()) + + val bpeTokenizer: BartTokenizer = BpeTokenizer + .forModel("bart", merges = merges, vocab = vocabulary, padWithSentenceTokens = false) + .asInstanceOf[BartTokenizer] + + private val paddingTokenId = 1L + private val eosTokenId = 2L + private val vocab_size = 50264 + + private def sessionWarmup(): Unit = { + val dummyInput = Array.fill(1)(0L) ++ Array(eosTokenId) + tag( + Seq(dummyInput), + minOutputLength = 0, + maxOutputLength = 1, + doSample = false, + temperature = 0f, + topK = 0, + topP = 0f, + repetitionPenalty = 0f, + noRepeatNgramSize = 0, + randomSeed = Option(0L), + ignoreTokenIds = Array(0), + beamSize = 1) + } + + sessionWarmup() + + def tag( + batch: Seq[Array[Long]], + minOutputLength: Int, + maxOutputLength: Int, + doSample: Boolean, + temperature: Double, + topK: Int, + topP: Double, + repetitionPenalty: Double, + noRepeatNgramSize: Int, + randomSeed: Option[Long], + ignoreTokenIds: Array[Int] = Array(), + beamSize: Int): Array[Array[Long]] = { + val ignoreTokenIdsInt = ignoreTokenIds.map(_.toLong) + val expandedEncoderInputIdsVals = batch.flatMap(x => List.fill(beamSize)(x)) +// val expandedEncoderInputIdsVals = batch + val sequencesLength = expandedEncoderInputIdsVals.map(x => x.length).toArray + val maxSentenceLength = sequencesLength.max // - curLen + + val numReturn_sequences = 1 + // from config + + var effectiveBatch_size = 1 + var effectiveBatch_mult = 1 + + // set effective batch size and effective batch multiplier according to do_sample + if (doSample) { + effectiveBatch_size = expandedEncoderInputIdsVals.length * numReturn_sequences + effectiveBatch_mult = numReturn_sequences + } else { + effectiveBatch_size = expandedEncoderInputIdsVals.length + effectiveBatch_mult = 1 + } + + // Run encoder + val tensorEncoder = new TensorResources() + val inputDim = expandedEncoderInputIdsVals.length * maxSentenceLength + + val encoderInputBuffers = tensorEncoder.createLongBuffer(inputDim) + val encoderAttentionMaskBuffers = tensorEncoder.createLongBuffer(inputDim) + + val shape = Array(expandedEncoderInputIdsVals.length.toLong, maxSentenceLength) + + expandedEncoderInputIdsVals.zipWithIndex.foreach { case (tokenIds, idx) => + val offset = idx * maxSentenceLength + val diff = maxSentenceLength - tokenIds.length + + val s = tokenIds.take(maxSentenceLength) ++ Array.fill[Long](diff)(this.paddingTokenId) + encoderInputBuffers.offset(offset).write(s) + val mask = s.map(x => if (x != this.paddingTokenId) 1L else 0L) + encoderAttentionMaskBuffers.offset(offset).write(mask) + } + + val session = tensorflow.getTFSessionWithSignature( + configProtoBytes = configProtoBytes, + initAllTables = false, + savedSignatures = signatures) + + val encoderInputTensors = tensorEncoder.createLongBufferTensor(shape, encoderInputBuffers) + val encoderAttentionMaskTensors = + tensorEncoder.createLongBufferTensor(shape, encoderAttentionMaskBuffers) + + val runner = session.runner + runner + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.EncoderInputIds.key, + "missing_encoder_input_ids"), + encoderInputTensors) + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.EncoderAttentionMask.key, + "missing_encoder_attention_mask"), + encoderAttentionMaskTensors) + .fetch(_tfBartSignatures + .getOrElse(ModelSignatureConstants.EncoderOutput.key, "missing_last_hidden_state")) + + val encoderOuts = runner.run().asScala + val encoderOutsFloats = TensorResources.extractFloats(encoderOuts.head) + val dim = encoderOutsFloats.length / inputDim + val encoderOutsBatch = + encoderOutsFloats.grouped(dim).toArray.grouped(maxSentenceLength).toArray + + encoderOuts.foreach(_.close()) + + // Run decoder + val decoderEncoderStateTensorResources = new TensorResources() + val decoderEncoderStateBuffers = + decoderEncoderStateTensorResources.createFloatBuffer( + expandedEncoderInputIdsVals.length * maxSentenceLength * dim) + expandedEncoderInputIdsVals.zipWithIndex.foreach { case (_, index) => + var offset = index * maxSentenceLength * dim + encoderOutsBatch(index).foreach(encoderOutput => { + decoderEncoderStateBuffers.offset(offset).write(encoderOutput) + offset += dim + }) + } + + val decoderEncoderStateTensors = tensorEncoder.createFloatBufferTensor( + Array(expandedEncoderInputIdsVals.length.toLong, maxSentenceLength, dim), + decoderEncoderStateBuffers) + +// val modelOutputs = generateNoBeamSearch( +// batch, +// decoderEncoderStateTensors, +// encoderAttentionMaskTensors, +// maxOutputLength, +// minOutputLength, +// doSample, +// temperature, +// topK, +// topP, +// repetitionPenalty, +// noRepeatNgramSize, +// randomSeed, +// ignoreTokenIds, +// session) + + val modelOutputs = generateBeamSearch( + batch, + decoderEncoderStateTensors, + encoderAttentionMaskTensors, + maxOutputLength, + minOutputLength, + doSample, + beamSize, + 1, + temperature, + topK, + topP, + repetitionPenalty, + noRepeatNgramSize, + randomSeed, + ignoreTokenIdsInt, + session) + + tensorEncoder.clearTensors() + tensorEncoder.clearSession(encoderOuts) + modelOutputs + + } + + def generateBeamSearch( + inputIds: Seq[Array[Long]], + decoderEncoderStateTensors: Tensor, + encoderAttentionMaskTensors: Tensor, + maxOutputLength: Int, + minOutputLength: Int, + doSample: Boolean, + beamSize: Int, + numReturnSequences: Int, + temperature: Double, + topK: Int, + topP: Double, + repetitionPenalty: Double, + noRepeatNgramSize: Int, + randomSeed: Option[Long], + ignoreTokenIds: Array[Long] = Array(), + session: Session): Array[Array[Long]] = { + + var decoderInputs = inputIds.map(_ => Array(this.eosTokenId)).toArray + + var logitProcessorList = new LogitProcessorList() + + logitProcessorList.addProcess(new RepetitionPenaltyLogitProcessor(repetitionPenalty)) + + logitProcessorList.addProcess( + new NoRepeatNgramsLogitProcessor( + noRepeatNgramSize = noRepeatNgramSize, + vocabSize = this.vocab_size)) + + logitProcessorList.addProcess( + new MinLengthLogitProcessor(this.eosTokenId, minOutputLength, this.vocab_size)) + + logitProcessorList.addProcess(new TemperatureLogitWarper(temperature)) + + logitProcessorList.addProcess(new TopKLogitWarper(topK)) + + logitProcessorList.addProcess(new TopPLogitWarper(topP)) + + val beamSearchScorer = new BeamSearchScorer( + beamSize = beamSize, + batchSize = inputIds.length, + lengthPenalty = repetitionPenalty.toFloat, + doEarlyStopping = false, + numBeamHypothesisToKeep = numReturnSequences, + maxLength = maxOutputLength) + + this.beamSearch( + inputIds, + decoderInputs, + decoderEncoderStateTensors, + encoderAttentionMaskTensors, + beamSearchScorer, + logitProcessorList, + maxOutputLength, + this.paddingTokenId, + this.eosTokenId, + doSample, + randomSeed, + session) + } + + def generateNoBeamSearch( + inputIds: Seq[Array[Long]], + decoderEncoderStateTensors: Tensor, + encoderAttentionMaskTensors: Tensor, + maxOutputLength: Int, + minOutputLength: Int, + doSample: Boolean, + temperature: Double, + topK: Int, + topP: Double, + repetitionPenalty: Double, + noRepeatNgramSize: Int, + randomSeed: Option[Long], + ignoreTokenIds: Array[Int] = Array(), + session: Session): Array[Array[Long]] = { + + /** Generate sequences for each example without beam search (numBeams == 1). All returned + * sequence are generated independently. + */ + + /* Actual size of each sentence to skip padding in the TF model */ + val sequencesLength = inputIds.map(x => x.length).toArray + val maxSentenceLength = sequencesLength.max // - curLen + + val numReturn_sequences = 1 + // from config + var effectiveBatch_size = 1 + var effectiveBatch_mult = 1 + + // set effective batch size and effective batch multiplier according to do_sample + if (doSample) { + effectiveBatch_size = inputIds.length * numReturn_sequences + effectiveBatch_mult = numReturn_sequences + } else { + effectiveBatch_size = inputIds.length + effectiveBatch_mult = 1 + } + + var decoderInputs = inputIds.map(_ => Array(this.eosTokenId)).toArray + + val batch_size = effectiveBatch_size + var curLen = decoderInputs(0).length + + var stopDecoder = false + + // length of generated sentences / unfinished sentences + var unfinishedSents = List.fill(decoderInputs.length)(1) + var sentLengths = List.fill(decoderInputs.length)(maxOutputLength) + + while (!stopDecoder) { + val decoderInputLength = decoderInputs.head.length + val tensorDecoder = new TensorResources() + + val decoderInputBuffers = + tensorDecoder.createLongBuffer(decoderInputs.length * decoderInputLength) + val decoderAttentionBuffers = + tensorDecoder.createLongBuffer(decoderInputs.length * decoderInputLength) + + decoderInputs.zipWithIndex.foreach { case (pieceIds, idx) => + val offset = idx * decoderInputLength + decoderInputBuffers.offset(offset).write(pieceIds) + val paddingMasks = pieceIds.map(_ => 1L) + decoderAttentionBuffers.offset(offset).write(paddingMasks) + } + + val decoderInputTensors = tensorDecoder.createLongBufferTensor( + Array(decoderInputs.length.toLong, decoderInputLength), + decoderInputBuffers) + val decoderAttentionMaskTensors = tensorDecoder.createLongBufferTensor( + Array(decoderInputs.length.toLong, decoderInputLength), + decoderAttentionBuffers) + + val runner = session.runner + + // TODO add past to the model and use cache + runner + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderInputIds.key, + "missing_decoder_input_ids"), + decoderInputTensors) + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderAttentionMask.key, + "missing_encoder_attention_mask"), + decoderAttentionMaskTensors) + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderEncoderInputIds.key, + "missing_encoder_state"), + decoderEncoderStateTensors) + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderEncoderAttentionMask.key, + "missing_decoder_encoder_attention_mask"), + encoderAttentionMaskTensors) + .fetch(_tfBartSignatures + .getOrElse(ModelSignatureConstants.DecoderOutput.key, "missing_output_0")) + + val decoderOuts = runner.run().asScala + val decoderOutputs = TensorResources + .extractFloats(decoderOuts.head) + .grouped(vocab_size) + .toArray + .grouped(decoderInputLength) + .toArray + var nextTokenLogits = for (decoderOutput <- decoderOutputs) yield decoderOutput.last + + nextTokenLogits = nextTokenLogits.map(logits => { + logits.indices + .map(i => { + if (ignoreTokenIds.contains(i)) Float.MinValue else logits(i) + }) + .toArray + }) + + // repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858) + if (repetitionPenalty != 1.0) { + nextTokenLogits = + createNextTokenLogitsPenalties(decoderInputs, nextTokenLogits, repetitionPenalty) + } + + if (noRepeatNgramSize > 0) { + // calculate a list of banned tokens to prevent repetitively generating the same ngrams + // from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345 + val bannedTokens = + calcBannedNgramTokens(decoderInputs, batch_size, noRepeatNgramSize, curLen) + // create bannedTokens boolean mask + var bannedTokensIndicesMask = Array.empty[IndexedSeq[Boolean]] + for (bannedTokensSlice <- bannedTokens) { + bannedTokensIndicesMask = bannedTokensIndicesMask :+ + (for (token <- 0 until vocab_size) + yield if (bannedTokensSlice.contains(token)) true else false) + } + if (!bannedTokensIndicesMask.isEmpty) + nextTokenLogits = + for ((nextTokenLogit, bannedTokensIndexMask) <- nextTokenLogits.zip( + bannedTokensIndicesMask)) + yield setTensorByIndicesToValue( + nextTokenLogit, + bannedTokensIndexMask, + Float.NegativeInfinity) + } + + // set eos token prob to zero if minLength is not reached + if (!eosTokenId.isNaN && curLen < minOutputLength) { + // create eosTokenId boolean mask + val isTokenLogit_eosToken = + for (token <- 0 until vocab_size) + yield if (token == eosTokenId) true else false + + val eosTokenIndices_mask = Array.fill(batch_size)(isTokenLogit_eosToken) + + nextTokenLogits = + for ((nextTokenLogit, bannedTokensIndex_mask) <- nextTokenLogits.zip( + eosTokenIndices_mask)) + yield setTensorByIndicesToValue( + nextTokenLogit, + bannedTokensIndex_mask, + Float.NegativeInfinity) + } + + var nextToken = Array.ofDim[Long](decoderInputs.length) + + if (doSample) { + // Temperature (higher temperature => more likely to sample low probability tokens) + if (temperature != 1.0) + nextTokenLogits = + for (nextTokenLogit <- nextTokenLogits) + yield nextTokenLogit.map(_ / temperature.toFloat) + // Top-p/top-k filtering + nextTokenLogits = topKTopPFiltering(nextTokenLogits, topK, topP) + // Sample + nextToken = nextTokenLogits.map(input => categoricalSample(input, randomSeed)) + } else { + // Greedy decoding + nextToken = nextTokenLogits.map(input => input.indexOf(input.max).toLong) + } + var tokensToAdd = Array.ofDim[Long](decoderInputs.length) + + // update generations and finished sentences + if (!eosTokenId.isNaN) + // pad finished sentences if eos_token_id exist + tokensToAdd = + nextToken.zip(unfinishedSents).map(x => x._1 * x._2 + paddingTokenId * (1 - x._2)) + else + tokensToAdd = nextToken + + decoderInputs = decoderInputs + .zip(tokensToAdd) + .map(x => { + x._1 ++ Array(x._2) + }) + decoderOuts.foreach(_.close()) + + curLen += 1 + + if (!eosTokenId.isNaN) { + val eosInSents = tokensToAdd.zipWithIndex.map { case (x, ind) => + if (x == eosTokenId && ind != 0) 1 else 0 + } + // if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length + val isSentsUnfinishedAndTokenToAddIsEos = + unfinishedSents.zip(eosInSents).map(x => x._1 * x._2) + + sentLengths = sentLengths + .zip(isSentsUnfinishedAndTokenToAddIsEos) + .map(x => x._1 * (1 - x._2) + curLen * x._2) + + // unfinishedSents is set to zero if eos in sentence + unfinishedSents = + unfinishedSents.zip(isSentsUnfinishedAndTokenToAddIsEos).map(x => x._1 - x._2) + } + + tensorDecoder.clearTensors() + tensorDecoder.clearSession(decoderOuts) + decoderInputTensors.close() + + // stop when there is a eos in each sentence, or if we exceed the maximum length + // stopDecoder = curLen < maxOutputLength || unfinishedSents.max == 0 + stopDecoder = (!decoderInputs.exists(o => !(o.count(_ == this.eosTokenId) == 2)) + || (decoderInputs.head.length > maxOutputLength)) + + } +// tensorEncoder.clearTensors() + decoderInputs + } + + def createNextTokenLogitsPenalties( + inputIds: Seq[Array[Long]], + logits: Array[Array[Float]], + repetitionPenalty: Double): Array[Array[Float]] = { + // create logit penalties for already seen inputIds + val nextTokenLogits = Array.ofDim[Array[Float]](logits.length) + + for (i <- logits.indices) { + var nextTokenLogit = logits(i) + val prevInputIds = inputIds.head.distinct + for ((prevInputId, _) <- prevInputIds.zipWithIndex) { + var logitPenalty = 1.0 + if (logits(i)(prevInputId.toInt) < 0) { + logitPenalty = repetitionPenalty + } else { + logitPenalty = 1 / repetitionPenalty + } + nextTokenLogit = nextTokenLogit.updated( + prevInputId.toInt, + (logitPenalty * nextTokenLogit(prevInputId.toInt)).toFloat) + } + nextTokenLogits(i) = nextTokenLogit + } + nextTokenLogits + } + + private def calcBannedNgramTokens( + prevInputIds: Seq[Array[Long]], + numHypos: Int, + noRepeatNgramSize: Int, + curLen: Int): Array[Array[Long]] = { + // based on fairseq for noRepeatNgram in beam_search + if (curLen + 1 < noRepeatNgramSize) + // return no banned tokens if we haven't generated noRepeatNgram_size tokens yet + return Array.ofDim[Long](numHypos, 0) + val generatedNgrams = + Array.tabulate(numHypos)(_ => mutable.Map.empty[IndexedSeq[Long], List[Long]]) + for (idx <- 0 until numHypos) { + val genTokens = prevInputIds(idx) + val generatedNgram = generatedNgrams(idx) + val ngramArrays = for (e <- 0 until noRepeatNgramSize) yield genTokens.drop(e) + for (ngramInd <- ngramArrays.last.indices) { + val ngram = for (e <- ngramArrays) yield e(ngramInd) + val prevNgramTuple = ngram.dropRight(1) + generatedNgram(prevNgramTuple) = + generatedNgram.getOrElse(prevNgramTuple, List.empty[Long]) :+ ngram.last + } + } + (for (hypoIdx <- 0 until numHypos) + yield getGeneratedNgrams( + prevInputIds, + generatedNgrams, + hypoIdx, + curLen, + noRepeatNgramSize)).toArray + } + + def getGeneratedNgrams( + prevInputIds: Seq[Array[Long]], + generatedNgrams: Array[mutable.Map[IndexedSeq[Long], List[Long]]], + hypoIdx: Int, + curLen: Int, + noRepeatNgramSize: Int): Array[Long] = { + // Before decoding the next token, prevent decoding of ngrams that have already appeared + val startIdx = curLen + 1 - noRepeatNgramSize + val ngramIdx = prevInputIds(hypoIdx).slice(startIdx, curLen) + generatedNgrams(hypoIdx).getOrElse(ngramIdx, List.empty[Long]).toArray + } + + private def topKTopPFiltering( + logits: Array[Array[Float]], + topK: Int, + topP: Double, + filterValue: Float = Float.NegativeInfinity, + minTokensToKeep: Int = 1): Array[Array[Float]] = { + + /** Filter a distribution of logits using top-k and/or nucleus (top-p) filtering * Args: + * logits: logits distribution shape (batch size, vocabulary size) if topK > 0: keep only top + * k tokens with highest probability (top-k filtering). if topP < 1.0: keep the top tokens + * with cumulative probability >= topP (nucleus filtering). Nucleus filtering is described in + * Holtzman et al. (http://arxiv.org/abs/1904.09751) Make sure we keep at least + * minTokensToKeep per batch example in the output From: + * https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 + */ + var logitsUpd = logits + val logitsShape = Array(logits.length, logits(0).length) + + if (topK > 0) { + val topKup = topK.max(minTokensToKeep).min(logitsShape.last) // Safety check + + /** Remove all tokens with a probability less than the last token of the top-k */ + val removeLimit = logits(0).sortWith(_ > _).take(topKup).min + val indicesToRemove = + for (logit <- logits) + yield for (elem <- logit) yield if (elem < removeLimit) true else false + + logitsUpd = + for ((nextTokenLogit, indexToRemove) <- logits.zip(indicesToRemove)) + yield setTensorByIndicesToValue(nextTokenLogit, indexToRemove, Float.NegativeInfinity) + } + if (topP < 1.0) { + val (sortedLogits, sortedIndices) = logits(0).zipWithIndex.sorted.reverse.unzip + + val cumulativeProbs = scanLeft(softmax(sortedLogits))(0.0)(_ + _).drop(1) + + /** Remove tokens with cumulative probability above the threshold (token with 0 are kept) */ + var sortedIndicesToRemove = + for (prob <- cumulativeProbs) + yield if (prob > topP) true else false + + if (minTokensToKeep > 1) { + + /** Keep at least minTokensToKeep (set to minTokensToKeep-1 because we add the first one + * below) + */ + sortedIndicesToRemove = List.fill(sortedIndicesToRemove.take(minTokensToKeep).length)( + false) ++ sortedIndicesToRemove.drop(minTokensToKeep) + } + + /** Shift the indices to the right to keep also the first token above the threshold */ + sortedIndicesToRemove = sortedIndicesToRemove.takeRight(1) ++ sortedIndicesToRemove + .dropRight(1) + sortedIndicesToRemove = + List.fill(sortedIndicesToRemove.take(1).length)(false) ++ sortedIndicesToRemove + .drop(1) + + /** scatter sorted tensors to original indexing */ + val indicesToRemove = scatterValuesOnBatchIndices(sortedIndicesToRemove, sortedIndices) + logitsUpd = + for ((nextTokenLogit, indexToRemove) <- logits.zip( + IndexedSeq.fill(logits.length)(indicesToRemove))) + yield setTensorByIndicesToValue( + nextTokenLogit, + indexToRemove.toIndexedSeq, + Float.NegativeInfinity) + } + logitsUpd + } + + private def scanLeft[a, b](xs: Iterable[a])(s: b)(f: (b, a) => b) = + xs.foldLeft(List(s))((acc, x) => f(acc.head, x) :: acc).reverse + + private def scatterValuesOnBatchIndices( + values: List[Boolean], + batchIndices: Array[Int]): List[Boolean] = { + // scatter values to pair indices + val (_, initArray) = batchIndices.zip(values).sorted.unzip + initArray.toList + } + + private def setTensorByIndicesToValue( + prevInputIds: Array[Float], + indices: IndexedSeq[Boolean], + value: Float): Array[Float] = { + for ((inputId, index) <- prevInputIds.zip(indices)) yield if (index) value else inputId + } + + private def categoricalSample(dist: Array[Float], randomSeed: Option[Long]): Long = { + val (distFiltered, indices) = + dist.zipWithIndex.filter { case (elem, index) => !elem.isInfinite }.sorted.unzip + + if (distFiltered.length == 1) + return indices(0) + + val normalized = softmax(distFiltered) + + var randomDouble = 0.0 + if (randomSeed.isDefined) + randomDouble = new scala.util.Random(randomSeed.get).nextDouble() + else + randomDouble = scala.util.Random.nextDouble() + + var accum = 0.0 + for ((itemProb, i) <- normalized.zip(indices)) { + accum += itemProb + if (accum >= randomDouble) { + return i + } + } + indices(0) + } + + def decode(sentences: Array[Array[Long]]): Seq[String] = { + sentences.map(s => bpeTokenizer.decodeTokens(s.map(_.toInt))) + } + + def encode(sentences: Seq[Annotation], task: String): Seq[Array[Long]] = { + SentenceSplit + .unpack(sentences) + .map(s => { + val sentWithTask = + if (task.nonEmpty) s +// new Sentence( +// content = task.concat(" ").concat(s.content), +// start = s.start, +// end = s.end + task.length + 1, +// index = s.index, +// metadata = s.metadata) + else s + bpeTokenizer + .tokenize(sentWithTask) + .map(bpeTokenizer.encode) + .flatMap(_.map(_.pieceId.toLong)) + }) + } + + def predict( + sentences: Seq[Annotation], + batchSize: Int, + minOutputLength: Int, + maxOutputLength: Int, + doSample: Boolean, + temperature: Double, + topK: Int, + topP: Double, + repetitionPenalty: Double, + noRepeatNgramSize: Int, + task: String, + randomSeed: Option[Long] = None, + ignoreTokenIds: Array[Int] = Array(), + beamSize: Int): Seq[Annotation] = { + + val batchDecoder = sentences.grouped(batchSize).toArray.flatMap { batch => + val batchSP = encode(batch, task) + val spIds = tag( + batchSP, + minOutputLength, + maxOutputLength, + doSample, + temperature, + topK, + topP, + repetitionPenalty, + noRepeatNgramSize, + randomSeed, + ignoreTokenIds, + beamSize) + + decode(spIds) + + } + + var sentBegin, nextSentEnd = 0 + batchDecoder.zip(sentences).map { case (content, sent) => + nextSentEnd += content.length - 1 + val annots = new Annotation( + annotatorType = AnnotatorType.DOCUMENT, + begin = sentBegin, + end = nextSentEnd, + result = content, + metadata = sent.metadata) + sentBegin += nextSentEnd + 1 + annots + } + } + + override def getModelOutput( + encoderInputIds: Seq[Array[Long]], + decoderInputIds: Seq[Array[Long]], + decoderEncoderStateTensors: Tensor, + encoderAttentionMaskTensors: Tensor, + maxLength: Int, + session: Session): Array[Array[Float]] = { + val sequencesLength = encoderInputIds.map(x => x.length).toArray + var maxSentenceLength = sequencesLength.max // - curLen + maxSentenceLength = Math.max(maxSentenceLength, maxLength) + val vocab_size = this.vocab_size + + val decoderInputLength = decoderInputIds.head.length + val tensorDecoder = new TensorResources() + + val decoderInputBuffers = + tensorDecoder.createLongBuffer(decoderInputIds.length * decoderInputLength) + val decoderAttentionBuffers = + tensorDecoder.createLongBuffer(decoderInputIds.length * decoderInputLength) + + decoderInputIds.zipWithIndex.foreach { case (pieceIds, idx) => + val offset = idx * decoderInputLength + decoderInputBuffers.offset(offset).write(pieceIds) + val paddingMasks = pieceIds.map(_ => 1L) + decoderAttentionBuffers.offset(offset).write(paddingMasks) + } + + val decoderInputTensors = tensorDecoder.createLongBufferTensor( + Array(decoderInputIds.length.toLong, decoderInputLength), + decoderInputBuffers) + val decoderAttentionMaskTensors = tensorDecoder.createLongBufferTensor( + Array(decoderInputIds.length.toLong, decoderInputLength), + decoderAttentionBuffers) + + val runner = session.runner + + // TODO add past to the model and use cache + runner + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderInputIds.key, + "missing_decoder_input_ids"), + decoderInputTensors) + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderAttentionMask.key, + "missing_encoder_attention_mask"), + decoderAttentionMaskTensors) + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderEncoderInputIds.key, + "missing_encoder_state"), + decoderEncoderStateTensors) + .feed( + _tfBartSignatures.getOrElse( + ModelSignatureConstants.DecoderEncoderAttentionMask.key, + "missing_decoder_encoder_attention_mask"), + encoderAttentionMaskTensors) + .fetch(_tfBartSignatures + .getOrElse(ModelSignatureConstants.DecoderOutput.key, "missing_output_0")) + + val decoderOuts = runner.run().asScala + val decoderOutputs = TensorResources + .extractFloats(decoderOuts.head) + .grouped(vocab_size) + .toArray + .grouped(decoderInputLength) + .toArray + val nextTokenLogits = for (decoderOutput <- decoderOutputs) yield decoderOutput.last + tensorDecoder.clearTensors() + tensorDecoder.clearSession(decoderOuts) + decoderInputTensors.close() + nextTokenLogits + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala new file mode 100644 index 000000000000..6432043b0231 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala @@ -0,0 +1,279 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation +import com.johnsnowlabs.ml.ai.util.Generation.Search.BeamScorer +import com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcessorList +import scala.math._ +import scala.util.control.Breaks._ +import scala.util.Random +import org.tensorflow.{Session, Tensor} + +trait Generate { + def beamSearch( + encoderInputIdsVals: Seq[Array[Long]], + inputIdsVal: Seq[Array[Long]], + decoderEncoderStateTensors: Tensor, + encoderAttentionMaskTensors: Tensor, + beamScorer: BeamScorer, + logitProcessor: LogitProcessorList, + maxLength: Int, + padTokenId: Long, + eosTokenId: Long, + doSample: Boolean, + randomSeed: Option[Long], + session: Session): Array[Array[Long]] = { + var inputIds = inputIdsVal + val batchSize = beamScorer.getBeamHypothesesSeq.length + val numBeams = beamScorer.getNumBeams + val batchBeamSize = batchSize * numBeams + var currentLength = inputIds.head.length + + var beamScores = Array.ofDim[Float](batchSize * numBeams) + beamScores = beamScores.zipWithIndex.map { case (_, ind) => + if (ind % numBeams == 0) 0 else (-1e-9).toFloat + } + var beamIndices = Seq.fill(batchBeamSize)(Array[Long]()) + var nextIndices = Array[Array[Long]]() + var nextTokens = Array[Array[Long]]() + var expandedInputs = inputIds.flatMap(x => List.fill(numBeams)(x)) + val expandedEncoderInputIdsVals = encoderInputIdsVals.flatMap(x => List.fill(numBeams)(x)) + breakable { + while (true) { + + // Feed the encoder input ids and decoder input ids to the model and get the output + // return shape (beamSize,vocabSize) + val nextTokenLogits = + this.getModelOutput( + expandedEncoderInputIdsVals, + expandedInputs, + decoderEncoderStateTensors, + encoderAttentionMaskTensors, + maxLength, + session) + + // Apply log softmax to model outputs + var nextTokenScores = nextTokenLogits.map(logSoftmax) + + // Process the logits by defined logit processors + val nextTokenScoresProcessed = + logitProcessor.process(expandedInputs, nextTokenScores, currentLength) + + // Add previous beam scores to the output + nextTokenScores = nextTokenScoresProcessed.zipWithIndex.map { case (x, ind1) => + x.zipWithIndex.map { case (y, _) => + y + beamScores(ind1) + } + } + // Process the logits by defined logit warpers + if (doSample) { + nextTokenScores = logitProcessor.warp(expandedInputs, nextTokenScores, currentLength) + } + // Reshape next token score to (batchSize, vocabSize * numBeams) + val vocabSize = nextTokenScores.head.length + val reshapedNextTokenScores = + reshapeArray(nextTokenScores, batchSize, vocabSize * numBeams) + + nextTokenScores = reshapedNextTokenScores + + var nextKTopTokenScores: Array[Array[Float]] = Array[Array[Float]]() + var nextKTopTokens: Array[Array[Long]] = Array[Array[Long]]() + + if (doSample) { + val nextKIndices = nextTokenScores.map(x => { + multinomialSampling(x, 2 * numBeams, randomSeed) + }) + nextKTopTokenScores = Array.ofDim[Float](nextKIndices.length, nextKIndices.head.length) + for (i <- nextKIndices.indices) { + for (j <- nextKIndices(i).indices) { + nextKTopTokenScores(i)(j) = nextTokenScores(i)(nextKIndices(i)(j).toInt) + } + } + nextKTopTokenScores = + nextKTopTokenScores.map(x => x.zipWithIndex.sortWith(_._1 > _._1).map(_._1)) + val tempNextKInd = + nextKTopTokenScores.map(x => x.zipWithIndex.sortWith(_._1 > _._1).map(_._2)) + nextKTopTokens = Array.ofDim[Long](nextKIndices.length, nextKIndices.head.length) + + for (i <- tempNextKInd.indices) { + for (j <- tempNextKInd(i).indices) { + nextKTopTokens(i)(j) = nextKIndices(i)(tempNextKInd(i)(j)) + } + } + } else { + nextKTopTokenScores = nextTokenScores.map(x => + x.zipWithIndex.sortWith(_._1 > _._1).take(2 * numBeams).map(_._1)) + nextKTopTokens = nextTokenScores.map(x => + x.zipWithIndex.sortWith(_._1 > _._1).take(2 * numBeams).map(_._2.toLong)) + } + nextIndices = nextKTopTokens.map(y => y.map(x => x / vocabSize)) + nextTokens = nextKTopTokens.map(y => y.map(x => x % vocabSize)) + + val beamOutputs = beamScorer.process( + expandedInputs, + nextKTopTokenScores, + nextTokens, + nextIndices, + padTokenId, + eosTokenId, + beamIndices, + currentLength) + val newBeamScores = beamOutputs._1.flatMap(_.toList) + val beamNextTokens = beamOutputs._2.flatMap(_.toList) + val beamIdx = beamOutputs._3.flatMap(_.toList) + var newInputIds = Seq[Array[Long]]() + + for ((i, ind) <- beamIdx.zipWithIndex) { + val tempInput = expandedInputs(i.toInt) :+ beamNextTokens(ind) + newInputIds = newInputIds :+ (tempInput) + } + expandedInputs = newInputIds + beamScores = newBeamScores + beamIndices = beamIndices.indices.map { elem => + beamIndices(beamIdx(elem).toInt) :+ beamIdx(elem) + } + currentLength = currentLength + 1 + if (beamScorer.isDone || (expandedInputs.head.length >= maxLength)) { + break + + } + } + } + + val sequenceOutputs = beamScorer.finalize( + inputIds = expandedInputs, + finalBeamScores = beamScores, + finalBeamTokens = nextTokens.flatMap(_.toList), + finalBeamIndices = nextIndices.flatMap(_.toList), + maxLength = maxLength, + padTokenId = padTokenId, + eosTokenId = eosTokenId, + beamIndices = beamIndices) + sequenceOutputs._1 + } + + def getModelOutput( + encoderInputIds: Seq[Array[Long]], + decoderInputIds: Seq[Array[Long]], + decoderEncoderStateTensors: Tensor, + encoderAttentionMaskTensors: Tensor, + maxLength: Int, + session: Session): Array[Array[Float]] + + def logSoftmax(values: Array[Float]): Array[Float] = { + val c = values.max + val expElem = values.map(x => exp(x - c)) + val logSumExp = log(expElem.sum) + values.map(x => (x - c - logSumExp).toFloat) + } + + def reshapeArray( + inputArray: Array[Array[Float]], + numRows: Int, + numCols: Int): Array[Array[Float]] = { + if (inputArray.length * inputArray(0).length != numRows * numCols) { + throw new IllegalArgumentException( + "Number of elements in input array does not match desired shape") + } + + val flatArray = inputArray.flatten // Flatten the input array into a 1D array + val outputArray = Array.ofDim[Float](numRows, numCols) // Initialize the output array + + // Loop through the output array and fill it with elements from the flat array + for (i <- 0 until numRows) { + for (j <- 0 until numCols) { + outputArray(i)(j) = flatArray(i * numCols + j) + } + } + + outputArray // Return the reshaped array + } + + def sample(logits: Seq[Float], k: Int, seed: Long = 42): Array[Long] = { + val maxLogit = logits.max + val logitsExp = logits.map(logit => math.exp(logit - maxLogit)) + val sumExp = logitsExp.sum + val probs = logitsExp.map(exp => exp / sumExp) + val SeededRandom = new scala.util.Random(seed) + val randSeq = Seq.fill(k)(SeededRandom.nextDouble()) + var cumProb = 0.0 + var index = 0 + var results = Seq[Int]() + for (rand <- randSeq) { + while (index < probs.length - 1 && cumProb + probs(index) < rand) { + cumProb += probs(index) + index += 1 + } + results :+= index + } + results.map(_.toLong).toArray + } + + def multinomialSampling(logitValues: Array[Float], k: Int, seed: Option[Long]): Array[Long] = { + val (distFiltered, indices) = + logitValues.zipWithIndex.filter { case (elem, index) => !elem.isInfinite }.sorted.unzip + + val maxLogit = distFiltered.max + val expLogitValues = distFiltered.map(logit => math.exp(logit - maxLogit)) + val sumExpLogitValues = expLogitValues.sum + val probabilities = expLogitValues.map(_ / sumExpLogitValues) + +// val indices = Array.range(0, logitValues.length) + val selectedIndices = new Array[Long](k) + var seededRandom = new scala.util.Random() + if (seed.isDefined) { + seededRandom = new scala.util.Random(seed.get) + } + for (i <- 0 until k) { + var rand = scala.util.Random.nextDouble() + if (seed.isDefined) { + rand = new scala.util.Random(seed.get).nextDouble() + } + var cumProb = 0.0 + var j = 0 + while (j < probabilities.length - i) { + cumProb += probabilities(j) + if (rand < cumProb) { + selectedIndices(i) = indices(j) + probabilities(j) = 0.0 + indices(j) = indices(indices.length - i - 1) + j = probabilities.length + } + j += 1 + } + } + + selectedIndices + } + + def softmax(logitValues: Array[Float]): Array[Float] = { + val maxLogit = logitValues.max + val logitsExp = logitValues.map(l => Math.exp(l - maxLogit)) + val expSum = logitsExp.sum + logitsExp.map(exp => (exp / expSum).toFloat) + } + + def getCDF(probs: Array[Float]): Array[Float] = { + val cdf = Array.ofDim[Float](probs.length) + var sum = 0.0 + for (i <- probs.indices) { + sum += probs(i) + cdf(i) = sum.toFloat + } + cdf + } + +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala new file mode 100644 index 000000000000..832ba60bbe4f --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit + +abstract class Logit { + def call( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] + + protected def setTensorByIndicesToValue( + prevInputIds: Array[Float], + indices: IndexedSeq[Boolean], + value: Float): Array[Float] = { + for ((inputId, index) <- prevInputIds.zip(indices)) yield if (index) value else inputId + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/LogitProcessor.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/LogitProcessor.scala new file mode 100644 index 000000000000..bda73e477ead --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/LogitProcessor.scala @@ -0,0 +1,19 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess +import com.johnsnowlabs.ml.ai.util.Generation.Logit.Logit +abstract class LogitProcessor extends Logit diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala new file mode 100644 index 000000000000..614ddc0f17ea --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala @@ -0,0 +1,45 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess + +class MinLengthLogitProcessor(val eosTokenId: Long, val minLength: Int, val vocabSize: Int) + extends LogitProcessor { + override def call( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + if (!eosTokenId.isNaN && currentLength < this.minLength) { + // create eosTokenId boolean mask + val isTokenLogit_eosToken = + for (token <- 0 until this.vocabSize) + yield if (token == eosTokenId) true else false + + val eosTokenIndices_mask = Array.fill(scores.length)(isTokenLogit_eosToken) + + val newScores = + for ((nextTokenLogit, bannedTokensIndex_mask) <- scores.zip(eosTokenIndices_mask)) + yield this.setTensorByIndicesToValue( + nextTokenLogit, + bannedTokensIndex_mask, + Float.NegativeInfinity) + newScores + } else { + scores + } + + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala new file mode 100644 index 000000000000..e83c7107376f --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala @@ -0,0 +1,95 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess +import scala.collection.mutable + +class NoRepeatNgramsLogitProcessor(val noRepeatNgramSize: Int, val vocabSize: Int) + extends LogitProcessor { + override def call( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + if (noRepeatNgramSize > 0) { + + val bannedTokens = + calcBannedNgramTokens(inputIds, inputIds.length, this.noRepeatNgramSize, currentLength) + // create bannedTokens boolean mask + var bannedTokensIndicesMask = Array.empty[IndexedSeq[Boolean]] + for (bannedTokensSlice <- bannedTokens) { + bannedTokensIndicesMask = bannedTokensIndicesMask :+ + (for (token <- 0 until this.vocabSize) + yield if (bannedTokensSlice.contains(token)) true else false) + } + if (!bannedTokensIndicesMask.isEmpty) { + val nextTokenLogits = + for ((nextTokenLogit, bannedTokensIndexMask) <- scores.zip(bannedTokensIndicesMask)) + yield setTensorByIndicesToValue( + nextTokenLogit, + bannedTokensIndexMask, + Float.NegativeInfinity) + nextTokenLogits + } else { + scores + } + } else { + scores + } + } + + private def calcBannedNgramTokens( + prevInputIds: Seq[Array[Long]], + numHypos: Int, + noRepeatNgramSize: Int, + curLen: Int): Array[Array[Long]] = { + // based on fairseq for noRepeatNgram in beam_search + if (curLen + 1 < noRepeatNgramSize) + // return no banned tokens if we haven't generated noRepeatNgram_size tokens yet + return Array.ofDim[Long](numHypos, 0) + val generatedNgrams = + Array.tabulate(numHypos)(_ => mutable.Map.empty[IndexedSeq[Long], List[Long]]) + for (idx <- 0 until numHypos) { + val genTokens = prevInputIds(idx) + val generatedNgram = generatedNgrams(idx) + val ngramArrays = for (e <- 0 until noRepeatNgramSize) yield genTokens.drop(e) + for (ngramInd <- ngramArrays.last.indices) { + val ngram = for (e <- ngramArrays) yield e(ngramInd) + val prevNgramTuple = ngram.dropRight(1) + generatedNgram(prevNgramTuple) = + generatedNgram.getOrElse(prevNgramTuple, List.empty[Long]) :+ ngram.last + } + } + (for (hypoIdx <- 0 until numHypos) + yield getGeneratedNgrams( + prevInputIds, + generatedNgrams, + hypoIdx, + curLen, + noRepeatNgramSize)).toArray + } + + private def getGeneratedNgrams( + prevInputIds: Seq[Array[Long]], + generatedNgrams: Array[mutable.Map[IndexedSeq[Long], List[Long]]], + hypoIdx: Int, + curLen: Int, + noRepeatNgramSize: Int): Array[Long] = { + // Before decoding the next token, prevent decoding of ngrams that have already appeared + val startIdx = curLen + 1 - noRepeatNgramSize + val ngramIdx = prevInputIds(hypoIdx).slice(startIdx, curLen) + generatedNgrams(hypoIdx).getOrElse(ngramIdx, List.empty[Long]).toArray + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala new file mode 100644 index 000000000000..1d2222967b75 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala @@ -0,0 +1,48 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess + +class RepetitionPenaltyLogitProcessor(val penalty: Double) extends LogitProcessor { + override def call( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + if (penalty != 1.0) { + val nextTokenscores = Array.ofDim[Array[Float]](scores.length) + + for (i <- scores.indices) { + var nextTokenLogit = scores(i) + val prevInputIds = inputIds.head.distinct + for ((prevInputId, _) <- prevInputIds.zipWithIndex) { + var logitPenalty = 1.0 + if (scores(i)(prevInputId.toInt) < 0) { + logitPenalty = this.penalty + } else { + logitPenalty = 1 / this.penalty + } + nextTokenLogit = nextTokenLogit.updated( + prevInputId.toInt, + (logitPenalty * nextTokenLogit(prevInputId.toInt)).toFloat) + } + nextTokenscores(i) = nextTokenLogit + } + nextTokenscores + } else { + scores + } + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala new file mode 100644 index 000000000000..fe8be1edfce8 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala @@ -0,0 +1,52 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit +import com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess.LogitProcessor +import com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper.LogitWarper +class LogitProcessorList { + private var logitProcesses: List[Logit] = List() + + def addProcess(process: Logit): Unit = { + logitProcesses = logitProcesses :+ process + } + + def process( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + var tempScores = scores + logitProcesses.foreach(p => { + if (p.isInstanceOf[LogitProcessor]) { + tempScores = p.call(inputIds, tempScores, currentLength) + } + }) + tempScores + } + + def warp( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + var tempScores = scores + logitProcesses.foreach(p => { + if (p.isInstanceOf[LogitWarper]) { + tempScores = p.call(inputIds, tempScores, currentLength) + } + }) + tempScores + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/LogitWarper.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/LogitWarper.scala new file mode 100644 index 000000000000..2ede60e0344e --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/LogitWarper.scala @@ -0,0 +1,38 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper +import scala.math._ +import com.johnsnowlabs.ml.ai.util.Generation.Logit.Logit +abstract class LogitWarper extends Logit { + + protected def scanLeft[a, b](xs: Iterable[a])(s: b)(f: (b, a) => b): Seq[b] = + xs.foldLeft(List(s))((acc, x) => f(acc.head, x) :: acc).reverse + + protected def softmax(values: Array[Float]): Array[Float] = { + val expElem = values.map(exp(_)) + val total = expElem.sum + expElem.map(_ / total).map(_.toFloat) + } + + protected def scatterValuesOnBatchIndices( + values: List[Boolean], + batchIndices: Array[Int]): List[Boolean] = { + // scatter values to pair indices + val (_, initArray) = batchIndices.zip(values).sorted.unzip + initArray.toList + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala new file mode 100644 index 000000000000..611ceb699aa6 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper + +class TemperatureLogitWarper(val temperature: Double) extends LogitWarper { + override def call( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + if (temperature > 0 && temperature <= 1) { + scores.map(_.map(x => (x / temperature.toFloat))) + } else { + scores + } + + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala new file mode 100644 index 000000000000..937033d368aa --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala @@ -0,0 +1,60 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper +import scala.collection.mutable.ArrayBuffer + +class TopKLogitWarper( + val k: Int, + val filterValue: Float = Float.NegativeInfinity, + val minTokensToKeep: Int = 1) + extends LogitWarper { + override def call( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + var logitsUpd = scores + val logitsShape = Array(scores.length, scores(0).length) + if (k > 0) { + val topKup = k.max(minTokensToKeep).min(logitsShape.last) // Safety check + + /** Remove all tokens with a probability less than the last token of the top-k */ + + val topKLogits = new ArrayBuffer[Array[Float]]() + for (logits <- scores) { + val topKIndices = getTopKIndices(logits, topKup) + val maskedValues = maskNotTopKValues(logits, topKIndices) + topKLogits += maskedValues + } + topKLogits.toArray + } + logitsUpd + } + + private def getTopKIndices(logits: Array[Float], k: Int): Array[Int] = { + logits.indices.sortBy(logits(_)).reverse.take(k).toArray + } + + private def maskNotTopKValues(logits: Array[Float], topKIndices: Array[Int]): Array[Float] = { + val maskedValues = logits.clone() + for (i <- logits.indices) { + if (!topKIndices.contains(i)) { + maskedValues(i) = this.filterValue + } + } + maskedValues + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala new file mode 100644 index 000000000000..2b74e3284b6d --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala @@ -0,0 +1,66 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper + +class TopPLogitWarper(val p: Double, val minTokensToKeep: Int = 1) extends LogitWarper { + override def call( + inputIds: Seq[Array[Long]], + scores: Array[Array[Float]], + currentLength: Int): Array[Array[Float]] = { + var scoresUpd = scores + val scoresShape = Array(scores.length, scores(0).length) + if (this.p < 1.0) { + val (sortedscores, sortedIndices) = scores(0).zipWithIndex.sorted.reverse.unzip + + val cumulativeProbs = this.scanLeft(this.softmax(sortedscores))(0.0)(_ + _).drop(1) + + /** Remove tokens with cumulative probability above the threshold (token with 0 are kept) */ + var sortedIndicesToRemove = + for (prob <- cumulativeProbs) + yield if (prob > this.p) true else false + + if (minTokensToKeep > 1) { + + /** Keep at least minTokensToKeep (set to minTokensToKeep-1 because we add the first one + * below) + */ + sortedIndicesToRemove = List.fill(sortedIndicesToRemove.take(minTokensToKeep).length)( + false) ++ sortedIndicesToRemove.drop(minTokensToKeep) + } + + /** Shift the indices to the right to keep also the first token above the threshold */ + sortedIndicesToRemove = sortedIndicesToRemove.takeRight(1) ++ sortedIndicesToRemove + .dropRight(1) + sortedIndicesToRemove = + List.fill(sortedIndicesToRemove.take(1).length)(false) ++ sortedIndicesToRemove + .drop(1) + + /** scatter sorted tensors to original indexing */ + val indicesToRemove = + this.scatterValuesOnBatchIndices(sortedIndicesToRemove.toList, sortedIndices) + scoresUpd = + for ((nextTokenLogit, indexToRemove) <- scores.zip( + IndexedSeq.fill(scores.length)(indicesToRemove))) + yield setTensorByIndicesToValue( + nextTokenLogit, + indexToRemove.toIndexedSeq, + Float.NegativeInfinity) + } + scoresUpd + } + +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala new file mode 100644 index 000000000000..04c1b7c1c037 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala @@ -0,0 +1,85 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Search + +class BeamHypotheses( + var lengthPenalty: Double, + var numBeams: Int, + var earlyStopping: Boolean = false, + var maxLength: Int) { + private var beams: Seq[(Double, Array[Long], Array[Long])] = Seq() + private var worstScore: Double = 1e9 + + def length(): Int = { + beams.length + } + + def getBeams: Seq[(Double, Array[Long], Array[Long])] = { + this.beams + } + + /** Add a new hypotheses to the list + * @param hypotheses + * Hypothesis + * @param sumLogProbs + * Sum of Log Probabilities + * @param beamIndices + * Beam Indices + */ + def add(hypotheses: Array[Long], sumLogProbs: Double, beamIndices: Array[Long]): Unit = { + val score = sumLogProbs / Math.pow(hypotheses.length, this.lengthPenalty) + if (this.beams.length < this.numBeams || score > this.worstScore) { + this.beams = beams :+ (score, hypotheses, beamIndices) + if (this.beams.length > this.numBeams) { + val sortedNextScores = this.beams.zipWithIndex.sortBy(_._1._1) + + this.beams = this.beams.zipWithIndex.filter(_._2 != sortedNextScores.head._2).map(_._1) + this.worstScore = sortedNextScores(1)._1._1 + } else { + this.worstScore = Math.min(score, this.worstScore) + } + } + } + + /** If there are enough hypotheses and that none of the hypotheses being generated can become + * better than the worst one in the heap, then we are done with this sentence. + * + * @param bestSumLogProbs + * Best Sum of Log Probabilities + * @param currentLength + * Current Length + * @return + * Status of the sentence + */ + def isDone(bestSumLogProbs: Double, currentLength: Long): Boolean = { + if (this.beams.length < this.numBeams) { + false + } else if (this.earlyStopping) { + true + } else if (!this.earlyStopping) { + val currentScore = bestSumLogProbs / Math.pow(currentLength, this.lengthPenalty) + this.worstScore >= currentScore + } else { + if (this.lengthPenalty > 0) { + this.worstScore >= (bestSumLogProbs / Math.pow(this.maxLength, this.lengthPenalty)) + } else { + val currentScore = bestSumLogProbs / Math.pow(currentLength, this.lengthPenalty) + this.worstScore >= currentScore + } + } + } +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala new file mode 100644 index 000000000000..43471fd28e45 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala @@ -0,0 +1,43 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Search + +abstract class BeamScorer() { + + def process( + inputIds: Seq[Array[Long]], + nextScores: Seq[Array[Float]], + nextTokens: Seq[Array[Long]], + nextIndices: Seq[Array[Long]], + padTokenId: Long, + eosTokenId: Long, + beamIndices: Seq[Array[Long]], + currentLength: Long): (Array[Array[Float]], Array[Array[Long]], Array[Array[Long]]) + + def finalize( + inputIds: Seq[Array[Long]], + finalBeamScores: Array[Float], + finalBeamTokens: Array[Long], + finalBeamIndices: Array[Long], + maxLength: Long, + padTokenId: Long, + eosTokenId: Long, + beamIndices: Seq[Array[Long]]): (Array[Array[Long]], Array[Float], Array[Array[Long]]) + def getBeamHypothesesSeq: Seq[BeamHypotheses] + def getNumBeams: Int + def isDone: Boolean +} diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala new file mode 100644 index 000000000000..afce9389f850 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala @@ -0,0 +1,176 @@ +/* + * Copyright 2017 - 2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.ml.ai.util.Generation.Search + +import scala.util.control.Breaks._ + +class BeamSearchScorer( + var beamSize: Int, + var batchSize: Int, + var lengthPenalty: Float = 1.0f, + var doEarlyStopping: Boolean = false, + var numBeamHypothesisToKeep: Int = 1, + var maxLength: Int) + extends BeamScorer { + + val numBeams: Int = beamSize + private var beamHypothesesSeq: Seq[BeamHypotheses] = Seq.empty[BeamHypotheses] + (1 to batchSize) foreach (i => + beamHypothesesSeq = beamHypothesesSeq :+ new BeamHypotheses( + lengthPenalty = lengthPenalty, + numBeams = beamSize, + earlyStopping = doEarlyStopping, + maxLength = maxLength)) + + override def getBeamHypothesesSeq: Seq[BeamHypotheses] = { + beamHypothesesSeq + } + + override def getNumBeams: Int = numBeams + private val done: Array[Boolean] = Array.fill(batchSize)(false) + + override def process( + inputIds: Seq[Array[Long]], + nextScores: Seq[Array[Float]], + nextTokens: Seq[Array[Long]], + nextIndices: Seq[Array[Long]], + padTokenId: Long, + eosTokenId: Long, + beamIndices: Seq[Array[Long]], + currentLength: Long): (Array[Array[Float]], Array[Array[Long]], Array[Array[Long]]) = { +// val currentLength = inputIds.length + val batchSize = this.beamHypothesesSeq.length + val nextBeamScores = Array.ofDim[Float](batchSize, this.beamSize) + val nextBeamTokens = Array.ofDim[Long](batchSize, this.beamSize) + val nextBeamIndices = Array.ofDim[Long](batchSize, this.beamSize) + + this.beamHypothesesSeq.zipWithIndex.foreach { case (hypotheses, batchIdx) => + breakable { + if (this.done(batchIdx)) { + nextBeamScores(batchIdx) = nextBeamScores(batchIdx).map(_ => 0.0f) + nextBeamTokens(batchIdx) = nextBeamTokens(batchIdx).map(_ => padTokenId) + nextBeamIndices(batchIdx) = nextBeamIndices(batchIdx).map(_ => 0L) + break + } + var beamIdx = 0 + var beamTokenRank = 0 + while (beamTokenRank < nextScores(batchIdx).length && beamIdx < this.beamSize) { + + val nextScore = nextScores(batchIdx)(beamTokenRank) + val nextToken = nextTokens(batchIdx)(beamTokenRank) + val nextIndex = nextIndices(batchIdx)(beamTokenRank) + val batchBeamIdx = batchIdx * this.beamSize + nextIndex + + if (eosTokenId == nextToken) { + if (beamTokenRank >= this.beamSize) { + break + } + var beamIndex = Array[Long]() + if (beamIndices.nonEmpty) { + beamIndex = beamIndices(batchBeamIdx.toInt) + beamIndex = beamIndex.map(i => i + batchBeamIdx) + } + + hypotheses.add(inputIds(batchBeamIdx.toInt), nextScore, beamIndex) + } else { + nextBeamScores(batchIdx)(beamIdx) = nextScore + nextBeamTokens(batchIdx)(beamIdx) = nextToken + nextBeamIndices(batchIdx)(beamIdx) = batchBeamIdx + beamIdx += 1 + } + beamTokenRank += 1 + } + this.done(batchIdx) = + this.done(batchIdx) || hypotheses.isDone(nextScores(batchIdx).max, currentLength) + } + } + (nextBeamScores, nextBeamTokens, nextBeamIndices) + } + + override def finalize( + inputIds: Seq[Array[Long]], + finalBeamScores: Array[Float], + finalBeamTokens: Array[Long], + finalBeamIndices: Array[Long], + maxLength: Long, + padTokenId: Long, + eosTokenId: Long, + beamIndices: Seq[Array[Long]]): (Array[Array[Long]], Array[Float], Array[Array[Long]]) = { + val batchSize = this.beamHypothesesSeq.length + this.beamHypothesesSeq.zipWithIndex.foreach { case (hypotheses, batchIdx) => + breakable { + if (!this.done.contains(false)) { + break + } + for (beamId <- 0 until this.beamSize) { + var batchBeamIdx = batchIdx * this.beamSize + beamId + var finalScore = finalBeamScores(batchBeamIdx) + var finalTokens = inputIds(batchBeamIdx) + var beamIndex = Array[Long]() + if (beamIndices.nonEmpty) { + beamIndex = beamIndices(batchBeamIdx) + } + hypotheses.add(finalTokens, finalScore, beamIndex) + } + } + } + val sentLengths = Array.ofDim[Long](batchSize * this.numBeamHypothesisToKeep) + var best = Seq[Array[Long]]() + var bestIndices = Seq[Array[Long]]() + val bestScores = Array.ofDim[Float](batchSize * this.numBeamHypothesisToKeep) + this.beamHypothesesSeq.zipWithIndex.foreach { case (hypotheses, i) => + breakable { + var sortedHypotheses = hypotheses.getBeams.sortWith(_._1 < _._1) + for (j <- 0 until this.numBeamHypothesisToKeep) { + val bestHypothesisTuple = sortedHypotheses.last + val bestScore = bestHypothesisTuple._1 + val bestHypothesis = bestHypothesisTuple._2 + val bestIndex = bestHypothesisTuple._3 + sentLengths(this.numBeamHypothesisToKeep * i + j) = bestHypothesis.length + best = best :+ bestHypothesis + bestIndices = bestIndices :+ bestIndex + bestScores(i * this.numBeamHypothesisToKeep + j) = bestScore.toFloat + sortedHypotheses = sortedHypotheses.dropRight(1) + } + } + } + val sentLengthMax = sentLengths.max + 1 + val sentMaxLength = Math.min(sentLengthMax, maxLength) + 1 + var decoded = Array.ofDim[Long](batchSize * this.numBeamHypothesisToKeep, sentMaxLength.toInt) + var indices = Array.ofDim[Long](batchSize * this.numBeamHypothesisToKeep, sentMaxLength.toInt) + + if (sentLengths.min != sentLengths.max) { + decoded = decoded.map(each => each.map(_ => padTokenId)) + } + indices = indices.map(each => each.map(_ => -1L)) + for (i <- best.indices) { + val hypo = best(i) + val bestIdx = bestIndices(i) + for (j <- 0 until sentLengths(i).toInt) { + decoded(i)(j) = hypo(j) +// indices(i)(j) = bestIdx(j) + } + if (sentLengths(i) < sentMaxLength) { + decoded(i)(sentLengths(i).toInt) = eosTokenId + } + } + (decoded, bestScores, indices) + + } + + override def isDone: Boolean = !this.done.contains(false) +} diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala index 6ba09e517050..33cd0da4f951 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala @@ -674,13 +674,19 @@ package object annotator { type Date2Chunk = com.johnsnowlabs.nlp.annotators.Date2Chunk object Date2Chunk extends DefaultParamsReadable[Date2Chunk] - + type Chunk2Doc = com.johnsnowlabs.nlp.annotators.Chunk2Doc object Chunk2Doc extends DefaultParamsReadable[Chunk2Doc] + type BartTransformer = com.johnsnowlabs.nlp.annotators.seq2seq.BartTransformer + + object BartTransformer + extends ReadablePretrainedBartTransformerModel + with ReadBartTransformerDLModel + type BertForZeroShotClassification = - com.johnsnowlabs.nlp.annotators.classifier.dl.BertForZeroShotClassification + com.johnsnowlabs.nlp.annotators.classifier.dl.BertForZeroShotClassification object BertForZeroShotClassification extends ReadablePretrainedBertForZeroShotModel diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala new file mode 100644 index 000000000000..c59408f117db --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala @@ -0,0 +1,608 @@ +/* + * Copyright 2017-2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp.annotators.seq2seq + +import com.johnsnowlabs.ml.ai.Bart +import com.johnsnowlabs.ml.tensorflow.{ + ReadTensorflowModel, + TensorflowWrapper, + WriteTensorflowModel +} +import com.johnsnowlabs.ml.util.LoadExternalModel.{ + loadTextAsset, + modelSanityCheck, + notSupportedEngineError +} +import com.johnsnowlabs.ml.util.ModelEngine +import com.johnsnowlabs.nlp.AnnotatorType.DOCUMENT +import com.johnsnowlabs.nlp._ +import com.johnsnowlabs.nlp.serialization.MapFeature +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.ml.param._ +import org.apache.spark.ml.util.Identifiable +import org.apache.spark.sql.SparkSession + +/** BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, + * Translation, and Comprehension Transformer + * + * The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art + * language generation model that was introduced by Facebook AI in 2019. It is based on the + * transformer architecture and is designed to handle a wide range of natural language processing + * tasks such as text generation, summarization, and machine translation. + * + * BART is unique in that it is both bidirectional and auto-regressive, meaning that it can + * generate text both from left-to-right and from right-to-left. This allows it to capture + * contextual information from both past and future tokens in a sentence,resulting in more + * accurate and natural language generation. + * + * The model was trained on a large corpus of text data using a combination of unsupervised and + * supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the + * model is first trained on a large unlabeled corpus of text, and then fine-tuned on specific + * downstream tasks. + * + * BART has achieved state-of-the-art performance on a wide range of NLP tasks, including + * summarization, question-answering, and language translation. Its ability to handle multiple + * tasks and its high performance on each of these tasks make it a versatile and valuable tool + * for natural language processing applications. + * + * Pretrained models can be loaded with `pretrained` of the companion object: + * {{{ + * val bart = BartTransformer.pretrained() + * .setInputCols("document") + * .setOutputCol("generation") + * }}} + * The default model is `"bart_large_cnn"`, if no name is provided. For available pretrained + * models please see the [[https://nlp.johnsnowlabs.com/models?q=bart Models Hub]]. + * + * For extended examples of usage, see + * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala BartTestSpec]]. + * + * '''References:''' + * - [[https://aclanthology.org/2020.acl-main.703.pdf BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension]] + * - [[https://github.com/pytorch/fairseq]] + * + * '''Paper Abstract:''' + * + * '' We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART + * is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model + * to reconstruct the original text. It uses a standard Tranformer-based neural machine + * translation architecture which, despite its simplicity, can be seen as generalizing BERT (due + * to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent + * pretraining schemes. We evaluate a number of noising approaches, finding the best performance + * by both randomly shuffling the order of sentences and using a novel in-filling scheme, where + * spans of text are replaced with a single mask token. BART is particularly effective when fine + * tuned for text generation but also works well for comprehension tasks. It matches the + * performance of RoBERTa on GLUE and SQuAD, and achieves new stateof-the-art results on a range + * of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 + * ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine + * translation, with only target language pretraining. We also replicate other pretraining + * schemes within the BART framework, to understand their effect on end-task performance '' + * + * '''Note:''' + * + * This is a very computationally expensive module especially on larger sequence. The use of an + * accelerator such as GPU is recommended. + * + * ==Example== + * {{{ + * import spark.implicits._ + * import com.johnsnowlabs.nlp.base.DocumentAssembler + * import com.johnsnowlabs.nlp.annotators.seq2seq.GPT2Transformer + * import org.apache.spark.ml.Pipeline + * + * val documentAssembler = new DocumentAssembler() + * .setInputCol("text") + * .setOutputCol("documents") + * + * val bart = BartTransformer.pretrained("bart_large_cnn") + * .setInputCols(Array("documents")) + * .setMinOutputLength(10) + * .setMaxOutputLength(30) + * .setDoSample(true) + * .setTopK(50) + * .setOutputCol("generation") + * + * val pipeline = new Pipeline().setStages(Array(documentAssembler, bart)) + * + * val data = Seq( + * "PG&E stated it scheduled the blackouts in response to forecasts for high winds " + + * "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " + + * "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow." + * ).toDF("text") + * val result = pipeline.fit(data).transform(data) + * + * results.select("generation.result").show(truncate = false) + * +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + * |result | + * +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + * |[Nearly 800 thousand customers were affected by the shutoffs.]| + * +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + * }}} + * + * @param uid + * required uid for storing annotator to disk + * @groupname anno Annotator types + * @groupdesc anno + * Required input and expected output annotator types + * @groupname Ungrouped Members + * @groupname param Parameters + * @groupname setParam Parameter setters + * @groupname getParam Parameter getters + * @groupname Ungrouped Members + * @groupprio param 1 + * @groupprio anno 2 + * @groupprio Ungrouped 3 + * @groupprio setParam 4 + * @groupprio getParam 5 + * @groupdesc param + * A list of (hyper-)parameter keys this annotator can take. Users can set and get the + * parameter values through setters and getters, respectively. + */ +class BartTransformer(override val uid: String) + extends AnnotatorModel[BartTransformer] + with HasBatchedAnnotate[BartTransformer] + with ParamsAndFeaturesWritable + with WriteTensorflowModel + with HasEngine { + + def this() = this(Identifiable.randomUID("BartTRANSFORMER")) + + /** Input annotator type : DOCUMENT + * + * @group param + */ + override val inputAnnotatorTypes: Array[AnnotatorType] = Array(DOCUMENT) + + /** Output annotator type : DOCUMENT + * + * @group param + */ + override val outputAnnotatorType: String = DOCUMENT + + /** Set transformer task, e.g. `"summarize:"` (Default: `""`). + * + * @group param + */ + val task = new Param[String](this, "task", "Set transformer task, e.g. 'summarize'") + + /** @group setParam */ + def setTask(value: String): BartTransformer.this.type = { + if (get(task).isEmpty) + set(task, value) + this + } + + /** Minimum length of the sequence to be generated (Default: `0`) + * + * @group param + */ + val minOutputLength = + new IntParam(this, "minOutputLength", "Minimum length of the sequence to be generated") + + /** @group setParam */ + def setMinOutputLength(value: Int): BartTransformer.this.type = { + set(minOutputLength, value) + this + } + + /** @group getParam */ + def getMinOutputLength: Int = $(this.minOutputLength) + + /** Maximum length of the sequence to be generated (Default: `20`) + * + * @group param + */ + val maxOutputLength = + new IntParam(this, "maxOutputLength", "Maximum length of the sequence to be generated") + + /** @group setParam */ + def setMaxOutputLength(value: Int): BartTransformer.this.type = { + set(maxOutputLength, value) + this + } + + /** @group getParam */ + def getMaxOutputLength: Int = $(this.maxOutputLength) + + /** Whether or not to use sampling, use greedy decoding otherwise (Default: `false`) + * + * @group param + */ + val doSample = new BooleanParam( + this, + "doSample", + "Whether or not to use sampling; use greedy decoding otherwise") + + /** @group setParam */ + def setDoSample(value: Boolean): BartTransformer.this.type = { + set(doSample, value) + this + } + + /** @group getParam */ + def getDoSample: Boolean = $(this.doSample) + + /** The value used to module the next token probabilities (Default: `1.0`) + * + * @group param + */ + val temperature = + new DoubleParam(this, "temperature", "The value used to module the next token probabilities") + + /** @group setParam */ + def setTemperature(value: Double): BartTransformer.this.type = { + set(temperature, value) + this + } + + /** @group getParam */ + def getTemperature: Double = $(this.temperature) + + /** The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: + * `50`) + * + * @group param + */ + val topK = new IntParam( + this, + "topK", + "The number of highest probability vocabulary tokens to keep for top-k-filtering") + + /** @group setParam */ + def setTopK(value: Int): BartTransformer.this.type = { + set(topK, value) + this + } + + /** @group getParam */ + def getTopK: Int = $(this.topK) + + /** If set to float < `1.0`, only the most probable tokens with probabilities that add up to + * `topP` or higher are kept for generation (Default: `1.0`) + * + * @group param + */ + val topP = new DoubleParam( + this, + "topP", + "If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation") + + /** @group setParam */ + def setTopP(value: Double): BartTransformer.this.type = { + set(topP, value) + this + } + + /** @group getParam */ + def getTopP: Double = $(this.topP) + + /** The parameter for repetition penalty (Default: `1.0`). `1.0` means no penalty. See + * [[https://arxiv.org/pdf/1909.05858.pdf this paper]] for more details. + * + * @group param + */ + val repetitionPenalty = new DoubleParam( + this, + "repetitionPenalty", + "The parameter for repetition penalty. 1.0 means no penalty.") + + /** @group setParam */ + def setRepetitionPenalty(value: Double): BartTransformer.this.type = { + set(repetitionPenalty, value) + this + } + + /** @group getParam */ + def getRepetitionPenalty: Double = $(this.repetitionPenalty) + + /** If set to int > `0`, all ngrams of that size can only occur once (Default: `0`) + * + * @group param + */ + val noRepeatNgramSize = new IntParam( + this, + "noRepeatNgramSize", + "If set to int > 0, all ngrams of that size can only occur once") + + /** @group setParam */ + def setNoRepeatNgramSize(value: Int): BartTransformer.this.type = { + set(noRepeatNgramSize, value) + this + } + + /** @group getParam */ + def getNoRepeatNgramSize: Int = $(this.noRepeatNgramSize) + + /** Optional Random seed for the model. Needs to be of type `Int`. + * + * @group param + */ + var randomSeed: Option[Long] = None + + /** @group setParam */ + def setRandomSeed(value: Long): BartTransformer.this.type = { + if (randomSeed.isEmpty) { + this.randomSeed = Some(value) + } + this + } + + /** @group getParam */ + def getRandomSeed: Option[Long] = this.randomSeed + + /** A list of token ids which are ignored in the decoder's output (Default: `Array()`) + * + * @group param + */ + var ignoreTokenIds = new IntArrayParam( + this, + "ignoreTokenIds", + "A list of token ids which are ignored in the decoder's output") + + /** @group setParam */ + def setIgnoreTokenIds(tokenIds: Array[Int]): BartTransformer.this.type = { + set(ignoreTokenIds, tokenIds) + } + + /** @group getParam */ + def getIgnoreTokenIds: Array[Int] = $(ignoreTokenIds) + + /** Beam size for the beam search algorithm (Default: `4`) + * + * @group param + */ + var beamSize = new IntParam(this, "beamSize", "Number of beams for beam search.") + + /** @group setParam */ + def setBeamSize(beamNum: Int): BartTransformer.this.type = { + set(beamSize, beamNum) + } + + /** @group getParam */ + def getBeamSize: Int = $(beamSize) + + /** ConfigProto from tensorflow, serialized into byte array. Get with + * config_proto.SerializeToString() + * + * @group param + */ + val configProtoBytes = new IntArrayParam( + this, + "configProtoBytes", + "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()") + + /** @group setParam */ + def setConfigProtoBytes(bytes: Array[Int]): BartTransformer.this.type = + set(this.configProtoBytes, bytes) + + /** @group getParam */ + def getConfigProtoBytes: Option[Array[Byte]] = get(this.configProtoBytes).map(_.map(_.toByte)) + + /** It contains TF model signatures for the laded saved model + * + * @group param + */ + val signatures = new MapFeature[String, String](model = this, name = "signatures") + + /** @group setParam */ + def setSignatures(value: Map[String, String]): this.type = { + if (get(signatures).isEmpty) + set(signatures, value) + this + } + + /** @group getParam */ + def getSignatures: Option[Map[String, String]] = get(this.signatures) + private var _tfModel: Option[Broadcast[Bart]] = None + + /** Vocabulary used to encode the words to ids with bpeTokenizer.encode + * + * @group param + */ + val vocabulary: MapFeature[String, Int] = new MapFeature(this, "vocabulary") + + /** @group setParam */ + def setVocabulary(value: Map[String, Int]): this.type = set(vocabulary, value) + + /** Holding merges.txt coming from RoBERTa model + * + * @group param + */ + val merges: MapFeature[(String, String), Int] = new MapFeature(this, "merges") + + /** @group setParam */ + def setMerges(value: Map[(String, String), Int]): this.type = set(merges, value) + + /** @group setParam */ + def setModelIfNotSet(spark: SparkSession, tfWrapper: TensorflowWrapper): this.type = { + if (_tfModel.isEmpty) { + + _tfModel = Some( + spark.sparkContext.broadcast( + new Bart( + tfWrapper, + configProtoBytes = getConfigProtoBytes, + signatures = getSignatures, + $$(merges), + $$(vocabulary)))) + } + this + } + + /** @group getParam */ + def getModelIfNotSet: Bart = _tfModel.get.value + + setDefault( + task -> "", + minOutputLength -> 0, + maxOutputLength -> 20, + doSample -> false, + temperature -> 1.0, + topK -> 50, + topP -> 1.0, + repetitionPenalty -> 1.0, + noRepeatNgramSize -> 0, + ignoreTokenIds -> Array(), + batchSize -> 1, + beamSize -> 4) + + override def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]] = { + + val allAnnotations = batchedAnnotations + .filter(_.nonEmpty) + .zipWithIndex + .flatMap { case (annotations, i) => + annotations.filter(_.result.nonEmpty).map(x => (x, i)) + } + + val processedAnnotations = if (allAnnotations.nonEmpty) { + this.getModelIfNotSet.predict( + sentences = allAnnotations.map(_._1), + batchSize = $(batchSize), + minOutputLength = $(minOutputLength), + maxOutputLength = $(maxOutputLength), + doSample = $(doSample), + temperature = $(temperature), + topK = $(topK), + topP = $(topP), + repetitionPenalty = $(repetitionPenalty), + noRepeatNgramSize = $(noRepeatNgramSize), + task = $(task), + randomSeed = this.randomSeed, + ignoreTokenIds = $(ignoreTokenIds), + beamSize = $(beamSize)) + } else { + Seq() + } + + // Group resulting annotations by rows. If there are not sentences in a given row, return empty sequence + batchedAnnotations.indices.map(rowIndex => { + val rowAnnotations = processedAnnotations + // zip each annotation with its corresponding row index + .zip(allAnnotations) + // select the sentences belonging to the current row + .filter(_._2._2 == rowIndex) + // leave the annotation only + .map(_._1) + + if (rowAnnotations.nonEmpty) + rowAnnotations + else + Seq.empty[Annotation] + }) + } + + override def onWrite(path: String, spark: SparkSession): Unit = { + super.onWrite(path, spark) + writeTensorflowModelV2( + path, + spark, + getModelIfNotSet.tensorflow, + "_bart", + BartTransformer.tfFile, + configProtoBytes = getConfigProtoBytes, + savedSignatures = getSignatures) + } +} + +trait ReadablePretrainedBartTransformerModel + extends ParamsAndFeaturesReadable[BartTransformer] + with HasPretrained[BartTransformer] { + override val defaultModelName: Some[String] = Some("bart") + + /** Java compliant-overrides */ + override def pretrained(): BartTransformer = super.pretrained() + + override def pretrained(name: String): BartTransformer = super.pretrained(name) + + override def pretrained(name: String, lang: String): BartTransformer = + super.pretrained(name, lang) + + override def pretrained(name: String, lang: String, remoteLoc: String): BartTransformer = + super.pretrained(name, lang, remoteLoc) +} + +trait ReadBartTransformerDLModel extends ReadTensorflowModel { + this: ParamsAndFeaturesReadable[BartTransformer] => + + override val tfFile: String = "bart_tensorflow" + + def readModel(instance: BartTransformer, path: String, spark: SparkSession): Unit = { + val tf = readTensorflowModel( + path, + spark, + "_bart_tf", + savedSignatures = instance.getSignatures, + initAllTables = false) + instance.setModelIfNotSet(spark, tf) + } + + addReader(readModel) + + def loadSavedModel(modelPath: String, spark: SparkSession): BartTransformer = { + + val (localModelPath, detectedEngine) = modelSanityCheck(modelPath) + + val vocabs = loadTextAsset(localModelPath, "vocab.txt").zipWithIndex.toMap + + val bytePairs = loadTextAsset(localModelPath, "merges.txt") + .map(_.split(" ")) + .filter(w => w.length == 2) + .map { case Array(c1, c2) => (c1, c2) } + .zipWithIndex + .toMap + + /*Universal parameters for all engines*/ + val annotatorModel = new BartTransformer() + .setVocabulary(vocabs) + .setMerges(bytePairs) + + annotatorModel.set(annotatorModel.engine, detectedEngine) + + detectedEngine match { + case ModelEngine.tensorflow => + val (wrapper, signatures) = + TensorflowWrapper.read( + localModelPath, + zipped = false, + useBundle = true, + tags = Array("serve")) + + val _signatures = signatures match { + case Some(s) => s + case None => throw new Exception("Cannot load signature definitions from model!") + } + + /** the order of setSignatures is important if we use getSignatures inside + * setModelIfNotSet + */ + annotatorModel + .setSignatures(_signatures) + .setModelIfNotSet(spark, wrapper) + + case _ => + throw new Exception(notSupportedEngineError) + } + + annotatorModel + } + +} + +object BartTransformer + extends ReadablePretrainedBartTransformerModel + with ReadBartTransformerDLModel diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizer.scala new file mode 100644 index 000000000000..c5b250578ec8 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizer.scala @@ -0,0 +1,31 @@ +/* + * Copyright 2017-2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp.annotators.tokenizer.bpe + +class BartTokenizer( + merges: Map[(String, String), Int], + vocab: Map[String, Int], + specialTokens: SpecialTokens, + padWithSentenceTokens: Boolean = false, + addPrefixSpace: Boolean = false) + extends Gpt2Tokenizer( + merges, + vocab, + specialTokens, + padWithSentenceTokens, + prependString = "Ġ", + addPrefixSpace) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeSpecialTokens.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeSpecialTokens.scala index 96f33148ad1a..ccfb69118eec 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeSpecialTokens.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeSpecialTokens.scala @@ -90,6 +90,15 @@ private[nlp] object SpecialTokens { "", "", "")) + case "bart" => + new SpecialTokens( + vocab, + startTokenString = "", + endTokenString = "", + unkTokenString = "", + maskTokenString = "", + padTokenString = "") + } } diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeTokenizer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeTokenizer.scala index 080f26413cd5..c777d12f475a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeTokenizer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BpeTokenizer.scala @@ -313,7 +313,7 @@ object BpeTokenizer { padWithSentenceTokens: Boolean = false, addPrefixSpace: Boolean = false, specialTokens: Option[SpecialTokens] = None): BpeTokenizer = { - val availableModels = Array("roberta", "xlm", "gpt2") + val availableModels = Array("roberta", "xlm", "gpt2", "bart") require( availableModels.contains(modelType), "Model type \"" + modelType + "\" not supported yet.") @@ -341,6 +341,14 @@ object BpeTokenizer { padWithSentenceTokens, addPrefixSpace = addPrefixSpace) + case "bart" => + new BartTokenizer( + merges, + vocab, + modelSpecialTokens, + padWithSentenceTokens, + addPrefixSpace = addPrefixSpace) + } } } diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala index 9b016c15be42..96618415fc37 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala @@ -37,7 +37,12 @@ import com.johnsnowlabs.nlp.annotators.sbd.pragmatic.SentenceDetector import com.johnsnowlabs.nlp.annotators.sda.pragmatic.SentimentDetectorModel import com.johnsnowlabs.nlp.annotators.sda.vivekn.ViveknSentimentModel import com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLModel -import com.johnsnowlabs.nlp.annotators.seq2seq.{GPT2Transformer, MarianTransformer, T5Transformer} +import com.johnsnowlabs.nlp.annotators.seq2seq.{ + BartTransformer, + GPT2Transformer, + MarianTransformer, + T5Transformer +} import com.johnsnowlabs.nlp.annotators.spell.context.ContextSpellCheckerModel import com.johnsnowlabs.nlp.annotators.spell.norvig.NorvigSweetingModel import com.johnsnowlabs.nlp.annotators.spell.symmetric.SymmetricDeleteModel @@ -721,6 +726,7 @@ object PythonResourceDownloader { "CamemBertForSequenceClassification" -> CamemBertForSequenceClassification, "CamemBertForQuestionAnswering" -> CamemBertForQuestionAnswering, "ZeroShotNerModel" -> ZeroShotNerModel, + "BartTransformer" -> BartTransformer, "BertForZeroShotClassification" -> BertForZeroShotClassification) // List pairs of types such as the one with key type can load a pretrained model from the value type diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala new file mode 100644 index 000000000000..d10fdf5386ef --- /dev/null +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala @@ -0,0 +1,206 @@ +/* + * Copyright 2017-2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp.annotators.seq2seq + +import com.johnsnowlabs.nlp.base.DocumentAssembler +import com.johnsnowlabs.nlp.util.io.ResourceHelper +import com.johnsnowlabs.tags.SlowTest +import com.johnsnowlabs.util.Benchmark +import org.apache.spark.ml.Pipeline +import org.scalatest.flatspec.AnyFlatSpec + +class BartTestSpec extends AnyFlatSpec { + + "bart-large-cnn" should "should handle temperature=0 correctly and not crash when predicting more than 1 element with doSample=True" taggedAs SlowTest in { + // Even tough the Paper states temperature in interval [0,1), using temperature=0 will result in division by 0 error. + // Also DoSample=True may result in infinities being generated and distFiltered.length==0 which results in exception if we don't return 0 instead internally. + val testData = ResourceHelper.spark + .createDataFrame( + Seq( + ( + 1, + "PG&E stated it scheduled the blackouts in response to forecasts for high winds " + + "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " + + "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."))) + .toDF("id", "text") + .repartition(1) + val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + + val bart = BartTransformer + .pretrained("bart_large_cnn") + .setTask("summarize:") + .setInputCols(Array("documents")) + .setDoSample(true) + .setMaxOutputLength(30) + .setOutputCol("generation") + new Pipeline() + .setStages(Array(documentAssembler, bart)) + .fit(testData) + .transform(testData) + .show(truncate = false) + + } + "bart-large-cnn" should "run SparkNLP pipeline with maxLength=130 and doSample=true" taggedAs SlowTest in { + val testData = ResourceHelper.spark + .createDataFrame( + Seq(( + 1, + "New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.\nA " + + "year later, she got married again in Westchester County, but to a different man and without divorcing her first husband." + + "\nOnly 18 days after that marriage, she got hitched yet again. Then, Barrientos declared \"I do\" five more times, sometimes " + + "only within two weeks of each other.\nIn 2010, she married once more, this time in the Bronx. In an application for a marriage " + + "license, she stated it was her \"first and only\" marriage.\nBarrientos, now 39, is facing two criminal counts of \"offering a " + + "false instrument for filing in the first degree,\" referring to her false statements on the\n2010 marriage license application, " + + "according to court documents.\nProsecutors said the marriages were part of an immigration scam.\nOn Friday, she pleaded not guilty " + + "at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.\nAfter leaving court, " + + "Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an " + + "emergency exit, said Detective\nAnnette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her " + + "marriages occurring between 1999 and 2002.\nAll occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to" + + " still be married to four men, and at one time, she was married to eight men at once, prosecutors say.\nProsecutors said the immigration scam involved " + + "some of her husbands, who filed for permanent residence status shortly after the marriages.\nAny divorces happened only after such filings were" + + " approved. It was unclear whether any of the men will be prosecuted.\nThe case was referred to the Bronx District Attorney\\'s Office " + + "by Immigration and Customs Enforcement and the Department of Homeland Security\\'s\nInvestigation Division. Seven of the men are from so-called" + + " \"red-flagged\" countries, including Egypt, Turkey, Georgia, Pakistan and Mali."))) + .toDF("id", "text") + + val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + + val bart = BartTransformer + .pretrained("bart_large_cnn") + .setTask("summarize:") + .setInputCols(Array("documents")) + .setMaxOutputLength(70) + .setMinOutputLength(30) + .setDoSample(true) + .setOutputCol("summaries") + + val pipeline = new Pipeline().setStages(Array(documentAssembler, bart)) + + val model = pipeline.fit(testData) + val results = model.transform(testData) + + results.select("summaries.result").show(truncate = false) +// val dataframe = results.select("summaries.result").collect() +// val result = dataframe.toSeq.head.getAs[Seq[String]](0).head +// println(result) + // assert( + // result == "a knob of dripping or 2 tablespoons of vegetable oil in a large large pan . cut the kidneys in half and snip out the white core . heat the pan for 1-2 minutes, turning once, until browned .") + } + "bart-large-cnn" should "run SparkNLP pipeline with maxLength=100 " taggedAs SlowTest in { + val testData = ResourceHelper.spark + .createDataFrame( + Seq( + ( + 1, + "Preheat the oven to 220°C/ fan200°C/gas 7. Trim the lamb fillet of fat and cut into slices the thickness" + + " of a chop. Cut the kidneys in half and snip out the white core. Melt a knob of dripping or 2 tablespoons " + + "of vegetable oil in a heavy large pan. Fry the lamb fillet in batches for 3-4 minutes, turning once, until " + + "browned. Set aside. Fry the kidneys and cook for 1-2 minutes, turning once, until browned. Set aside." + + "Wipe the pan with kitchen paper, then add the butter. Add the onions and fry for about 10 minutes until " + + "softened. Sprinkle in the flour and stir well for 1 minute. Gradually pour in the stock, stirring all the " + + "time to avoid lumps. Add the herbs. Stir the lamb and kidneys into the onions. Season well. Transfer to a" + + " large 2.5-litre casserole. Slice the peeled potatoes thinly and arrange on top in overlapping rows. Brush " + + "with melted butter and season. Cover and bake for 30 minutes. Reduce the oven temperature to 160°C" + + "/fan140°C/gas 3 and cook for a further 2 hours. Then increase the oven temperature to 200°C/ fan180°C/gas 6," + + " uncover, and brush the potatoes with more butter. Cook uncovered for 15-20 minutes, or until golden."))) + .toDF("id", "text") + + val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + + val bart = BartTransformer + .pretrained("bart_large_cnn") + .setTask("summarize:") + .setInputCols(Array("documents")) + .setMaxOutputLength(100) + .setOutputCol("summaries") + + val pipeline = new Pipeline().setStages(Array(documentAssembler, bart)) + + val model = pipeline.fit(testData) + val results = model.transform(testData) + + results.select("summaries.result").show(truncate = false) + val dataframe = results.select("summaries.result").collect() + val result = dataframe.toSeq.head.getAs[Seq[String]](0).head + println(result) +// assert( +// result == "a knob of dripping or 2 tablespoons of vegetable oil in a large large pan . cut the kidneys in half and snip out the white core . heat the pan for 1-2 minutes, turning once, until browned .") + } + "bart-large-cnn" should "run SparkNLP pipeline with doSample=true " taggedAs SlowTest in { + val testData = ResourceHelper.spark + .createDataFrame( + Seq( + ( + 1, + "Preheat the oven to 220°C/ fan200°C/gas 7. Trim the lamb fillet of fat and cut into slices the thickness" + + " of a chop. Cut the kidneys in half and snip out the white core. Melt a knob of dripping or 2 tablespoons " + + "of vegetable oil in a heavy large pan. Fry the lamb fillet in batches for 3-4 minutes, turning once, until " + + "browned. Set aside. Fry the kidneys and cook for 1-2 minutes, turning once, until browned. Set aside." + + "Wipe the pan with kitchen paper, then add the butter. Add the onions and fry for about 10 minutes until " + + "softened. Sprinkle in the flour and stir well for 1 minute. Gradually pour in the stock, stirring all the " + + "time to avoid lumps. Add the herbs. Stir the lamb and kidneys into the onions. Season well. Transfer to a" + + " large 2.5-litre casserole. Slice the peeled potatoes thinly and arrange on top in overlapping rows. Brush " + + "with melted butter and season. Cover and bake for 30 minutes. Reduce the oven temperature to 160°C" + + "/fan140°C/gas 3 and cook for a further 2 hours. Then increase the oven temperature to 200°C/ fan180°C/gas 6," + + " uncover, and brush the potatoes with more butter. Cook uncovered for 15-20 minutes, or until golden."))) + .toDF("id", "text") + + val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + + val bart = BartTransformer + .pretrained("bart_large_cnn") + .setTask("summarize:") + .setInputCols(Array("documents")) + .setDoSample(true) + .setRandomSeed(56) + .setMaxOutputLength(50) + .setOutputCol("summaries") + + val pipeline = new Pipeline().setStages(Array(documentAssembler, bart)) + + val model = pipeline.fit(testData) + + val dataframe1 = model + .transform(testData) + .select("summaries.result") + .collect() + .toSeq + .head + .getAs[Seq[String]](0) + .head + println(dataframe1) + val dataframe2 = model + .transform(testData) + .select("summaries.result") + .collect() + .toSeq + .head + .getAs[Seq[String]](0) + .head + println(dataframe2) + + assert(dataframe1.equals(dataframe2)) + } +} diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizerTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizerTestSpec.scala new file mode 100644 index 000000000000..f6407968b297 --- /dev/null +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/tokenizer/bpe/BartTokenizerTestSpec.scala @@ -0,0 +1,92 @@ +/* + * Copyright 2017-2023 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package com.johnsnowlabs.nlp.annotators.tokenizer.bpe + +import org.scalatest.flatspec.AnyFlatSpec + +class BartTokenizerTestSpec extends AnyFlatSpec with BpeTokenizerBehaviours { + val vocab: Map[String, Int] = + Array( + "", + "", + "", + "ĠI", + "Ġunamb", + "ig", + "ou", + "os", + "ly", + "Ġgood", + "Ġ3", + "ĠAs", + "d", + "Ġ!", + "", + "", + "I").zipWithIndex.toMap + + val merges: Map[(String, String), Int] = Array( + "o u", + "l y", + "Ġ g", + "a m", + "i g", + "Ġ u", + "o d", + "u n", + "o s", + "Ġg o", + "Ġu n", + "o od", + "A s", + "m b", + "g o", + "o o", + "n a", + "am b", + "s l", + "n am", + "b i", + "b ig", + "u o", + "s d", + "Ġun amb", + "Ġgo od", + "Ġ 3").map(_.split(" ")).map { case Array(c1, c2) => (c1, c2) }.zipWithIndex.toMap + + override val modelType = "bart" + + override val replaceCharBeforeAssertion: Some[String] = Some("Ġ") + + "RobertaTokenizer" should behave like correctBpeTokenizer( + text = "I unambigouosly good 3Asd!", + Array("I", "Ġunamb", "ig", "ou", "os", "ly", "Ġgood", "Ġ3", "As", "d", "!"), + Array(16, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) + + "RobertaTokenizer" should behave like correctBpeTokenizerWithAddedPrefixSpace( + text = "I unambigouosly good 3Asd!", + Array("I", "Ġunamb", "ig", "ou", "os", "ly", "Ġgood", "Ġ3", "As", "d", "!"), + Array(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) + + it should behave like correctBpeTokenizerInFringeSituations() + + it should behave like correctBpeTokenizerSpecialTokens( + text = "I unambigouosly good 3Asd ", + expected = + Array("I", "Ġunamb", "ig", "ou", "os", "ly", "", "Ġgood", "Ġ3", "As", "d", ""), + expectedIds = Array(16, 4, 5, 6, 7, 8, 2, 9, 10, 11, 12, 2)) +} From 7080915e7ae74bf4f913d30a2a9a0de5e9453b6d Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Thu, 6 Apr 2023 20:53:11 +0200 Subject: [PATCH 09/26] Bump version to 4.4.0 --- README.md | 114 +++++++++--------- build.sbt | 2 +- docs/demos/mental_health.md | 71 ----------- .../understand_financial_entities_context.md | 84 ------------- .../understand_legal_entities_context.md | 48 -------- docs/en/concepts.md | 2 +- docs/en/examples.md | 4 +- docs/en/hardware_acceleration.md | 2 +- docs/en/install.md | 54 ++++----- docs/en/spark_nlp.md | 2 +- examples/docker/README.md | 4 +- examples/util/Training_Helpers.ipynb | 4 +- python/README.md | 114 +++++++++--------- python/docs/conf.py | 2 +- python/setup.py | 2 +- python/sparknlp/__init__.py | 4 +- scripts/colab_setup.sh | 2 +- scripts/kaggle_setup.sh | 2 +- scripts/sagemaker_setup.sh | 2 +- .../scala/com/johnsnowlabs/nlp/SparkNLP.scala | 2 +- .../scala/com/johnsnowlabs/util/Build.scala | 2 +- 21 files changed, 162 insertions(+), 361 deletions(-) delete mode 100644 docs/demos/mental_health.md delete mode 100644 docs/demos/understand_financial_entities_context.md delete mode 100644 docs/demos/understand_legal_entities_context.md diff --git a/README.md b/README.md index 418e62ea9fa7..14da5a95cec1 100644 --- a/README.md +++ b/README.md @@ -162,7 +162,7 @@ To use Spark NLP you need the following requirements: **GPU (optional):** -Spark NLP 4.3.2 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: +Spark NLP 4.4.0 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: - NVIDIA® GPU drivers version 450.80.02 or higher - CUDA® Toolkit 11.2 @@ -178,7 +178,7 @@ $ java -version $ conda create -n sparknlp python=3.7 -y $ conda activate sparknlp # spark-nlp by default is based on pyspark 3.x -$ pip install spark-nlp==4.3.2 pyspark==3.3.1 +$ pip install spark-nlp==4.4.0 pyspark==3.3.1 ``` In Python console or Jupyter `Python3` kernel: @@ -223,11 +223,12 @@ For more examples, you can visit our dedicated [examples](https://github.com/Joh ## Apache Spark Support -Spark NLP *4.3.2* has been built on top of Apache Spark 3.2 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, and +Spark NLP *4.4.0* has been built on top of Apache Spark 3.2 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x: | Spark NLP | Apache Spark 2.3.x | Apache Spark 2.4.x | Apache Spark 3.0.x | Apache Spark 3.1.x | Apache Spark 3.2.x | Apache Spark 3.3.x | |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------| +| 4.4.x | NO | NO | YES | YES | YES | YES | | 4.3.x | NO | NO | YES | YES | YES | YES | | 4.2.x | NO | NO | YES | YES | YES | YES | | 4.1.x | NO | NO | YES | YES | YES | YES | @@ -246,22 +247,23 @@ Find out more about `Spark NLP` versions from our [release notes](https://github ## Scala and Python Support -| Spark NLP | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Scala 2.11 | Scala 2.12 | -|-----------|------------|------------|------------|------------|------------|------------| -| 4.3.x | YES | YES | YES | YES | NO | YES | -| 4.2.x | YES | YES | YES | YES | NO | YES | -| 4.1.x | YES | YES | YES | YES | NO | YES | -| 4.0.x | YES | YES | YES | YES | NO | YES | -| 3.4.x | YES | YES | YES | YES | YES | YES | -| 3.3.x | YES | YES | YES | NO | YES | YES | -| 3.2.x | YES | YES | YES | NO | YES | YES | -| 3.1.x | YES | YES | YES | NO | YES | YES | -| 3.0.x | YES | YES | YES | NO | YES | YES | -| 2.7.x | YES | YES | NO | NO | YES | NO | +| Spark NLP | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10| Scala 2.11 | Scala 2.12 | +|-----------|------------|------------|------------|------------|------------|------------|------------| +| 4.4.x | NO | YES | YES | YES | YES | NO | YES | +| 4.3.x | YES | YES | YES | YES | YES | NO | YES | +| 4.2.x | YES | YES | YES | YES | YES | NO | YES | +| 4.1.x | YES | YES | YES | YES | NO | NO | YES | +| 4.0.x | YES | YES | YES | YES | NO | NO | YES | +| 3.4.x | YES | YES | YES | YES | NO | YES | YES | +| 3.3.x | YES | YES | YES | NO | NO | YES | YES | +| 3.2.x | YES | YES | YES | NO | NO | YES | YES | +| 3.1.x | YES | YES | YES | NO | NO | YES | YES | +| 3.0.x | YES | YES | YES | NO | NO | YES | YES | +| 2.7.x | YES | YES | NO | NO | NO | YES | NO | ## Databricks Support -Spark NLP 4.3.2 has been tested and is compatible with the following runtimes: +Spark NLP 4.4.0 has been tested and is compatible with the following runtimes: **CPU:** @@ -315,7 +317,7 @@ runtimes supporting CUDA 11 are 9.x and above as listed under GPU. ## EMR Support -Spark NLP 4.3.2 has been tested and is compatible with the following EMR releases: +Spark NLP 4.4.0 has been tested and is compatible with the following EMR releases: - emr-6.2.0 - emr-6.3.0 @@ -359,11 +361,11 @@ Spark NLP supports all major releases of Apache Spark 3.0.x, Apache Spark 3.1.x, ```sh # CPU -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` The `spark-nlp` has been published to @@ -372,11 +374,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s ```sh # GPU -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0 ``` @@ -386,11 +388,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s ```sh # AArch64 -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0 ``` @@ -400,11 +402,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s ```sh # M1/M2 (Apple Silicon) -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0 ``` @@ -418,7 +420,7 @@ set in your SparkSession: spark-shell \ --driver-memory 16g \ --conf spark.kryoserializer.buffer.max=2000M \ - --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` ## Scala @@ -436,7 +438,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp_2.12 - 4.3.2 + 4.4.0 ``` @@ -447,7 +449,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp-gpu_2.12 - 4.3.2 + 4.4.0 ``` @@ -458,7 +460,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp-aarch64_2.12 - 4.3.2 + 4.4.0 ``` @@ -469,7 +471,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp-silicon_2.12 - 4.3.2 + 4.4.0 ``` @@ -479,28 +481,28 @@ coordinates: ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.4.0" ``` **spark-nlp-gpu:** ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.4.0" ``` **spark-nlp-aarch64:** ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64 -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.4.0" ``` **spark-nlp-silicon:** ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-silicon -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.4.0" ``` Maven @@ -522,7 +524,7 @@ If you installed pyspark through pip/conda, you can install `spark-nlp` through Pip: ```bash -pip install spark-nlp==4.3.2 +pip install spark-nlp==4.4.0 ``` Conda: @@ -551,7 +553,7 @@ spark = SparkSession.builder .config("spark.driver.memory", "16G") .config("spark.driver.maxResultSize", "0") .config("spark.kryoserializer.buffer.max", "2000M") - .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2") + .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0") .getOrCreate() ``` @@ -622,7 +624,7 @@ Use either one of the following options - Add the following Maven Coordinates to the interpreter's library list ```bash -com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` - Add a path to pre-built jar from [here](#compiled-jars) in the interpreter's library list making sure the jar is @@ -633,7 +635,7 @@ com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 Apart from the previous step, install the python module through pip ```bash -pip install spark-nlp==4.3.2 +pip install spark-nlp==4.4.0 ``` Or you can install `spark-nlp` from inside Zeppelin by using Conda: @@ -661,7 +663,7 @@ launch the Jupyter from the same Python environment: $ conda create -n sparknlp python=3.8 -y $ conda activate sparknlp # spark-nlp by default is based on pyspark 3.x -$ pip install spark-nlp==4.3.2 pyspark==3.3.1 jupyter +$ pip install spark-nlp==4.4.0 pyspark==3.3.1 jupyter $ jupyter notebook ``` @@ -678,7 +680,7 @@ export PYSPARK_PYTHON=python3 export PYSPARK_DRIVER_PYTHON=jupyter export PYSPARK_DRIVER_PYTHON_OPTS=notebook -pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` Alternatively, you can mix in using `--jars` option for pyspark + `pip install spark-nlp` @@ -705,7 +707,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi # -s is for spark-nlp # -g will enable upgrading libcudnn8 to 8.1.0 on Google Colab for GPU usage # by default they are set to the latest -!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.3.2 +!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.4.0 ``` [Spark NLP quick start on Google Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/quick_start_google_colab.ipynb) @@ -728,7 +730,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi # -s is for spark-nlp # -g will enable upgrading libcudnn8 to 8.1.0 on Kaggle for GPU usage # by default they are set to the latest -!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.3.2 +!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.4.0 ``` [Spark NLP quick start on Kaggle Kernel](https://www.kaggle.com/mozzie/spark-nlp-named-entity-recognition) is a live @@ -747,9 +749,9 @@ demo on Kaggle Kernel that performs named entity recognitions by using Spark NLP 3. In `Libraries` tab inside your cluster you need to follow these steps: - 3.1. Install New -> PyPI -> `spark-nlp==4.3.2` -> Install + 3.1. Install New -> PyPI -> `spark-nlp==4.4.0` -> Install - 3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2` -> Install + 3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0` -> Install 4. Now you can attach your notebook to the cluster and use Spark NLP! @@ -800,7 +802,7 @@ A sample of your software configuration in JSON on S3 (must be public access): "spark.kryoserializer.buffer.max": "2000M", "spark.serializer": "org.apache.spark.serializer.KryoSerializer", "spark.driver.maxResultSize": "0", - "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2" + "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0" } }] ``` @@ -809,7 +811,7 @@ A sample of AWS CLI to launch EMR cluster: ```.sh aws emr create-cluster \ ---name "Spark NLP 4.3.2" \ +--name "Spark NLP 4.4.0" \ --release-label emr-6.2.0 \ --applications Name=Hadoop Name=Spark Name=Hive \ --instance-type m4.4xlarge \ @@ -873,7 +875,7 @@ gcloud dataproc clusters create ${CLUSTER_NAME} \ --enable-component-gateway \ --metadata 'PIP_PACKAGES=spark-nlp spark-nlp-display google-cloud-bigquery google-cloud-storage' \ --initialization-actions gs://goog-dataproc-initialization-actions-${REGION}/python/pip-install.sh \ - --properties spark:spark.serializer=org.apache.spark.serializer.KryoSerializer,spark:spark.driver.maxResultSize=0,spark:spark.kryoserializer.buffer.max=2000M,spark:spark.jars.packages=com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --properties spark:spark.serializer=org.apache.spark.serializer.KryoSerializer,spark:spark.driver.maxResultSize=0,spark:spark.kryoserializer.buffer.max=2000M,spark:spark.jars.packages=com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` 2. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI. @@ -912,7 +914,7 @@ spark = SparkSession.builder .config("spark.kryoserializer.buffer.max", "2000m") .config("spark.jsl.settings.pretrained.cache_folder", "sample_data/pretrained") .config("spark.jsl.settings.storage.cluster_tmp_dir", "sample_data/storage") - .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2") + .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0") .getOrCreate() ``` @@ -926,7 +928,7 @@ spark-shell \ --conf spark.kryoserializer.buffer.max=2000M \ --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \ --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \ - --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` **pyspark:** @@ -939,7 +941,7 @@ pyspark \ --conf spark.kryoserializer.buffer.max=2000M \ --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \ --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \ - --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` **Databricks:** @@ -1211,7 +1213,7 @@ spark = SparkSession.builder .config("spark.driver.memory", "16G") .config("spark.driver.maxResultSize", "0") .config("spark.kryoserializer.buffer.max", "2000M") - .config("spark.jars", "/tmp/spark-nlp-assembly-4.3.2.jar") + .config("spark.jars", "/tmp/spark-nlp-assembly-4.4.0.jar") .getOrCreate() ``` @@ -1220,7 +1222,7 @@ spark = SparkSession.builder version (3.0.x, 3.1.x, 3.2.x, and 3.3.x) - If you are local, you can load the Fat JAR from your local FileSystem, however, if you are in a cluster setup you need to put the Fat JAR on a distributed FileSystem such as HDFS, DBFS, S3, etc. ( - i.e., `hdfs:///tmp/spark-nlp-assembly-4.3.2.jar`) + i.e., `hdfs:///tmp/spark-nlp-assembly-4.4.0.jar`) Example of using pretrained Models and Pipelines in offline: diff --git a/build.sbt b/build.sbt index 4a62fb8c806b..3e164402d0b8 100644 --- a/build.sbt +++ b/build.sbt @@ -6,7 +6,7 @@ name := getPackageName(is_silicon, is_gpu, is_aarch64) organization := "com.johnsnowlabs.nlp" -version := "4.3.2" +version := "4.4.0" (ThisBuild / scalaVersion) := scalaVer diff --git a/docs/demos/mental_health.md b/docs/demos/mental_health.md deleted file mode 100644 index 68f2d9af04d7..000000000000 --- a/docs/demos/mental_health.md +++ /dev/null @@ -1,71 +0,0 @@ ---- -layout: demopagenew -title: Mental Health - Clinical NLP Demos & Notebooks -seotitle: 'Clinical NLP: Mental Health - John Snow Labs' -full_width: true -permalink: /mental_health -key: demo -nav_key: demo -article_header: - type: demo -license: false -mode: immersivebg -show_edit_on_github: false -show_date: false -data: - sections: - - secheader: yes - secheader: - - subtitle: Mental Health - Live Demos & Notebooks - activemenu: mental_health - source: yes - source: - - title: Identify Depression for Patient Posts - id: depression_classifier_tweets - image: - src: /assets/images/Depression_Classifier_for_Tweets.svg - excerpt: This demo shows a classifier that can classify whether tweets contain depressive text. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/healthcare/MENTAL_HEALTH_DEPRESSION/ - - text: Colab - type: blue_btn - url: https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/MENTAL_HEALTH.ipynb - - title: Identify Intimate Partner Violence from Patient Posts - id: classify_intimate_partner_violence_tweet - image: - src: /assets/images/Classify_Intimate_Partner_Violence_Tweet.svg - excerpt: This model involves the detection the potential IPV victims on social media platforms (in English tweets). - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/healthcare/PUBLIC_HEALTH_PARTNER_VIOLENCE/ - - text: Colab - type: blue_btn - url: https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/PUBLIC_HEALTH_MB4SC.ipynb - - title: Identify Stress from Patient Posts - id: classify_stress_tweet - image: - src: /assets/images/Classify_Stress_Tweet.svg - excerpt: This model can identify stress in social media (Twitter) posts in the self-disclosure category. The model finds whether a person claims he/she is stressed or not. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/healthcare/PUBLIC_HEALTH_STRESS/ - - text: Colab - type: blue_btn - url: https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/PUBLIC_HEALTH_MB4SC.ipynb - - title: Identify the Source of Stress from Patient Posts - id: identify_source_stress_patient_posts - image: - src: /assets/images/Identify_Source_Stress_Patient.svg - excerpt: This demo shows how to classify source of emotional stress in text. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/healthcare/PUBLIC_HEALTH_SOURCE_OF_STRESS/ - - text: Colab - type: blue_btn - url: https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/PUBLIC_HEALTH_MB4SC.ipynb ---- diff --git a/docs/demos/understand_financial_entities_context.md b/docs/demos/understand_financial_entities_context.md deleted file mode 100644 index ae6704a2931f..000000000000 --- a/docs/demos/understand_financial_entities_context.md +++ /dev/null @@ -1,84 +0,0 @@ ---- -layout: demopagenew -title: Understand Entities in Context - Finance NLP Demos & Notebooks -seotitle: 'Finance NLP: Understand Entities in Context - John Snow Labs' -subtitle: Run 300+ live demos and notebooks -full_width: true -permalink: /understand_financial_entities_context -key: demo -nav_key: demo -article_header: - type: demo -license: false -mode: immersivebg -show_edit_on_github: false -show_date: false -data: - sections: - - secheader: yes - secheader: - - subtitle: Understand Entities in Context - Live Demos & Notebooks - activemenu: understand_financial_entities_context - source: yes - source: - - title: Identify Competitors in a text - id: identify_competitors_text - image: - src: /assets/images/Identify_Competitors_in_a_text.svg - excerpt: This model uses Assertion Status to identify if a PRODUCT or an ORG is mentioned to be a competitor. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/finance/ASSERTIONDL_COMPETITORS - - text: Colab - type: blue_btn - url: - - title: Identify Past Work Experience - id: identify_past_work_experience - image: - src: /assets/images/Identify_Competitors_in_a_text.svg - excerpt: This model uses Assertion Status to identify if a mention to an Organization, Job Title or Date is about the past. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/finance/ASSERTIONDL_PAST_ROLES/ - - text: Colab - type: blue_btn - url: - - title: Detect Temporality and Certainty in Financial texts - id: detect_temporality_certainty_financial_texts - image: - src: /assets/images/Detect_Temporality_and_Certainty.svg - excerpt: This demo shows how to use Assertion Status to identify if financial information is described to happen in present, past, future or it’s just possible. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/finance/FINASSERTION_TEMPORALITY/ - - text: Colab - type: blue_btn - url: - - title: Financial Assertion Status (Negation) - id: financial_assertion_status_negation - image: - src: /assets/images/Financial_Assertion_Status_Negation.svg - excerpt: This is a Financial Negation model, aimed to identify if an NER entity is mentioned in the context to be negated or not. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/finance/NEGATION_DETECTION_IN_FINANCIAL_TEXTS/ - - text: Colab - type: blue_btn - url: - - title: Understand Increased or Decreased Amounts and Percentages in Context - id: understand_increased_decreased_amounts_percentages_context - image: - src: /assets/images/Understand_Increased_or_Decreased_Amounts_and_Percentages_in_Context.svg - excerpt: This demo shows how to use the Assertion Status model to identify if a mentioned amount or percentage is stated to be increased or decreased in context. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/finance/FINASSERTION_INCREASE_DECREASE/ - - text: Colab - type: blue_btn - url: ---- \ No newline at end of file diff --git a/docs/demos/understand_legal_entities_context.md b/docs/demos/understand_legal_entities_context.md deleted file mode 100644 index 1553748a4207..000000000000 --- a/docs/demos/understand_legal_entities_context.md +++ /dev/null @@ -1,48 +0,0 @@ ---- -layout: demopagenew -title: Understand Entities in Context - Spark NLP Demos & Notebooks -seotitle: 'Spark NLP: Understand Entities in Context - John Snow Labs' -subtitle: Run 300+ live demos and notebooks -full_width: true -permalink: /understand_legal_entities_context -key: demo -nav_key: demo -article_header: - type: demo -license: false -mode: immersivebg -show_edit_on_github: false -show_date: false -data: - sections: - - secheader: yes - secheader: - - subtitle: Understand Entities in Context - Live Demos & Notebooks - activemenu: understand_legal_entities_context - source: yes - source: - - title: Detect Temporality and Certainty in Legal texts - id: detect_temporality_certainty_legal_texts - image: - src: /assets/images/Detect_Temporality.svg - excerpt: This demo shows how to use Assertion Status to identify if legal information is described to happen in the present, past, future or if it’s just possible. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/legal/LEGASSERTION_TEMPORALITY/ - - text: Colab - type: blue_btn - url: - - title: Legal Assertion Status (Negation) - id: legal_assertion_status_negation - image: - src: /assets/images/Legal_Assertion_Status_Negation.svg - excerpt: This is a Legal Negation model, aimed to identify if an NER entity is mentioned in the context to be negated or not. - actions: - - text: Live Demo - type: normal - url: https://demo.johnsnowlabs.com/finance/NEGATION_DETECTION_IN_FINANCIAL_TEXTS/ - - text: Colab - type: blue_btn - url: ---- diff --git a/docs/en/concepts.md b/docs/en/concepts.md index e8eb559c62f4..35a12232444c 100644 --- a/docs/en/concepts.md +++ b/docs/en/concepts.md @@ -62,7 +62,7 @@ $ java -version $ conda create -n sparknlp python=3.7 -y $ conda activate sparknlp # spark-nlp by default is based on pyspark 3.x -$ pip install spark-nlp==4.3.2 pyspark==3.3.1 jupyter +$ pip install spark-nlp==4.4.0 pyspark==3.3.1 jupyter $ jupyter notebook ``` diff --git a/docs/en/examples.md b/docs/en/examples.md index 881729ac9b69..3923b36c66bc 100644 --- a/docs/en/examples.md +++ b/docs/en/examples.md @@ -16,7 +16,7 @@ $ java -version # should be Java 8 (Oracle or OpenJDK) $ conda create -n sparknlp python=3.7 -y $ conda activate sparknlp -$ pip install spark-nlp==4.3.2 pyspark==3.3.1 +$ pip install spark-nlp==4.4.0 pyspark==3.3.1 ``` ## Google Colab Notebook @@ -36,7 +36,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi # -p is for pyspark # -s is for spark-nlp # by default they are set to the latest -!bash colab.sh -p 3.2.3 -s 4.3.2 +!bash colab.sh -p 3.2.3 -s 4.4.0 ``` [Spark NLP quick start on Google Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/quick_start_google_colab.ipynb) is a live demo on Google Colab that performs named entity recognitions and sentiment analysis by using Spark NLP pretrained pipelines. diff --git a/docs/en/hardware_acceleration.md b/docs/en/hardware_acceleration.md index 3bdf36b0faae..4a93b374e2b5 100644 --- a/docs/en/hardware_acceleration.md +++ b/docs/en/hardware_acceleration.md @@ -49,7 +49,7 @@ Since the new Transformer models such as BERT for Word and Sentence embeddings a | DeBERTa Large | +477%(5.8x) | | Longformer Base | +52%(1.5x) | -Spark NLP 4.3.2 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: +Spark NLP 4.4.0 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: - NVIDIA® GPU drivers version 450.80.02 or higher - CUDA® Toolkit 11.2 diff --git a/docs/en/install.md b/docs/en/install.md index 629db45c8e73..4de942cecae4 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -15,22 +15,22 @@ sidebar: ```bash # Install Spark NLP from PyPI -pip install spark-nlp==4.3.2 +pip install spark-nlp==4.4.0 # Install Spark NLP from Anacodna/Conda conda install -c johnsnowlabs spark-nlp # Load Spark NLP with Spark Shell -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 # Load Spark NLP with PySpark -pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 # Load Spark NLP with Spark Submit -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 # Load Spark NLP as external JAR after compiling and building Spark NLP by `sbt assembly` -spark-shell --jars spark-nlp-assembly-4.3.2.jar +spark-shell --jars spark-nlp-assembly-4.4.0.jar ``` ## Python @@ -49,7 +49,7 @@ $ java -version # should be Java 8 (Oracle or OpenJDK) $ conda create -n sparknlp python=3.8 -y $ conda activate sparknlp -$ pip install spark-nlp==4.3.2 pyspark==3.3.1 +$ pip install spark-nlp==4.4.0 pyspark==3.3.1 ``` Of course you will need to have jupyter installed in your system: @@ -76,7 +76,7 @@ spark = SparkSession.builder \ .config("spark.driver.memory","16G")\ .config("spark.driver.maxResultSize", "0") \ .config("spark.kryoserializer.buffer.max", "2000M")\ - .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2")\ + .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0")\ .getOrCreate() ``` @@ -91,7 +91,7 @@ spark = SparkSession.builder \ com.johnsnowlabs.nlp spark-nlp_2.12 - 4.3.2 + 4.4.0 ``` @@ -102,7 +102,7 @@ spark = SparkSession.builder \ com.johnsnowlabs.nlp spark-nlp-gpu_2.12 - 4.3.2 + 4.4.0 ``` @@ -113,7 +113,7 @@ spark = SparkSession.builder \ com.johnsnowlabs.nlp spark-nlp-m1_2.12 - 4.3.2 + 4.4.0 ``` @@ -124,7 +124,7 @@ spark = SparkSession.builder \ com.johnsnowlabs.nlp spark-nlp-aarch64_2.12 - 4.3.2 + 4.4.0 ``` @@ -134,28 +134,28 @@ spark = SparkSession.builder \ ```scala // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.4.0" ``` **spark-nlp-gpu:** ```scala // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.4.0" ``` **spark-nlp-m1:** ```scala // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-m1 -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-m1" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-m1" % "4.4.0" ``` **spark-nlp-aarch64:** ```scala // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64 -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.4.0" ``` Maven Central: [https://mvnrepository.com/artifact/com.johnsnowlabs.nlp](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp) @@ -233,7 +233,7 @@ maven coordinates like these: com.johnsnowlabs.nlp spark-nlp-m1_2.12 - 4.3.2 + 4.4.0 ``` @@ -241,7 +241,7 @@ or in case of sbt: ```scala // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-m1" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-m1" % "4.4.0" ``` If everything went well, you can now start Spark NLP with the `m1` flag set to `true`: @@ -274,7 +274,7 @@ spark = sparknlp.start(m1=True) ## Installation for Linux Aarch64 Systems -Starting from version 4.3.2, Spark NLP supports Linux systems running on an aarch64 +Starting from version 4.4.0, Spark NLP supports Linux systems running on an aarch64 processor architecture. The necessary dependencies have been built on Ubuntu 16.04, so a recent system with an environment of at least that will be needed. @@ -318,7 +318,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi # -p is for pyspark # -s is for spark-nlp # by default they are set to the latest -!wget http://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.3.2 +!wget http://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.4.0 ``` [Spark NLP quick start on Google Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/quick_start_google_colab.ipynb) is a live demo on Google Colab that performs named entity recognitions and sentiment analysis by using Spark NLP pretrained pipelines. @@ -337,7 +337,7 @@ Run the following code in Kaggle Kernel and start using spark-nlp right away. ## Databricks Support -Spark NLP 4.3.2 has been tested and is compatible with the following runtimes: +Spark NLP 4.4.0 has been tested and is compatible with the following runtimes: **CPU:** @@ -403,7 +403,7 @@ NOTE: Spark NLP 4.0.x is based on TensorFlow 2.7.x which is compatible with CUDA 3.1. Install New -> PyPI -> `spark-nlp` -> Install - 3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2` -> Install + 3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0` -> Install 4. Now you can attach your notebook to the cluster and use Spark NLP! @@ -419,7 +419,7 @@ Note: You can import these notebooks by using their URLs. ## EMR Support -Spark NLP 4.3.2 has been tested and is compatible with the following EMR releases: +Spark NLP 4.4.0 has been tested and is compatible with the following EMR releases: - emr-6.2.0 - emr-6.3.0 @@ -477,7 +477,7 @@ A sample of your software configuration in JSON on S3 (must be public access): "spark.kryoserializer.buffer.max": "2000M", "spark.serializer": "org.apache.spark.serializer.KryoSerializer", "spark.driver.maxResultSize": "0", - "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2" + "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0" } } ] @@ -487,7 +487,7 @@ A sample of AWS CLI to launch EMR cluster: ```sh aws emr create-cluster \ ---name "Spark NLP 4.3.2" \ +--name "Spark NLP 4.4.0" \ --release-label emr-6.2.0 \ --applications Name=Hadoop Name=Spark Name=Hive \ --instance-type m4.4xlarge \ @@ -741,7 +741,7 @@ We recommend using `conda` to manage your Python environment on Windows. Now you can use the downloaded binary by navigating to `%SPARK_HOME%\bin` and running -Either create a conda env for python 3.6, install *pyspark==3.3.1 spark-nlp numpy* and use Jupyter/python console, or in the same conda env you can go to spark bin for *pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2*. +Either create a conda env for python 3.6, install *pyspark==3.3.1 spark-nlp numpy* and use Jupyter/python console, or in the same conda env you can go to spark bin for *pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0*. @@ -767,12 +767,12 @@ spark = SparkSession.builder \ .config("spark.driver.memory","16G")\ .config("spark.driver.maxResultSize", "0") \ .config("spark.kryoserializer.buffer.max", "2000M")\ - .config("spark.jars", "/tmp/spark-nlp-assembly-4.3.2.jar")\ + .config("spark.jars", "/tmp/spark-nlp-assembly-4.4.0.jar")\ .getOrCreate() ``` - You can download provided Fat JARs from each [release notes](https://github.com/JohnSnowLabs/spark-nlp/releases), please pay attention to pick the one that suits your environment depending on the device (CPU/GPU) and Apache Spark version (3.x) -- If you are local, you can load the Fat JAR from your local FileSystem, however, if you are in a cluster setup you need to put the Fat JAR on a distributed FileSystem such as HDFS, DBFS, S3, etc. (i.e., `hdfs:///tmp/spark-nlp-assembly-4.3.2.jar`) +- If you are local, you can load the Fat JAR from your local FileSystem, however, if you are in a cluster setup you need to put the Fat JAR on a distributed FileSystem such as HDFS, DBFS, S3, etc. (i.e., `hdfs:///tmp/spark-nlp-assembly-4.4.0.jar`) Example of using pretrained Models and Pipelines in offline: diff --git a/docs/en/spark_nlp.md b/docs/en/spark_nlp.md index e60d4f75ea13..b308fc05b843 100644 --- a/docs/en/spark_nlp.md +++ b/docs/en/spark_nlp.md @@ -25,7 +25,7 @@ Spark NLP is built on top of **Apache Spark 3.x**. For using Spark NLP you need: **GPU (optional):** -Spark NLP 4.3.2 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: +Spark NLP 4.4.0 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: - NVIDIA® GPU drivers version 450.80.02 or higher - CUDA® Toolkit 11.2 diff --git a/examples/docker/README.md b/examples/docker/README.md index a94cd58239d5..758a10266956 100644 --- a/examples/docker/README.md +++ b/examples/docker/README.md @@ -73,7 +73,7 @@ docker run -it --name sparknlp-container \ --conf "spark.serializer"="org.apache.spark.serializer.KryoSerializer" \ --conf "spark.kryoserializer.buffer.max"="2000M" \ --conf "spark.driver.maxResultSize"="0" \ - --packages "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2" + --packages "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0" ``` To run the shell with GPU support, we use the image from [Jupyter Notebook with GPU @@ -91,5 +91,5 @@ docker run -it --name sparknlp-container \ --conf "spark.serializer"="org.apache.spark.serializer.KryoSerializer" \ --conf "spark.kryoserializer.buffer.max"="2000M" \ --conf "spark.driver.maxResultSize"="0" \ - --packages "com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.3.2" + --packages "com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0" ``` diff --git a/examples/util/Training_Helpers.ipynb b/examples/util/Training_Helpers.ipynb index d2eb474ed15e..03c9bc06d0a4 100644 --- a/examples/util/Training_Helpers.ipynb +++ b/examples/util/Training_Helpers.ipynb @@ -129,7 +129,7 @@ "id": "Jn3axMFZTaxV" }, "source": [ - "Starting at spark-nlp 4.3.2, you can also set an S3 URI. To configure this, it is necessary to set up the Spark session with the appropriate settings for both Spark NLP and Spark ML." + "Starting at spark-nlp 4.4.0, you can also set an S3 URI. To configure this, it is necessary to set up the Spark session with the appropriate settings for both Spark NLP and Spark ML." ] }, { @@ -246,7 +246,7 @@ " .config(\"spark.jsl.settings.aws.credentials.secret_access_key\", MY_SECRET_KEY) \\\n", " .config(\"spark.jsl.settings.aws.credentials.session_token\", MY_SESSION_KEY) \\\n", " .config(\"spark.jsl.settings.aws.region\", \"us-east-1\") \\\n", - " .config(\"spark.jars.packages\", \"com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2,org.apache.hadoop:hadoop-aws:3.3.1,com.amazonaws:aws-java-sdk:1.11.901\") \\\n", + " .config(\"spark.jars.packages\", \"com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,org.apache.hadoop:hadoop-aws:3.3.1,com.amazonaws:aws-java-sdk:1.11.901\") \\\n", " .config(\"spark.hadoop.fs.s3a.impl\", \"org.apache.hadoop.fs.s3a.S3AFileSystem\") \\\n", " .config(\"spark.hadoop.fs.s3a.path.style.access\", \"true\") \\\n", " .getOrCreate()" diff --git a/python/README.md b/python/README.md index 418e62ea9fa7..14da5a95cec1 100644 --- a/python/README.md +++ b/python/README.md @@ -162,7 +162,7 @@ To use Spark NLP you need the following requirements: **GPU (optional):** -Spark NLP 4.3.2 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: +Spark NLP 4.4.0 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support: - NVIDIA® GPU drivers version 450.80.02 or higher - CUDA® Toolkit 11.2 @@ -178,7 +178,7 @@ $ java -version $ conda create -n sparknlp python=3.7 -y $ conda activate sparknlp # spark-nlp by default is based on pyspark 3.x -$ pip install spark-nlp==4.3.2 pyspark==3.3.1 +$ pip install spark-nlp==4.4.0 pyspark==3.3.1 ``` In Python console or Jupyter `Python3` kernel: @@ -223,11 +223,12 @@ For more examples, you can visit our dedicated [examples](https://github.com/Joh ## Apache Spark Support -Spark NLP *4.3.2* has been built on top of Apache Spark 3.2 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, and +Spark NLP *4.4.0* has been built on top of Apache Spark 3.2 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x: | Spark NLP | Apache Spark 2.3.x | Apache Spark 2.4.x | Apache Spark 3.0.x | Apache Spark 3.1.x | Apache Spark 3.2.x | Apache Spark 3.3.x | |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------| +| 4.4.x | NO | NO | YES | YES | YES | YES | | 4.3.x | NO | NO | YES | YES | YES | YES | | 4.2.x | NO | NO | YES | YES | YES | YES | | 4.1.x | NO | NO | YES | YES | YES | YES | @@ -246,22 +247,23 @@ Find out more about `Spark NLP` versions from our [release notes](https://github ## Scala and Python Support -| Spark NLP | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Scala 2.11 | Scala 2.12 | -|-----------|------------|------------|------------|------------|------------|------------| -| 4.3.x | YES | YES | YES | YES | NO | YES | -| 4.2.x | YES | YES | YES | YES | NO | YES | -| 4.1.x | YES | YES | YES | YES | NO | YES | -| 4.0.x | YES | YES | YES | YES | NO | YES | -| 3.4.x | YES | YES | YES | YES | YES | YES | -| 3.3.x | YES | YES | YES | NO | YES | YES | -| 3.2.x | YES | YES | YES | NO | YES | YES | -| 3.1.x | YES | YES | YES | NO | YES | YES | -| 3.0.x | YES | YES | YES | NO | YES | YES | -| 2.7.x | YES | YES | NO | NO | YES | NO | +| Spark NLP | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10| Scala 2.11 | Scala 2.12 | +|-----------|------------|------------|------------|------------|------------|------------|------------| +| 4.4.x | NO | YES | YES | YES | YES | NO | YES | +| 4.3.x | YES | YES | YES | YES | YES | NO | YES | +| 4.2.x | YES | YES | YES | YES | YES | NO | YES | +| 4.1.x | YES | YES | YES | YES | NO | NO | YES | +| 4.0.x | YES | YES | YES | YES | NO | NO | YES | +| 3.4.x | YES | YES | YES | YES | NO | YES | YES | +| 3.3.x | YES | YES | YES | NO | NO | YES | YES | +| 3.2.x | YES | YES | YES | NO | NO | YES | YES | +| 3.1.x | YES | YES | YES | NO | NO | YES | YES | +| 3.0.x | YES | YES | YES | NO | NO | YES | YES | +| 2.7.x | YES | YES | NO | NO | NO | YES | NO | ## Databricks Support -Spark NLP 4.3.2 has been tested and is compatible with the following runtimes: +Spark NLP 4.4.0 has been tested and is compatible with the following runtimes: **CPU:** @@ -315,7 +317,7 @@ runtimes supporting CUDA 11 are 9.x and above as listed under GPU. ## EMR Support -Spark NLP 4.3.2 has been tested and is compatible with the following EMR releases: +Spark NLP 4.4.0 has been tested and is compatible with the following EMR releases: - emr-6.2.0 - emr-6.3.0 @@ -359,11 +361,11 @@ Spark NLP supports all major releases of Apache Spark 3.0.x, Apache Spark 3.1.x, ```sh # CPU -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` The `spark-nlp` has been published to @@ -372,11 +374,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s ```sh # GPU -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0 ``` @@ -386,11 +388,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s ```sh # AArch64 -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0 ``` @@ -400,11 +402,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s ```sh # M1/M2 (Apple Silicon) -spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.3.2 +spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0 -pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0 -spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.3.2 +spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0 ``` @@ -418,7 +420,7 @@ set in your SparkSession: spark-shell \ --driver-memory 16g \ --conf spark.kryoserializer.buffer.max=2000M \ - --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` ## Scala @@ -436,7 +438,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp_2.12 - 4.3.2 + 4.4.0 ``` @@ -447,7 +449,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp-gpu_2.12 - 4.3.2 + 4.4.0 ``` @@ -458,7 +460,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp-aarch64_2.12 - 4.3.2 + 4.4.0 ``` @@ -469,7 +471,7 @@ coordinates: com.johnsnowlabs.nlp spark-nlp-silicon_2.12 - 4.3.2 + 4.4.0 ``` @@ -479,28 +481,28 @@ coordinates: ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.4.0" ``` **spark-nlp-gpu:** ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.4.0" ``` **spark-nlp-aarch64:** ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64 -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.4.0" ``` **spark-nlp-silicon:** ```sbtshell // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-silicon -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.3.2" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.4.0" ``` Maven @@ -522,7 +524,7 @@ If you installed pyspark through pip/conda, you can install `spark-nlp` through Pip: ```bash -pip install spark-nlp==4.3.2 +pip install spark-nlp==4.4.0 ``` Conda: @@ -551,7 +553,7 @@ spark = SparkSession.builder .config("spark.driver.memory", "16G") .config("spark.driver.maxResultSize", "0") .config("spark.kryoserializer.buffer.max", "2000M") - .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2") + .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0") .getOrCreate() ``` @@ -622,7 +624,7 @@ Use either one of the following options - Add the following Maven Coordinates to the interpreter's library list ```bash -com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` - Add a path to pre-built jar from [here](#compiled-jars) in the interpreter's library list making sure the jar is @@ -633,7 +635,7 @@ com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 Apart from the previous step, install the python module through pip ```bash -pip install spark-nlp==4.3.2 +pip install spark-nlp==4.4.0 ``` Or you can install `spark-nlp` from inside Zeppelin by using Conda: @@ -661,7 +663,7 @@ launch the Jupyter from the same Python environment: $ conda create -n sparknlp python=3.8 -y $ conda activate sparknlp # spark-nlp by default is based on pyspark 3.x -$ pip install spark-nlp==4.3.2 pyspark==3.3.1 jupyter +$ pip install spark-nlp==4.4.0 pyspark==3.3.1 jupyter $ jupyter notebook ``` @@ -678,7 +680,7 @@ export PYSPARK_PYTHON=python3 export PYSPARK_DRIVER_PYTHON=jupyter export PYSPARK_DRIVER_PYTHON_OPTS=notebook -pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 +pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` Alternatively, you can mix in using `--jars` option for pyspark + `pip install spark-nlp` @@ -705,7 +707,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi # -s is for spark-nlp # -g will enable upgrading libcudnn8 to 8.1.0 on Google Colab for GPU usage # by default they are set to the latest -!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.3.2 +!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.4.0 ``` [Spark NLP quick start on Google Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/quick_start_google_colab.ipynb) @@ -728,7 +730,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi # -s is for spark-nlp # -g will enable upgrading libcudnn8 to 8.1.0 on Kaggle for GPU usage # by default they are set to the latest -!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.3.2 +!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.4.0 ``` [Spark NLP quick start on Kaggle Kernel](https://www.kaggle.com/mozzie/spark-nlp-named-entity-recognition) is a live @@ -747,9 +749,9 @@ demo on Kaggle Kernel that performs named entity recognitions by using Spark NLP 3. In `Libraries` tab inside your cluster you need to follow these steps: - 3.1. Install New -> PyPI -> `spark-nlp==4.3.2` -> Install + 3.1. Install New -> PyPI -> `spark-nlp==4.4.0` -> Install - 3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2` -> Install + 3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0` -> Install 4. Now you can attach your notebook to the cluster and use Spark NLP! @@ -800,7 +802,7 @@ A sample of your software configuration in JSON on S3 (must be public access): "spark.kryoserializer.buffer.max": "2000M", "spark.serializer": "org.apache.spark.serializer.KryoSerializer", "spark.driver.maxResultSize": "0", - "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2" + "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0" } }] ``` @@ -809,7 +811,7 @@ A sample of AWS CLI to launch EMR cluster: ```.sh aws emr create-cluster \ ---name "Spark NLP 4.3.2" \ +--name "Spark NLP 4.4.0" \ --release-label emr-6.2.0 \ --applications Name=Hadoop Name=Spark Name=Hive \ --instance-type m4.4xlarge \ @@ -873,7 +875,7 @@ gcloud dataproc clusters create ${CLUSTER_NAME} \ --enable-component-gateway \ --metadata 'PIP_PACKAGES=spark-nlp spark-nlp-display google-cloud-bigquery google-cloud-storage' \ --initialization-actions gs://goog-dataproc-initialization-actions-${REGION}/python/pip-install.sh \ - --properties spark:spark.serializer=org.apache.spark.serializer.KryoSerializer,spark:spark.driver.maxResultSize=0,spark:spark.kryoserializer.buffer.max=2000M,spark:spark.jars.packages=com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --properties spark:spark.serializer=org.apache.spark.serializer.KryoSerializer,spark:spark.driver.maxResultSize=0,spark:spark.kryoserializer.buffer.max=2000M,spark:spark.jars.packages=com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` 2. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI. @@ -912,7 +914,7 @@ spark = SparkSession.builder .config("spark.kryoserializer.buffer.max", "2000m") .config("spark.jsl.settings.pretrained.cache_folder", "sample_data/pretrained") .config("spark.jsl.settings.storage.cluster_tmp_dir", "sample_data/storage") - .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2") + .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0") .getOrCreate() ``` @@ -926,7 +928,7 @@ spark-shell \ --conf spark.kryoserializer.buffer.max=2000M \ --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \ --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \ - --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` **pyspark:** @@ -939,7 +941,7 @@ pyspark \ --conf spark.kryoserializer.buffer.max=2000M \ --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \ --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \ - --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 ``` **Databricks:** @@ -1211,7 +1213,7 @@ spark = SparkSession.builder .config("spark.driver.memory", "16G") .config("spark.driver.maxResultSize", "0") .config("spark.kryoserializer.buffer.max", "2000M") - .config("spark.jars", "/tmp/spark-nlp-assembly-4.3.2.jar") + .config("spark.jars", "/tmp/spark-nlp-assembly-4.4.0.jar") .getOrCreate() ``` @@ -1220,7 +1222,7 @@ spark = SparkSession.builder version (3.0.x, 3.1.x, 3.2.x, and 3.3.x) - If you are local, you can load the Fat JAR from your local FileSystem, however, if you are in a cluster setup you need to put the Fat JAR on a distributed FileSystem such as HDFS, DBFS, S3, etc. ( - i.e., `hdfs:///tmp/spark-nlp-assembly-4.3.2.jar`) + i.e., `hdfs:///tmp/spark-nlp-assembly-4.4.0.jar`) Example of using pretrained Models and Pipelines in offline: diff --git a/python/docs/conf.py b/python/docs/conf.py index 028615dbe872..06ebf3cdb6a8 100644 --- a/python/docs/conf.py +++ b/python/docs/conf.py @@ -23,7 +23,7 @@ author = "John Snow Labs" # The full version, including alpha/beta/rc tags -release = "4.3.2" +release = "4.4.0" pyspark_version = "3.2.3" # -- General configuration --------------------------------------------------- diff --git a/python/setup.py b/python/setup.py index 31dca66a73cf..b0bb5695f537 100644 --- a/python/setup.py +++ b/python/setup.py @@ -41,7 +41,7 @@ # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='4.3.2', # Required + version='4.4.0', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the 'Summary' metadata field: diff --git a/python/sparknlp/__init__.py b/python/sparknlp/__init__.py index ede23d2fd859..562f6b8eb9c5 100644 --- a/python/sparknlp/__init__.py +++ b/python/sparknlp/__init__.py @@ -128,7 +128,7 @@ def start(gpu=False, The initiated Spark session. """ - current_version = "4.3.2" + current_version = "4.4.0" if params is None: params = {} @@ -298,4 +298,4 @@ def version(): str The current Spark NLP version. """ - return '4.3.2' + return '4.4.0' diff --git a/scripts/colab_setup.sh b/scripts/colab_setup.sh index a23017c2df5e..bd91295c8dd4 100644 --- a/scripts/colab_setup.sh +++ b/scripts/colab_setup.sh @@ -1,7 +1,7 @@ #!/bin/bash #default values for pyspark, spark-nlp, and SPARK_HOME -SPARKNLP="4.3.2" +SPARKNLP="4.4.0" PYSPARK="3.2.3" while getopts s:p:g option diff --git a/scripts/kaggle_setup.sh b/scripts/kaggle_setup.sh index 492365c31d2a..33b7fb3c7cad 100644 --- a/scripts/kaggle_setup.sh +++ b/scripts/kaggle_setup.sh @@ -1,7 +1,7 @@ #!/bin/bash #default values for pyspark, spark-nlp, and SPARK_HOME -SPARKNLP="4.3.2" +SPARKNLP="4.4.0" PYSPARK="3.2.3" while getopts s:p:g option diff --git a/scripts/sagemaker_setup.sh b/scripts/sagemaker_setup.sh index fba7d9f80a6a..29828875db87 100644 --- a/scripts/sagemaker_setup.sh +++ b/scripts/sagemaker_setup.sh @@ -1,7 +1,7 @@ #!/bin/bash # Default values for pyspark, spark-nlp, and SPARK_HOME -SPARKNLP="4.3.2" +SPARKNLP="4.4.0" PYSPARK="3.2.3" echo "Setup SageMaker for PySpark $PYSPARK and Spark NLP $SPARKNLP" diff --git a/src/main/scala/com/johnsnowlabs/nlp/SparkNLP.scala b/src/main/scala/com/johnsnowlabs/nlp/SparkNLP.scala index 98f4b6e6d9e9..608a5003b87c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/SparkNLP.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/SparkNLP.scala @@ -20,7 +20,7 @@ import org.apache.spark.sql.SparkSession object SparkNLP { - val currentVersion = "4.3.2" + val currentVersion = "4.4.0" val MavenSpark3 = s"com.johnsnowlabs.nlp:spark-nlp_2.12:$currentVersion" val MavenGpuSpark3 = s"com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:$currentVersion" val MavenSparkSilicon = s"com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:$currentVersion" diff --git a/src/main/scala/com/johnsnowlabs/util/Build.scala b/src/main/scala/com/johnsnowlabs/util/Build.scala index aa56594894b4..23922a8c8bb0 100644 --- a/src/main/scala/com/johnsnowlabs/util/Build.scala +++ b/src/main/scala/com/johnsnowlabs/util/Build.scala @@ -17,5 +17,5 @@ package com.johnsnowlabs.util object Build { - val version: String = "4.3.2" + val version: String = "4.4.0" } From be12a9b53b11977f2f7bc9628aa234866770b11e Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Fri, 7 Apr 2023 08:17:38 +0000 Subject: [PATCH 10/26] Update doc style and fix unit test [skip test] Signed-off-by: Maziyar Panahi --- src/main/scala/com/johnsnowlabs/nlp/annotator.scala | 4 ++-- .../johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala | 1 - .../classifier/dl/BertForZeroShotClassificationTestSpec.scala | 3 +-- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala index 33cd0da4f951..8a8e510cf99e 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala @@ -674,7 +674,7 @@ package object annotator { type Date2Chunk = com.johnsnowlabs.nlp.annotators.Date2Chunk object Date2Chunk extends DefaultParamsReadable[Date2Chunk] - + type Chunk2Doc = com.johnsnowlabs.nlp.annotators.Chunk2Doc object Chunk2Doc extends DefaultParamsReadable[Chunk2Doc] @@ -686,7 +686,7 @@ package object annotator { with ReadBartTransformerDLModel type BertForZeroShotClassification = - com.johnsnowlabs.nlp.annotators.classifier.dl.BertForZeroShotClassification + com.johnsnowlabs.nlp.annotators.classifier.dl.BertForZeroShotClassification object BertForZeroShotClassification extends ReadablePretrainedBertForZeroShotModel diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala index 19efe6bdad5a..acb2effa519c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerApproach.scala @@ -44,7 +44,6 @@ import scala.io.Source * `setPatternsResource`. The file format needs to be set as the "format" field in the `option` * parameter map and depending on the file type, additional parameters might need to be set. * - * * If the file is in a JSON format, then the rule definitions need to be given in a list with the * fields "id", "label" and "patterns": * {{{ diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala index dbd181263442..b1709e732e29 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassificationTestSpec.scala @@ -61,8 +61,7 @@ class BertForZeroShotClassificationTestSpec extends AnyFlatSpec { .setCoalesceSentences(true) .setCandidateLabels(candidateLabels) - val pipeline = new Pipeline().setStages( - Array(document, tokenizer, tokenClassifier)) + val pipeline = new Pipeline().setStages(Array(document, tokenizer, tokenClassifier)) val pipelineModel = pipeline.fit(ddd) val pipelineDF = pipelineModel.transform(ddd) From 47d8e516d5678e19a469c36439b50d842332ae72 Mon Sep 17 00:00:00 2001 From: Devin Ha Date: Fri, 7 Apr 2023 10:30:53 +0200 Subject: [PATCH 11/26] SPARKNLP-605: Fix parameter eval for vit tests --- .../nlp/annotators/cv/ViTImageClassificationTestSpec.scala | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala index da5441f820c5..fdf2e43b574a 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ViTImageClassificationTestSpec.scala @@ -38,9 +38,9 @@ trait ViTForImageClassificationBehaviors { this: AnyFlatSpec => .setOutputCol("image_assembler") def behaviorsViTForImageClassification[M <: ViTForImageClassification]( - loadModelFunction: String => M, - vitClassifier: ViTForImageClassification, - expectedPredictions: Map[String, String]): Unit = { + loadModelFunction: => String => M, + vitClassifier: => ViTForImageClassification, + expectedPredictions: => Map[String, String]): Unit = { def setUpImageClassifierPipeline(): Pipeline = { val imageClassifier: ViTForImageClassification = vitClassifier From 02c079c0dbc93e32175f1738cfd75d21ef4de7fe Mon Sep 17 00:00:00 2001 From: Prabod Rathnayaka Date: Fri, 7 Apr 2023 21:01:19 +1000 Subject: [PATCH 12/26] Update default model name (#13744) --- python/sparknlp/annotator/seq2seq/bart_transformer.py | 4 ++-- .../johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/python/sparknlp/annotator/seq2seq/bart_transformer.py b/python/sparknlp/annotator/seq2seq/bart_transformer.py index f717cc4bf049..bd7c25cf7ff9 100755 --- a/python/sparknlp/annotator/seq2seq/bart_transformer.py +++ b/python/sparknlp/annotator/seq2seq/bart_transformer.py @@ -380,13 +380,13 @@ def loadSavedModel(folder, spark_session): return BartTransformer(java_model=jModel) @staticmethod - def pretrained(name="t5_small", lang="en", remote_loc=None): + def pretrained(name="bart_large_cnn", lang="en", remote_loc=None): """Downloads and loads a pretrained model. Parameters ---------- name : str, optional - Name of the pretrained model, by default "t5_small" + Name of the pretrained model, by default "bart_large_cnn" lang : str, optional Language of the pretrained model, by default "en" remote_loc : str, optional diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala index c59408f117db..65ddcb2ae616 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala @@ -522,7 +522,7 @@ class BartTransformer(override val uid: String) trait ReadablePretrainedBartTransformerModel extends ParamsAndFeaturesReadable[BartTransformer] with HasPretrained[BartTransformer] { - override val defaultModelName: Some[String] = Some("bart") + override val defaultModelName: Some[String] = Some("bart_large_cnn") /** Java compliant-overrides */ override def pretrained(): BartTransformer = super.pretrained() From 07d5da88baec697a92576747d9e27272c37f0d95 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Fri, 7 Apr 2023 13:53:58 +0200 Subject: [PATCH 13/26] SPARKNLP-796 Creating a new `nerHasNoSchema` param (#13745) * Adding missing CPUvsGPUbenchmark page * SPARKNLP-796 Creating a new `nerHasNoSchema` param Signed-off-by: Maziyar Panahi --------- Signed-off-by: Maziyar Panahi --- docs/en/CPUvsGPUbenchmark.md | 269 ++++++++++++++++++ .../sparknlp/annotator/ner/ner_converter.py | 18 ++ .../nlp/annotators/ner/NerConverter.scala | 21 +- .../nlp/annotators/ner/NerTagsEncoding.scala | 12 +- 4 files changed, 312 insertions(+), 8 deletions(-) create mode 100644 docs/en/CPUvsGPUbenchmark.md diff --git a/docs/en/CPUvsGPUbenchmark.md b/docs/en/CPUvsGPUbenchmark.md new file mode 100644 index 000000000000..96e3491232cf --- /dev/null +++ b/docs/en/CPUvsGPUbenchmark.md @@ -0,0 +1,269 @@ +--- +layout: docs +header: true +title: GPU vs CPU benchmark +permalink: /docs/en/CPUvsGPUbenchmark +key: docs-concepts +modify_date: "2023-04-06" +use_language_switcher: "Python-Scala" +show_nav: true +sidebar: + nav: sparknlp +--- + +

+ +This section includes benchmarks for different Approach() (training classes), comparing their performance when running in `m5.8xlarge` CPU vs a `Tesla V100 SXM2` GPU, as described in the `Machine Specs` section below. + +Different benchmarks, as well as their takeaways and some conclusions of how to get the best of GPU, are included as well, to guide you in the process of getting the best performance out of Spark NLP on GPU. + +Each major release comes with big improvements, so please, make sure you use at least that version to fully levearge Spark NLP capabilities on GPU. + +
+
+ +### Machine specs + +#### CPU +An AWS `m5.8xlarge` machine was used for the CPU benchmarking. This machine consists of `32 vCPUs` and `128 GB of RAM`, as you can check in the official specification webpage available [here](https://aws.amazon.com/ec2/instance-types/m5/) + +#### GPU +A `Tesla V100 SXM2` GPU with `32GB` of memory was used to calculate the GPU benchmarking. + +
+
+ +### Versions +The benchmarking was carried out with the following Spark NLP versions: + +Spark version: `3.0.2` + +Hadoop version: `3.2.0` + +SparkNLP version: `3.3.4` + +Spark nodes: 1 + +
+
+ +### Benchmark on classifierDLApproach() + +This experiment consisted of training a Deep Learning Binary Classifier (Question vs Statement classes) at sentence-level, using a fully connected CNN and Bert Sentence Embeddings. Only 1 Spark node was usd for the training. + +We used the Spark NLP class `ClassifierDL` and it's method `Approach()` as described in the [documentation](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl). + +The pipeline looks as follows: +![](/assets/images/gpu_v2_pic3.png) + +
+
+ +#### Dataset +The size of the dataset was relatively small (200K), consisting of: + +Training (rows): `162250` + +Test (rows): `40301` + +
+
+ +#### Training params +Different batch sizes were tested to demonstrate how GPU performance improves with bigger batches compared to CPU, for a constant number of epochs and learning rate. + +Epochs: `10` + +Learning rate: `0.003` + +Batch sizes: `32`, `64`, `256`, `1024` + +
+
+ +#### Results +Even for this average-sized dataset, we can observe that GPU is able to beat the CPU machine by a `76%` in both `training` and `inference` times. + +
+
+ +#### Training times depending on batch (in minutes) + +![](/assets/images/gpu_v2_pic1.png) + + +{:.table-model-big} +| Batch size | CPU | GPU | +| :---: | :---: | :--: | +| 32 | 66 | 16.1 | +| 64 | 65 | 15.3 | +| 256 | 64 | 14.5 | +| 1024 | 64 | 14 | + +
+
+ +#### Inference times (in minutes) +The average inference time remained more or less constant regardless the batch size: +CPU: `8.7 min` +GPU: `2 min` + +![](/assets/images/gpu_v2_pic5.png) + +
+
+ +#### Performance metrics +A weighted F1-score of 0.88 was achieved, with a 0.90 score for question detection and 0.83 for statements. + +![](/assets/images/gpu_v2_pic2.png) + +
+
+ +### Benchmark on NerDLApproach() + +This experiment consisted of training a Name Entity Recognition model (token-level), using our class NerDLApproach(), using Bert Word Embeddings and a Char-CNN-BiLSTM Neural Network. Only 1 Spark node was used for the training. + +We used the Spark NLP class `NerDL` and it's method `Approach()` as described in the [documentation](https://nlp.johnsnowlabs.com/docs/en/annotators#nerdl). + +The pipeline looks as follows: +![](/assets/images/gpu_v2_pic4.png) + +
+
+ +#### Dataset +The size of the dataset was small (17K), consisting of: + +Training (rows): `14041` + +Test (rows): `3250` + +
+
+ +#### Training params +Different batch sizes were tested to demonstrate how GPU performance improves with bigger batches compared to CPU, for a constant number of epochs and learning rate. + +Epochs: `10` + +Learning rate: `0.003` + +Batch sizes: `32`, `64`, `256`, `512`, `1024`, `2048` + +
+
+ +#### Results +Even for this small dataset, we can observe that GPU is able to beat the CPU machine by a `62%` in `training` time and a `68%` in `inference` times. It's important to mention that the batch size is very relevant when using GPU, since CPU scales much worse with bigger batch sizes than GPU. + +
+
+ +#### Training times depending on batch (in minutes) + +![](/assets/images/gpu_v2_pic6.png) + +{:.table-model-big} +| Batch size | CPU | GPU | +| :---: | :---: | :--: | +| 32 | 9.5 | 10 | +| 64 | 8.1 | 6.5 | +| 256 | 6.9 | 3.5 | +| 512 | 6.7 | 3 | +| 1024 | 6.5 | 2.5 | +| 2048 | 6.5 | 2.5 | + +
+
+ +#### Inference times (in minutes) +Although CPU times in inference remain more or less constant regardless the batch sizes, GPU time experiment good improvements the bigger the batch size is. + +CPU times: `~29 min` + +{:.table-model-big} +| Batch size | GPU | +| :---: | :--: | +| 32 | 10 | +| 64 | 6.5 | +| 256 | 3.5 | +| 512 | 3 | +| 1024 | 2.5 | +| 2048 | 2.5 | + +![](/assets/images/gpu_v2_pic7.png) + +
+
+ +#### Performance metrics +A macro F1-score of about `0.92` (`0.90` in micro) was achieved, with the following charts extracted from the `NERDLApproach()` logs: + +![](/assets/images/gpu_v2_pic8.png) + +
+
+ +### Inference benchmark on BertSentenceEmbeddings() + +This experiment consisted of benchmarking the improvement obtained in inference by using GPU on BertSentenceEmbeddings(). + +We used the Spark NLP class `BertSentenceEmbeddings()` described in the Transformers [documentation](https://nlp.johnsnowlabs.com/docs/en/transformers#bertsentenceembeddings). + +The pipeline contains only two components and looks as follows: +![](/assets/images/gpu_v2_pic9.png) + +
+
+ +#### Dataset +The size of the dataset was bigger than the previous ones, with `417735` rows for inference. + +
+
+ +#### Results +We have observed in previous experiments, using BertSentenceEmbeddings (classifierDL) and also BertEmbeddings (NerDL) how GPU improved both training and inference times. In this case, we observe again big improvements in inference, what is already pointing that one of the main reasons of why GPU improves so much over CPU is the better management of Embeddings (word, sentence level) and bigger batch sizes. + +Batch sizes: `32`, `64`, `256`, `1024` + +
+
+ +#### Inference times depending on batch (in minutes) +![](/assets/images/gpu_v2_pic10.png) + +{:.table-model-big} +| Batch size | CPU | GPU | +| :---: | :---: | :--: | +| 32 | 80 | 9.9 | +| 64 | 77 | 9.8 | +| 256 | 63 | 9.4 | +| 1024 | 62 | 9.1 | + +
+
+ +### Takeaways: How to get the best of the GPU +You will experiment big GPU improvements in the following cases: + +{:.list1} +1. Embeddings and Transformers are used in your pipeline. Take into consideration that GPU will performance very well in Embeddings / Transformer components, but other components of your pipeline may not leverage as well GPU capabilities; +2. Bigger batch sizes get the best of GPU, while CPU does not scale with bigger batch sizes; +3. Bigger dataset sizes get the best of GPU, while may be a bottleneck while running in CPU and lead to performance drops; + +
+
+ +### MultiGPU training +Right now, we don't support multigpu training (1 model in different GPUs in parallel), but you can train different models in different GPU. + +
+
+ +### Where to look for more information about Training +Please, take a look at the [Spark NLP](https://nlp.johnsnowlabs.com/docs/en/training) and [Spark NLP for Healthcare](https://nlp.johnsnowlabs.com/docs/en/licensed_training) Training sections, and feel free to reach us out in case you want to maximize the performance on your GPU. + +
\ No newline at end of file diff --git a/python/sparknlp/annotator/ner/ner_converter.py b/python/sparknlp/annotator/ner/ner_converter.py index 210a6cd2cbf2..b3b7780f05db 100644 --- a/python/sparknlp/annotator/ner/ner_converter.py +++ b/python/sparknlp/annotator/ner/ner_converter.py @@ -98,6 +98,13 @@ class on how to extract the entities. The output of the NerDLModel follows typeConverter=TypeConverters.toBoolean ) + nerHasNoSchema = Param( + Params._dummy(), + "nerHasNoSchema", + "set this to true if your NER tags coming from a model that does not have a IOB/IOB2 schema", + typeConverter=TypeConverters.toBoolean + ) + def setWhiteList(self, entities): """Sets list of entities to process. The rest will be ignored. @@ -124,6 +131,17 @@ def setPreservePosition(self, value): """ return self._set(preservePosition=value) + def setNerHasNoSchema(self, value): + """ + set this to true if your NER tags coming from a model that does not have a IOB/IOB2 schema + + Parameters + ---------- + value : bool + set this to true if your NER tags coming from a model that does not have a IOB/IOB2 schema + """ + return self._set(nerHasNoSchema=value) + @keyword_only def __init__(self): super(NerConverter, self).__init__( diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerConverter.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerConverter.scala index 7aa8efc87bf1..0f51cb56ced4 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerConverter.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerConverter.scala @@ -141,7 +141,19 @@ class NerConverter(override val uid: String) */ def setPreservePosition(value: Boolean): this.type = set(preservePosition, value) - setDefault(preservePosition -> true) + /** set this to true if your NER tags coming from a model that does not have a IOB/IOB2 schema + * + * @group param + */ + val nerHasNoSchema: BooleanParam = new BooleanParam( + this, + "nerHasNoSchema", + "set this to true if your NER tags coming from a model that does not have a IOB/IOB2 schema") + + /** @group setParam */ + def setNerHasNoSchema(value: Boolean): this.type = set(nerHasNoSchema, value) + + setDefault(preservePosition -> true, nerHasNoSchema -> false) override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = { val sentences = NerTagged.unpack(annotations) @@ -150,7 +162,12 @@ class NerConverter(override val uid: String) b.indexedTaggedWords.exists(c => c.begin >= a.begin && c.end <= a.end))) val entities = sentences.zip(docs.zipWithIndex).flatMap { case (sentence, doc) => - NerTagsEncoding.fromIOB(sentence, doc._1, sentenceIndex = doc._2, $(preservePosition)) + NerTagsEncoding.fromIOB( + sentence, + doc._1, + sentenceIndex = doc._2, + originalOffset = $(preservePosition), + nerHasNoSchema = $(nerHasNoSchema)) } entities diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerTagsEncoding.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerTagsEncoding.scala index 0925357c4283..f9bf0090f77a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerTagsEncoding.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/NerTagsEncoding.scala @@ -37,6 +37,7 @@ object NerTagsEncoding { doc: Annotation, sentenceIndex: Int = 0, originalOffset: Boolean = true, + nerHasNoSchema: Boolean = false, includeNoneEntities: Boolean = false, format: String = "IOB2"): Seq[NamedEntity] = { @@ -87,9 +88,9 @@ object NerTagsEncoding { } - def getTag(tag: String, startsWithSchema: Boolean = true): Option[String] = { + def getTag(tag: String): Option[String] = { try { - lastTag = Some(if (startsWithSchema) tag.substring(2) else tag) + lastTag = Some(if (nerHasNoSchema) tag else tag.substring(2)) } catch { case e: StringIndexOutOfBoundsException => require( @@ -101,17 +102,16 @@ object NerTagsEncoding { for (i <- 0 until words) { val tag = sentence.tags(i) - val hasSchema = tag.startsWith(beginningTagChunk) - if (lastTag.isDefined && (hasSchema || tag == noChunk)) { + if (lastTag.isDefined && (tag.startsWith(beginningTagChunk) || tag == noChunk)) { flushEntity(lastTagStart, i - 1) } if (includeNoneEntities && lastTag.isEmpty) { - lastTag = if (tag == noChunk) Some(tag) else getTag(tag, hasSchema) + lastTag = if (tag == noChunk) Some(tag) else getTag(tag) lastTagStart = i } else { if (lastTag.isEmpty && tag != noChunk) { - lastTag = getTag(tag, hasSchema) + lastTag = getTag(tag) lastTagStart = i } } From e92d21bef28df88151d5eaf5924cf37dcaf5ba69 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Fri, 7 Apr 2023 12:16:07 +0000 Subject: [PATCH 14/26] Change default model for BART to distilbart-xsum-12-6 Signed-off-by: Maziyar Panahi --- python/sparknlp/annotator/seq2seq/bart_transformer.py | 8 ++++---- .../nlp/annotators/seq2seq/BartTransformer.scala | 6 +++--- .../nlp/annotators/seq2seq/BartTestSpec.scala | 8 ++++---- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/python/sparknlp/annotator/seq2seq/bart_transformer.py b/python/sparknlp/annotator/seq2seq/bart_transformer.py index bd7c25cf7ff9..2c0bf3eac55e 100755 --- a/python/sparknlp/annotator/seq2seq/bart_transformer.py +++ b/python/sparknlp/annotator/seq2seq/bart_transformer.py @@ -50,7 +50,7 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): ... .setOutputCol("summaries") - The default model is ``"bart_large_cnn"``, if no name is provided. For available + The default model is ``"distilbart-xsum-12-6"``, if no name is provided. For available pretrained models please see the `Models Hub `__. @@ -131,7 +131,7 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): >>> documentAssembler = DocumentAssembler() \\ ... .setInputCol("text") \\ ... .setOutputCol("documents") - >>> bart = BartTransformer.pretrained("bart_large_cnn") \\ + >>> bart = BartTransformer.pretrained("distilbart-xsum-12-6") \\ ... .setTask("summarize:") \\ ... .setInputCols(["documents"]) \\ ... .setMaxOutputLength(200) \\ @@ -380,13 +380,13 @@ def loadSavedModel(folder, spark_session): return BartTransformer(java_model=jModel) @staticmethod - def pretrained(name="bart_large_cnn", lang="en", remote_loc=None): + def pretrained(name="distilbart-xsum-12-6", lang="en", remote_loc=None): """Downloads and loads a pretrained model. Parameters ---------- name : str, optional - Name of the pretrained model, by default "bart_large_cnn" + Name of the pretrained model, by default "distilbart-xsum-12-6" lang : str, optional Language of the pretrained model, by default "en" remote_loc : str, optional diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala index 65ddcb2ae616..51b19ce97b7c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala @@ -65,7 +65,7 @@ import org.apache.spark.sql.SparkSession * .setInputCols("document") * .setOutputCol("generation") * }}} - * The default model is `"bart_large_cnn"`, if no name is provided. For available pretrained + * The default model is `"distilbart-xsum-12-6"`, if no name is provided. For available pretrained * models please see the [[https://nlp.johnsnowlabs.com/models?q=bart Models Hub]]. * * For extended examples of usage, see @@ -108,7 +108,7 @@ import org.apache.spark.sql.SparkSession * .setInputCol("text") * .setOutputCol("documents") * - * val bart = BartTransformer.pretrained("bart_large_cnn") + * val bart = BartTransformer.pretrained("distilbart-xsum-12-6") * .setInputCols(Array("documents")) * .setMinOutputLength(10) * .setMaxOutputLength(30) @@ -522,7 +522,7 @@ class BartTransformer(override val uid: String) trait ReadablePretrainedBartTransformerModel extends ParamsAndFeaturesReadable[BartTransformer] with HasPretrained[BartTransformer] { - override val defaultModelName: Some[String] = Some("bart_large_cnn") + override val defaultModelName: Some[String] = Some("distilbart-xsum-12-6") /** Java compliant-overrides */ override def pretrained(): BartTransformer = super.pretrained() diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala index d10fdf5386ef..252e4dbb1feb 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala @@ -43,7 +43,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("bart_large_cnn") + .pretrained("distilbart-xsum-12-6") .setTask("summarize:") .setInputCols(Array("documents")) .setDoSample(true) @@ -84,7 +84,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("bart_large_cnn") + .pretrained("distilbart-xsum-12-6") .setTask("summarize:") .setInputCols(Array("documents")) .setMaxOutputLength(70) @@ -128,7 +128,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("bart_large_cnn") + .pretrained("distilbart-xsum-12-6") .setTask("summarize:") .setInputCols(Array("documents")) .setMaxOutputLength(100) @@ -170,7 +170,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("bart_large_cnn") + .pretrained("distilbart-xsum-12-6") .setTask("summarize:") .setInputCols(Array("documents")) .setDoSample(true) From 1676c6f10e58b34563564e6df05abb62a008bcc3 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Fri, 7 Apr 2023 13:57:54 +0000 Subject: [PATCH 15/26] Change default model for BART to distilbart_xsum_12_6 Signed-off-by: Maziyar Panahi --- .../sparknlp/annotator/seq2seq/bart_transformer.py | 8 ++++---- .../annotator/seq2seq/bart_transformer_test.py | 14 +++++++------- .../nlp/annotators/seq2seq/BartTransformer.scala | 8 ++++---- .../nlp/annotators/seq2seq/BartTestSpec.scala | 8 ++++---- 4 files changed, 19 insertions(+), 19 deletions(-) diff --git a/python/sparknlp/annotator/seq2seq/bart_transformer.py b/python/sparknlp/annotator/seq2seq/bart_transformer.py index 2c0bf3eac55e..35cb40d13fe9 100755 --- a/python/sparknlp/annotator/seq2seq/bart_transformer.py +++ b/python/sparknlp/annotator/seq2seq/bart_transformer.py @@ -50,7 +50,7 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): ... .setOutputCol("summaries") - The default model is ``"distilbart-xsum-12-6"``, if no name is provided. For available + The default model is ``"distilbart_xsum_12_6"``, if no name is provided. For available pretrained models please see the `Models Hub `__. @@ -131,7 +131,7 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): >>> documentAssembler = DocumentAssembler() \\ ... .setInputCol("text") \\ ... .setOutputCol("documents") - >>> bart = BartTransformer.pretrained("distilbart-xsum-12-6") \\ + >>> bart = BartTransformer.pretrained("distilbart_xsum_12_6") \\ ... .setTask("summarize:") \\ ... .setInputCols(["documents"]) \\ ... .setMaxOutputLength(200) \\ @@ -380,13 +380,13 @@ def loadSavedModel(folder, spark_session): return BartTransformer(java_model=jModel) @staticmethod - def pretrained(name="distilbart-xsum-12-6", lang="en", remote_loc=None): + def pretrained(name="distilbart_xsum_12_6", lang="en", remote_loc=None): """Downloads and loads a pretrained model. Parameters ---------- name : str, optional - Name of the pretrained model, by default "distilbart-xsum-12-6" + Name of the pretrained model, by default "distilbart_xsum_12_6" lang : str, optional Language of the pretrained model, by default "en" remote_loc : str, optional diff --git a/python/test/annotator/seq2seq/bart_transformer_test.py b/python/test/annotator/seq2seq/bart_transformer_test.py index 955b6b9041c9..c2637a853ae2 100644 --- a/python/test/annotator/seq2seq/bart_transformer_test.py +++ b/python/test/annotator/seq2seq/bart_transformer_test.py @@ -37,7 +37,7 @@ def runTest(self): .setInputCol("text") \ .setOutputCol("documents") - bart = BartTransformer.pretrained("bart_large_cnn") \ + bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ .setInputCols(["documents"]) \ .setOutputCol("answers") @@ -77,7 +77,7 @@ def runTest(self): .setInputCol("text") \ .setOutputCol("documents") - bart = BartTransformer.pretrained("bart_large_cnn") \ + bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ .setTask("summarize:") \ .setMaxOutputLength(200) \ .setInputCols(["documents"]) \ @@ -113,7 +113,7 @@ def runTest(self): .setInputCol("text") \ .setOutputCol("documents") - bart = BartTransformer.pretrained("bart_large_cnn") \ + bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ .setTask("summarize:") \ .setMaxOutputLength(50) \ .setInputCols(["documents"]) \ @@ -150,7 +150,7 @@ def runTest(self): .setInputCol("text") \ .setOutputCol("documents") - bart = BartTransformer.pretrained("bart_large_cnn") \ + bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ .setTask("summarize:") \ .setMaxOutputLength(50) \ .setDoSample(True) \ @@ -188,7 +188,7 @@ def runTest(self): .setInputCol("text") \ .setOutputCol("documents") - bart = BartTransformer.pretrained("bart_large_cnn") \ + bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ .setTask("summarize:") \ .setMaxOutputLength(50) \ .setDoSample(True) \ @@ -227,7 +227,7 @@ def runTest(self): .setInputCol("text") \ .setOutputCol("documents") - bart = BartTransformer.pretrained("bart_large_cnn")\ + bart = BartTransformer.pretrained("distilbart_xsum_12_6")\ .setTask("summarize:") \ .setMaxOutputLength(50) \ .setDoSample(True) \ @@ -266,7 +266,7 @@ def runTest(self): .setInputCol("text") \ .setOutputCol("documents") - bart = BartTransformer.pretrained("bart_large_cnn") \ + bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ .setTask("summarize:") \ .setMaxOutputLength(50) \ .setDoSample(True) \ diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala index 51b19ce97b7c..b7f878f8e781 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala @@ -65,8 +65,8 @@ import org.apache.spark.sql.SparkSession * .setInputCols("document") * .setOutputCol("generation") * }}} - * The default model is `"distilbart-xsum-12-6"`, if no name is provided. For available pretrained - * models please see the [[https://nlp.johnsnowlabs.com/models?q=bart Models Hub]]. + * The default model is `"distilbart_xsum_12_6"`, if no name is provided. For available + * pretrained models please see the [[https://nlp.johnsnowlabs.com/models?q=bart Models Hub]]. * * For extended examples of usage, see * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala BartTestSpec]]. @@ -108,7 +108,7 @@ import org.apache.spark.sql.SparkSession * .setInputCol("text") * .setOutputCol("documents") * - * val bart = BartTransformer.pretrained("distilbart-xsum-12-6") + * val bart = BartTransformer.pretrained("distilbart_xsum_12_6") * .setInputCols(Array("documents")) * .setMinOutputLength(10) * .setMaxOutputLength(30) @@ -522,7 +522,7 @@ class BartTransformer(override val uid: String) trait ReadablePretrainedBartTransformerModel extends ParamsAndFeaturesReadable[BartTransformer] with HasPretrained[BartTransformer] { - override val defaultModelName: Some[String] = Some("distilbart-xsum-12-6") + override val defaultModelName: Some[String] = Some("distilbart_xsum_12_6") /** Java compliant-overrides */ override def pretrained(): BartTransformer = super.pretrained() diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala index 252e4dbb1feb..11b5e8d768aa 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala @@ -43,7 +43,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("distilbart-xsum-12-6") + .pretrained("distilbart_xsum_12_6") .setTask("summarize:") .setInputCols(Array("documents")) .setDoSample(true) @@ -84,7 +84,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("distilbart-xsum-12-6") + .pretrained("distilbart_xsum_12_6") .setTask("summarize:") .setInputCols(Array("documents")) .setMaxOutputLength(70) @@ -128,7 +128,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("distilbart-xsum-12-6") + .pretrained("distilbart_xsum_12_6") .setTask("summarize:") .setInputCols(Array("documents")) .setMaxOutputLength(100) @@ -170,7 +170,7 @@ class BartTestSpec extends AnyFlatSpec { .setOutputCol("documents") val bart = BartTransformer - .pretrained("distilbart-xsum-12-6") + .pretrained("distilbart_xsum_12_6") .setTask("summarize:") .setInputCols(Array("documents")) .setDoSample(true) From dc149647c00d219a8193239947ac4c42e5b305d4 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Fri, 7 Apr 2023 16:21:03 +0200 Subject: [PATCH 16/26] Replace nlp with sparknlp.org website --- .github/ISSUE_TEMPLATE/config.yml | 4 +- CHANGELOG | 2 +- README.md | 10 +- build.sbt | 2 +- docs/en/CPUvsGPUbenchmark.md | 8 +- docs/en/annotator_entries/ClassifierDL.md | 2 +- .../annotator_entries/ContextSpellChecker.md | 2 +- docs/en/annotator_entries/DateMatcher.md | 2 +- docs/en/annotator_entries/DependencyParser.md | 2 +- docs/en/annotator_entries/Doc2Vec.md | 2 +- .../annotator_entries/LanguageDetectorDL.md | 2 +- docs/en/annotator_entries/Lemmatizer.md | 4 +- .../en/annotator_entries/MultiClassifierDL.md | 2 +- docs/en/annotator_entries/NerCrf.md | 2 +- docs/en/annotator_entries/NerDL.md | 4 +- docs/en/annotator_entries/NorvigSweeting.md | 2 +- docs/en/annotator_entries/Perceptron.md | 4 +- docs/en/annotator_entries/RegexMatcher.md | 2 +- .../annotator_entries/SentenceDetectorDL.md | 2 +- docs/en/annotator_entries/SentimentDL.md | 2 +- docs/en/annotator_entries/StopWordsCleaner.md | 4 +- docs/en/annotator_entries/SymmetricDelete.md | 2 +- .../TypedDependencyParser.md | 2 +- docs/en/annotator_entries/Word2Vec.md | 2 +- docs/en/annotator_entries/WordEmbeddings.md | 2 +- docs/en/annotator_entries/WordSegmenter.md | 2 +- docs/en/best_practices_pretrained_models.md | 304 ------- docs/en/display.md | 2 +- docs/en/graph.md | 2 +- docs/en/hardware_acceleration.md | 2 +- docs/en/install.md | 2 +- docs/en/mlflow.md | 2 +- .../release_notes_0_4_0.md | 2 +- docs/en/pipelines.md | 2 +- docs/en/production-readiness.md | 860 ------------------ docs/en/quickstart.md | 8 +- ...ing_spark_nlp_via_api_databricks_mlflow.md | 10 +- docs/en/spark_nlp.md | 8 +- docs/en/training.md | 2 +- .../AlbertForQuestionAnswering.md | 2 +- .../AlbertForSequenceClassification.md | 4 +- .../AlbertForTokenClassification.md | 2 +- docs/en/transformer_entries/BertEmbeddings.md | 2 +- .../BertForQuestionAnswering.md | 2 +- .../BertForSequenceClassification.md | 4 +- .../BertForTokenClassification.md | 2 +- .../BertSentenceEmbeddings.md | 2 +- .../CamemBertEmbeddings.md | 2 +- .../CamemBertForSequenceClassification.md | 2 +- .../CamemBertForTokenClassification.md | 4 +- .../ConvNextForImageClassification.md | 2 +- .../DeBertaForQuestionAnswering.md | 2 +- .../DistilBertEmbeddings.md | 2 +- .../DistilBertForQuestionAnswering.md | 2 +- .../DistilBertForSequenceClassification.md | 4 +- .../DistilBertForTokenClassification.md | 2 +- docs/en/transformer_entries/ElmoEmbeddings.md | 2 +- .../en/transformer_entries/GPT2Transformer.md | 2 +- docs/en/transformer_entries/HubertForCTC.md | 2 +- .../LongformerEmbeddings.md | 2 +- .../LongformerForQuestionAnswering.md | 2 +- .../LongformerForSequenceClassification.md | 4 +- .../LongformerForTokenClassification.md | 2 +- .../transformer_entries/MarianTransformer.md | 2 +- .../transformer_entries/RoBertaEmbeddings.md | 2 +- .../RoBertaForQuestionAnswering.md | 2 +- .../RoBertaForSequenceClassification.md | 2 +- .../RoBertaForTokenClassification.md | 2 +- .../RoBertaSentenceEmbeddings.md | 2 +- docs/en/transformer_entries/SpanBertCoref.md | 2 +- .../SwinForImageClassification.md | 2 +- docs/en/transformer_entries/T5Transformer.md | 2 +- .../TapasForQuestionAnswering.md | 2 +- .../UniversalSentenceEncoder.md | 2 +- .../ViTForImageClassification.md | 2 +- docs/en/transformer_entries/Wav2Vec2ForCTC.md | 2 +- .../XlmRoBertaEmbeddings.md | 2 +- .../XlmRoBertaForQuestionAnswering.md | 2 +- .../XlmRoBertaForSequenceClassification.md | 4 +- .../XlmRoBertaForTokenClassification.md | 2 +- .../XlmRoBertaSentenceEmbeddings.md | 2 +- .../XlnetForSequenceClassification.md | 4 +- .../XlnetForTokenClassification.md | 2 +- docs/en/transformer_entries/ZeroShotNer.md | 2 +- ...ecognition_Wav2Vec2_(Wav2Vec2ForCTC).ipynb | 6 +- .../image/ViTForImageClassification.ipynb | 2 +- .../words_segmenter_demo.ipynb | 2 +- .../MultiDateMatcherMultiLanguage_en.ipynb | 2 +- .../Text_Preprocessing_with_SparkNLP.ipynb | 2 +- .../Chunk_Extraction_with_Chunker.ipynb | 2 +- .../english/chunking/NgramGenerator.ipynb | 4 +- ...ing_Documents_With_DocumentAssembler.ipynb | 2 +- .../Loading_Multiple_Documents.ipynb | 2 +- .../document_normalizer_notebook.ipynb | 2 +- .../english/embeddings/ChunkEmbeddings.ipynb | 2 +- .../graph-extraction/graph_extraction.ipynb | 2 +- .../graph_extraction_explode_entities.ipynb | 2 +- .../graph_extraction_helper_display.ipynb | 2 +- .../graph_extraction_intro.ipynb | 2 +- .../graph_extraction_roots_paths.ipynb | 2 +- .../Keyword_Extraction_YAKE.ipynb | 2 +- ...nguage_Detection_and_Indentification.ipynb | 4 +- .../Create custom pipeline - NerDL.ipynb | 2 +- .../ModelDownloaderExample.ipynb | 2 +- ...Named entity recognition - OntoNotes.ipynb | 2 +- .../ZeroShot_NER.ipynb | 2 +- .../Explain Document DL.ipynb | 2 +- .../Pretrained-MatchDateTime-Pipeline.ipynb | 2 +- .../Pretrained-MatchPattern-Pipeline.ipynb | 2 +- .../explain_document_ml.ipynb | 2 +- ..._Answering_and_Summarization_with_T5.ipynb | 22 +- .../Matching_Text_with_RegexMatcher.ipynb | 2 +- .../regex_tokenizer_examples.ipynb | 2 +- .../SentenceDetector_advanced_examples.ipynb | 4 +- .../sentiment-detection/sentiment_rb.ipynb | 2 +- .../playground-dataFrames.ipynb | 2 +- .../spark_nlp_basics_functions.ipynb | 2 +- .../Pretrained-SpellCheckML-Pipeline.ipynb | 2 +- .../stemmer/Word_Stemming_with_Stemmer.ipynb | 2 +- .../english/stop-words/StopWordsCleaner.ipynb | 2 +- .../text-matcher-pipeline/extractor.ipynb | 2 +- .../Spark_NLP_Spark_ML_Text_Similarity.ipynb | 2 +- .../Assembling_Tokens_to_Documents.ipynb | 2 +- .../MultiDateMatcherMultiLanguage_fr.ipynb | 2 +- .../date_matcher_multi_language_fr.ipynb | 2 +- .../MultiDateMatcherMultiLanguage_de.ipynb | 2 +- .../date_matcher_multi_language_de.ipynb | 2 +- .../german/pretrained_german_models.ipynb | 2 +- .../MultiDateMatcherMultiLanguage_it.ipynb | 2 +- .../date_matcher_multi_language_it.ipynb | 2 +- .../multilingual/SentenceDetectorDL.ipynb | 2 +- .../multilingual/Translation_Marian.ipynb | 6 +- .../WordSegmenterMultilingual.ipynb | 6 +- .../MultiDateMatcherMultiLanguage_pt.ipynb | 2 +- .../date_matcher_multi_language_pt.ipynb | 2 +- .../MultiDateMatcherMultiLanguage_es.ipynb | 2 +- .../date_matcher_multi_language_es.ipynb | 2 +- examples/python/quick_start.ipynb | 2 +- .../python/quick_start_google_colab.ipynb | 4 +- ...Segmenter_train_chinese_segmentation.ipynb | 4 +- .../ClassifierDL_Train_and_Evaluate.ipynb | 2 +- ...multi_class_news_category_classifier.ipynb | 2 +- ...MultiClassifierDL_Train_and_Evaluate.ipynb | 2 +- ...multi_label_E2E_challenge_classifier.ipynb | 2 +- ...L_train_multi_label_toxic_classifier.ipynb | 2 +- .../SentimentDL_Train_and_Evaluate.ipynb | 2 +- ...rain_multiclass_sentiment_classifier.ipynb | 2 +- .../training/english/crf-ner/ner_dl_crf.ipynb | 2 +- .../english/dl-ner/mfa_ner_graphs_s3.ipynb | 2 +- .../training/english/dl-ner/ner_albert.ipynb | 2 +- .../training/english/dl-ner/ner_bert.ipynb | 2 +- .../training/english/dl-ner/ner_dl.ipynb | 2 +- .../training/english/dl-ner/ner_elmo.ipynb | 2 +- .../english/dl-ner/ner_graph_builder.ipynb | 2 +- .../training/english/dl-ner/ner_logs.ipynb | 2 +- .../training/english/dl-ner/ner_xlnet.ipynb | 2 +- ...rain_Doc2Vec_and_Text_Classification.ipynb | 2 +- .../english/entity-ruler/EntityRuler.ipynb | 2 +- .../entity-ruler/EntityRuler_Alphabet.ipynb | 2 +- .../EntityRuler_LightPipeline.ipynb | 2 +- .../EntityRuler_Without_Storage.ipynb | 2 +- .../RuleBasedSentiment.ipynb | 2 +- .../VivekNarayanSentimentApproach.ipynb | 2 +- ...ord2Vec_and_Named_Entity_Recognition.ipynb | 2 +- .../french/Train-Perceptron-French.ipynb | 2 +- .../italian/Train-Lemmatizer-Italian.ipynb | 2 +- .../Train-SentimentDetector-Italian.ipynb | 2 +- ...aining_Context_Spell_Checker_Italian.ipynb | 2 +- .../HuggingFace in Spark NLP - ALBERT.ipynb | 2 +- ...ark NLP - AlbertForQuestionAnswering.ipynb | 2 +- ...LP - AlbertForSequenceClassification.ipynb | 2 +- ...k NLP - AlbertForTokenClassification.ipynb | 2 +- ...ingFace in Spark NLP - BERT Sentence.ipynb | 2 +- .../HuggingFace in Spark NLP - BERT.ipynb | 2 +- ...Spark NLP - BertForQuestionAnswering.ipynb | 2 +- ... NLP - BertForSequenceClassification.ipynb | 2 +- ...ark NLP - BertForTokenClassification.ipynb | 2 +- ...HuggingFace in Spark NLP - CamemBERT.ipynb | 2 +- ... NLP - CamemBertForQuestionAnswering.ipynb | 2 +- ...- CamemBertForSequenceClassification.ipynb | 2 +- ...LP - CamemBertForTokenClassification.ipynb | 2 +- .../HuggingFace in Spark NLP - DeBERTa.ipynb | 2 +- ...rk NLP - DeBertaForQuestionAnswering.ipynb | 2 +- ...uggingFace in Spark NLP - DistilBERT.ipynb | 2 +- ...NLP - DistilBertForQuestionAnswering.ipynb | 2 +- ... DistilBertForSequenceClassification.ipynb | 2 +- ...P - DistilBertForTokenClassification.ipynb | 2 +- ...uggingFace in Spark NLP - Longformer.ipynb | 2 +- ...NLP - LongformerForQuestionAnswering.ipynb | 2 +- .../HuggingFace in Spark NLP - RoBERTa.ipynb | 2 +- ...rk NLP - RoBertaForQuestionAnswering.ipynb | 2 +- ...P - RoBertaForSequenceClassification.ipynb | 2 +- ... NLP - RoBertaForTokenClassification.ipynb | 2 +- ...park NLP - ViTForImageClassification.ipynb | 2 +- ...ggingFace in Spark NLP - XLM-RoBERTa.ipynb | 2 +- .../HuggingFace in Spark NLP - XLNet.ipynb | 2 +- ... XlmRoBertaForSequenceClassification.ipynb | 2 +- ...P - XlmRoBertaForTokenClassification.ipynb | 2 +- ...NLP - XlmRobertaForQuestionAnswering.ipynb | 2 +- ...NLP - XlnetForSequenceClassification.ipynb | 2 +- ...ernal SavedModel From Remote Storage.ipynb | 2 +- .../TF Hub in Spark NLP - ALBERT.ipynb | 2 +- .../TF Hub in Spark NLP - BERT Sentence.ipynb | 2 +- .../TF Hub in Spark NLP - BERT.ipynb | 2 +- examples/scala/training/NerDL/win/README.md | 2 +- .../CustomForNerDLPipeline.java | 2 +- examples/util/Load_Model_From_S3.ipynb | 2 +- .../util/Load_Model_from_GCP_Storage.ipynb | 2 +- examples/util/Training_Helpers.ipynb | 2 +- python/README.md | 10 +- python/docs/index.rst | 2 +- python/docs/user_guide/annotators.rst | 2 +- python/docs/user_guide/index.rst | 2 +- .../docs/user_guide/pretrained_pipelines.rst | 2 +- .../annotator/audio/hubert_for_ctc.py | 2 +- .../annotator/audio/wav2vec2_for_ctc.py | 28 +- .../albert_for_question_answering.py | 2 +- .../albert_for_sequence_classification.py | 4 +- .../albert_for_token_classification.py | 2 +- .../bert_for_question_answering.py | 2 +- .../bert_for_sequence_classification.py | 2 +- .../bert_for_token_classification.py | 2 +- .../bert_for_zero_shot_classification.py | 2 +- .../camembert_for_question_answering.py | 2 +- .../camembert_for_sequence_classification.py | 2 +- .../camembert_for_token_classification.py | 2 +- .../annotator/classifier_dl/classifier_dl.py | 2 +- .../deberta_for_question_answering.py | 2 +- .../deberta_for_sequence_classification.py | 4 +- .../deberta_for_token_classification.py | 2 +- .../distil_bert_for_question_answering.py | 2 +- ...distil_bert_for_sequence_classification.py | 4 +- .../distil_bert_for_token_classification.py | 2 +- .../longformer_for_question_answering.py | 2 +- .../longformer_for_sequence_classification.py | 4 +- .../longformer_for_token_classification.py | 2 +- .../classifier_dl/multi_classifier_dl.py | 2 +- .../roberta_for_question_answering.py | 2 +- .../roberta_for_sequence_classification.py | 4 +- .../roberta_for_token_classification.py | 2 +- .../annotator/classifier_dl/sentiment_dl.py | 2 +- .../tapas_for_question_answering.py | 2 +- .../xlm_roberta_for_question_answering.py | 2 +- ...xlm_roberta_for_sequence_classification.py | 4 +- .../xlm_roberta_for_token_classification.py | 2 +- .../xlnet_for_sequence_classification.py | 4 +- .../xlnet_for_token_classification.py | 2 +- .../annotator/coref/spanbert_coref.py | 2 +- .../cv/convnext_for_image_classification.py | 2 +- .../cv/swin_for_image_classification.py | 2 +- .../cv/vit_for_image_classification.py | 2 +- .../annotator/dependency/dependency_parser.py | 2 +- .../dependency/typed_dependency_parser.py | 2 +- .../annotator/embeddings/bert_embeddings.py | 2 +- .../embeddings/bert_sentence_embeddings.py | 2 +- .../embeddings/camembert_embeddings.py | 2 +- .../embeddings/distil_bert_embeddings.py | 2 +- .../sparknlp/annotator/embeddings/doc2vec.py | 2 +- .../annotator/embeddings/elmo_embeddings.py | 2 +- .../embeddings/longformer_embeddings.py | 2 +- .../embeddings/roberta_embeddings.py | 2 +- .../embeddings/roberta_sentence_embeddings.py | 2 +- .../embeddings/universal_sentence_encoder.py | 2 +- .../sparknlp/annotator/embeddings/word2vec.py | 2 +- .../annotator/embeddings/word_embeddings.py | 2 +- .../embeddings/xlm_roberta_embeddings.py | 2 +- .../xlm_roberta_sentence_embeddings.py | 2 +- .../annotator/ld_dl/language_detector_dl.py | 2 +- python/sparknlp/annotator/lemmatizer.py | 4 +- .../annotator/matcher/date_matcher.py | 2 +- .../annotator/matcher/regex_matcher.py | 2 +- python/sparknlp/annotator/ner/ner_crf.py | 2 +- python/sparknlp/annotator/ner/ner_dl.py | 4 +- python/sparknlp/annotator/pos/perceptron.py | 4 +- .../sentence/sentence_detector_dl.py | 2 +- .../annotator/seq2seq/bart_transformer.py | 10 +- .../annotator/seq2seq/gpt2_transformer.py | 2 +- .../annotator/seq2seq/marian_transformer.py | 2 +- .../annotator/seq2seq/t5_transformer.py | 2 +- .../spell_check/context_spell_checker.py | 8 +- .../annotator/spell_check/norvig_sweeting.py | 2 +- .../annotator/spell_check/symmetric_delete.py | 2 +- .../sparknlp/annotator/stop_words_cleaner.py | 2 +- .../sparknlp/annotator/ws/word_segmenter.py | 2 +- .../pretrained/pretrained_pipeline.py | 2 +- .../nlp/annotators/DateMatcher.scala | 2 +- .../nlp/annotators/Lemmatizer.scala | 2 +- .../nlp/annotators/LemmatizerModel.scala | 2 +- .../nlp/annotators/RegexMatcher.scala | 2 +- .../nlp/annotators/StopWordsCleaner.scala | 2 +- .../nlp/annotators/audio/HubertForCTC.scala | 2 +- .../nlp/annotators/audio/Wav2Vec2ForCTC.scala | 2 +- .../dl/AlbertForQuestionAnswering.scala | 4 +- .../dl/AlbertForSequenceClassification.scala | 4 +- .../dl/AlbertForTokenClassification.scala | 4 +- .../dl/BertForQuestionAnswering.scala | 4 +- .../dl/BertForSequenceClassification.scala | 4 +- .../dl/BertForTokenClassification.scala | 4 +- .../dl/BertForZeroShotClassification.scala | 6 +- .../dl/CamemBertForQuestionAnswering.scala | 4 +- .../CamemBertForSequenceClassification.scala | 4 +- .../dl/CamemBertForTokenClassification.scala | 4 +- .../classifier/dl/ClassifierDLModel.scala | 2 +- .../dl/DeBertaForQuestionAnswering.scala | 4 +- .../dl/DeBertaForSequenceClassification.scala | 4 +- .../dl/DeBertaForTokenClassification.scala | 4 +- .../dl/DistilBertForQuestionAnswering.scala | 4 +- .../DistilBertForSequenceClassification.scala | 4 +- .../dl/DistilBertForTokenClassification.scala | 4 +- .../dl/LongformerForQuestionAnswering.scala | 4 +- .../LongformerForSequenceClassification.scala | 4 +- .../dl/LongformerForTokenClassification.scala | 4 +- .../dl/MultiClassifierDLModel.scala | 2 +- .../dl/RoBertaForQuestionAnswering.scala | 4 +- .../dl/RoBertaForSequenceClassification.scala | 4 +- .../dl/RoBertaForTokenClassification.scala | 4 +- .../classifier/dl/SentimentDLModel.scala | 2 +- .../dl/TapasForQuestionAnswering.scala | 4 +- .../dl/XlmRoBertaForQuestionAnswering.scala | 4 +- .../XlmRoBertaForSequenceClassification.scala | 4 +- .../dl/XlmRoBertaForTokenClassification.scala | 4 +- .../dl/XlnetForSequenceClassification.scala | 4 +- .../dl/XlnetForTokenClassification.scala | 4 +- .../annotators/coref/SpanBertCorefModel.scala | 2 +- .../cv/ConvNextForImageClassification.scala | 2 +- .../cv/SwinForImageClassification.scala | 2 +- .../cv/ViTForImageClassification.scala | 2 +- .../annotators/ld/dl/LanguageDetectorDL.scala | 2 +- .../nlp/annotators/ner/crf/NerCrfModel.scala | 2 +- .../nlp/annotators/ner/dl/NerDLApproach.scala | 6 +- .../nlp/annotators/ner/dl/NerDLModel.scala | 4 +- .../annotators/ner/dl/ZeroShotNerModel.scala | 2 +- .../parser/dep/DependencyParserModel.scala | 2 +- .../typdep/TypedDependencyParserModel.scala | 2 +- .../pos/perceptron/PerceptronModel.scala | 4 +- .../SentenceDetectorDLModel.scala | 2 +- .../annotators/seq2seq/BartTransformer.scala | 2 +- .../annotators/seq2seq/GPT2Transformer.scala | 2 +- .../seq2seq/MarianTransformer.scala | 2 +- .../annotators/seq2seq/T5Transformer.scala | 2 +- .../context/ContextSpellCheckerModel.scala | 2 +- .../spell/norvig/NorvigSweetingModel.scala | 2 +- .../symmetric/SymmetricDeleteModel.scala | 2 +- .../annotators/ws/WordSegmenterModel.scala | 2 +- .../nlp/embeddings/AlbertEmbeddings.scala | 2 +- .../nlp/embeddings/BertEmbeddings.scala | 4 +- .../embeddings/BertSentenceEmbeddings.scala | 4 +- .../nlp/embeddings/CamemBertEmbeddings.scala | 4 +- .../nlp/embeddings/DeBertaEmbeddings.scala | 2 +- .../nlp/embeddings/DistilBertEmbeddings.scala | 4 +- .../nlp/embeddings/Doc2VecModel.scala | 2 +- .../nlp/embeddings/ElmoEmbeddings.scala | 4 +- .../nlp/embeddings/LongformerEmbeddings.scala | 4 +- .../nlp/embeddings/RoBertaEmbeddings.scala | 4 +- .../RoBertaSentenceEmbeddings.scala | 4 +- .../embeddings/UniversalSentenceEncoder.scala | 4 +- .../nlp/embeddings/Word2VecModel.scala | 2 +- .../nlp/embeddings/WordEmbeddings.scala | 2 +- .../nlp/embeddings/WordEmbeddingsModel.scala | 4 +- .../nlp/embeddings/XlmRoBertaEmbeddings.scala | 4 +- .../XlmRoBertaSentenceEmbeddings.scala | 4 +- .../nlp/embeddings/XlnetEmbeddings.scala | 2 +- .../nlp/pretrained/PretrainedPipeline.scala | 2 +- 363 files changed, 490 insertions(+), 1654 deletions(-) delete mode 100644 docs/en/best_practices_pretrained_models.md delete mode 100644 docs/en/production-readiness.md diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 8d04672dd9d1..dfa9fabe971d 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -5,5 +5,5 @@ contact_links: url: https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 about: Discussion about importing models from other libraries - name: Want to contribute a model? Visit the NLP Models Hub to upload your model. - url: https://nlp.johnsnowlabs.com/models - about: A place for sharing and discovering Spark NLP models and pipelines + url: https://sparknlp.org/models + about: A place for sharing and discovering Spark NLP models and pipelines diff --git a/CHANGELOG b/CHANGELOG index 60862dd626fd..8a87d011587c 100644 --- a/CHANGELOG +++ b/CHANGELOG @@ -798,7 +798,7 @@ New Features Backward compatibility ---------------- -* We have updated our MarianTransformer annotator to be compatible with TF v2 models. This change is not compatible with previous models/pipelines. However, we have updated and uploaded all the models and pipelines for `3.1.x` release. You can either use `MarianTransformer.pretrained(MODEL_NAME)` and it will automatically download the compatible model or you can visit [Models Hub](https://nlp.johnsnowlabs.com/models) to download the compatible models for offline use via `MarianTransformer.load(PATH)` +* We have updated our MarianTransformer annotator to be compatible with TF v2 models. This change is not compatible with previous models/pipelines. However, we have updated and uploaded all the models and pipelines for `3.1.x` release. You can either use `MarianTransformer.pretrained(MODEL_NAME)` and it will automatically download the compatible model or you can visit [Models Hub](https://sparknlp.org/models) to download the compatible models for offline use via `MarianTransformer.load(PATH)` ======== 3.0.3 diff --git a/README.md b/README.md index 14da5a95cec1..fa5682b3092c 100644 --- a/README.md +++ b/README.md @@ -1100,7 +1100,7 @@ ResourceDownloader.showPublicPipelines(lang = "en", version = "3.1.0") */ ``` -#### Please check out our Models Hub for the full list of [pre-trained pipelines](https://nlp.johnsnowlabs.com/models) with examples, demos, benchmarks, and more +#### Please check out our Models Hub for the full list of [pre-trained pipelines](https://sparknlp.org/models) with examples, demos, benchmarks, and more ### Models @@ -1190,7 +1190,7 @@ XlnetEmbeddings */ ``` -#### Please check out our Models Hub for the full list of [pre-trained models](https://nlp.johnsnowlabs.com/models) with examples, demo, benchmark, and more +#### Please check out our Models Hub for the full list of [pre-trained models](https://sparknlp.org/models) with examples, demo, benchmark, and more ## Offline @@ -1201,7 +1201,7 @@ any limitations offline: - Instead of using the Maven package, you need to load our Fat JAR - Instead of using PretrainedPipeline for pretrained pipelines or the `.pretrained()` function to download pretrained - models, you will need to manually download your pipeline/model from [Models Hub](https://nlp.johnsnowlabs.com/models), + models, you will need to manually download your pipeline/model from [Models Hub](https://sparknlp.org/models), extract it, and load it. Example of `SparkSession` with Fat JAR to have Spark NLP offline: @@ -1252,13 +1252,13 @@ PipelineModel.load("/tmp/explain_document_dl_en_2.0.2_2.4_1556530585689/") Need more **examples**? Check out our dedicated [Spark NLP Examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples) repository to showcase all Spark NLP use cases! -Also, don't forget to check [Spark NLP in Action](https://nlp.johnsnowlabs.com/demo) built by Streamlit. +Also, don't forget to check [Spark NLP in Action](https://sparknlp.org/demo) built by Streamlit. ### All examples: [spark-nlp/examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples) ## FAQ -[Check our Articles and Videos page here](https://nlp.johnsnowlabs.com/learn) +[Check our Articles and Videos page here](https://sparknlp.org/learn) ## Citation diff --git a/build.sbt b/build.sbt index 3e164402d0b8..bba94df39d9e 100644 --- a/build.sbt +++ b/build.sbt @@ -54,7 +54,7 @@ publishTo := { else Some("releases" at nexus + "service/local/staging/deploy/maven2") } -homepage := Some(url("https://nlp.johnsnowlabs.com")) +homepage := Some(url("https://sparknlp.org")) scmInfo := Some( ScmInfo( diff --git a/docs/en/CPUvsGPUbenchmark.md b/docs/en/CPUvsGPUbenchmark.md index 96e3491232cf..5da2ca4984f9 100644 --- a/docs/en/CPUvsGPUbenchmark.md +++ b/docs/en/CPUvsGPUbenchmark.md @@ -51,7 +51,7 @@ Spark nodes: 1 This experiment consisted of training a Deep Learning Binary Classifier (Question vs Statement classes) at sentence-level, using a fully connected CNN and Bert Sentence Embeddings. Only 1 Spark node was usd for the training. -We used the Spark NLP class `ClassifierDL` and it's method `Approach()` as described in the [documentation](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl). +We used the Spark NLP class `ClassifierDL` and it's method `Approach()` as described in the [documentation](https://sparknlp.org/docs/en/annotators#classifierdl). The pipeline looks as follows: ![](/assets/images/gpu_v2_pic3.png) @@ -125,7 +125,7 @@ A weighted F1-score of 0.88 was achieved, with a 0.90 score for question detecti This experiment consisted of training a Name Entity Recognition model (token-level), using our class NerDLApproach(), using Bert Word Embeddings and a Char-CNN-BiLSTM Neural Network. Only 1 Spark node was used for the training. -We used the Spark NLP class `NerDL` and it's method `Approach()` as described in the [documentation](https://nlp.johnsnowlabs.com/docs/en/annotators#nerdl). +We used the Spark NLP class `NerDL` and it's method `Approach()` as described in the [documentation](https://sparknlp.org/docs/en/annotators#nerdl). The pipeline looks as follows: ![](/assets/images/gpu_v2_pic4.png) @@ -210,7 +210,7 @@ A macro F1-score of about `0.92` (`0.90` in micro) was achieved, with the follow This experiment consisted of benchmarking the improvement obtained in inference by using GPU on BertSentenceEmbeddings(). -We used the Spark NLP class `BertSentenceEmbeddings()` described in the Transformers [documentation](https://nlp.johnsnowlabs.com/docs/en/transformers#bertsentenceembeddings). +We used the Spark NLP class `BertSentenceEmbeddings()` described in the Transformers [documentation](https://sparknlp.org/docs/en/transformers#bertsentenceembeddings). The pipeline contains only two components and looks as follows: ![](/assets/images/gpu_v2_pic9.png) @@ -264,6 +264,6 @@ Right now, we don't support multigpu training (1 model in different GPUs in para
### Where to look for more information about Training -Please, take a look at the [Spark NLP](https://nlp.johnsnowlabs.com/docs/en/training) and [Spark NLP for Healthcare](https://nlp.johnsnowlabs.com/docs/en/licensed_training) Training sections, and feel free to reach us out in case you want to maximize the performance on your GPU. +Please, take a look at the [Spark NLP](https://sparknlp.org/docs/en/training) and [Spark NLP for Healthcare](https://sparknlp.org/docs/en/licensed_training) Training sections, and feel free to reach us out in case you want to maximize the performance on your GPU.
\ No newline at end of file diff --git a/docs/en/annotator_entries/ClassifierDL.md b/docs/en/annotator_entries/ClassifierDL.md index a4e468d1a7dd..7ea3a3640667 100644 --- a/docs/en/annotator_entries/ClassifierDL.md +++ b/docs/en/annotator_entries/ClassifierDL.md @@ -21,7 +21,7 @@ val classifierDL = ClassifierDLModel.pretrained() The default model is `"classifierdl_use_trec6"`, if no name is provided. It uses embeddings from the [UniversalSentenceEncoder](/docs/en/transformers#universalsentenceencoder) and is trained on the [TREC-6](https://deepai.org/dataset/trec-6#:~:text=The%20TREC%20dataset%20is%20dataset,50%20has%20finer%2Dgrained%20labels) dataset. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb) diff --git a/docs/en/annotator_entries/ContextSpellChecker.md b/docs/en/annotator_entries/ContextSpellChecker.md index e3a0a074ae5a..3fe8ea7e2cfa 100644 --- a/docs/en/annotator_entries/ContextSpellChecker.md +++ b/docs/en/annotator_entries/ContextSpellChecker.md @@ -24,7 +24,7 @@ val spellChecker = ContextSpellCheckerModel.pretrained() .setOutputCol("checked") ``` The default model is `"spellcheck_dl"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Spell+Check). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Spell+Check). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb) and the [ContextSpellCheckerTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/context/ContextSpellCheckerTestSpec.scala). diff --git a/docs/en/annotator_entries/DateMatcher.md b/docs/en/annotator_entries/DateMatcher.md index 14302090ec3b..98ab7512943d 100644 --- a/docs/en/annotator_entries/DateMatcher.md +++ b/docs/en/annotator_entries/DateMatcher.md @@ -21,7 +21,7 @@ Reads the following kind of dates: For example `"The 31st of April in the year 2008"` will be converted into `2008/04/31`. -Pretrained pipelines are available for this module, see [Pipelines](https://nlp.johnsnowlabs.com/docs/en/pipelines). +Pretrained pipelines are available for this module, see [Pipelines](https://sparknlp.org/docs/en/pipelines). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/MultiDateMatcherMultiLanguage_en.ipynb) and the [DateMatcherTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/DateMatcherTestSpec.scala). diff --git a/docs/en/annotator_entries/DependencyParser.md b/docs/en/annotator_entries/DependencyParser.md index 12b1e449bee3..b0365bac05a5 100644 --- a/docs/en/annotator_entries/DependencyParser.md +++ b/docs/en/annotator_entries/DependencyParser.md @@ -19,7 +19,7 @@ val dependencyParserApproach = DependencyParserModel.pretrained() .setOutputCol("dependency") ``` The default model is `"dependency_conllu"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb) and the [DependencyParserApproachTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/parser/dep/DependencyParserApproachTestSpec.scala). diff --git a/docs/en/annotator_entries/Doc2Vec.md b/docs/en/annotator_entries/Doc2Vec.md index 579b1626b125..ee91d5cf2e89 100644 --- a/docs/en/annotator_entries/Doc2Vec.md +++ b/docs/en/annotator_entries/Doc2Vec.md @@ -24,7 +24,7 @@ val embeddings = Doc2VecModel.pretrained() ``` The default model is `"doc2vec_gigaword_300"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models). **Sources** : diff --git a/docs/en/annotator_entries/LanguageDetectorDL.md b/docs/en/annotator_entries/LanguageDetectorDL.md index 0168f3926d8c..22242221053b 100644 --- a/docs/en/annotator_entries/LanguageDetectorDL.md +++ b/docs/en/annotator_entries/LanguageDetectorDL.md @@ -19,7 +19,7 @@ Val languageDetector = LanguageDetectorDL.pretrained() ``` The default model is `"ld_wiki_tatoeba_cnn_21"`, default language is `"xx"` (meaning multi-lingual), if no values are provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Language+Detection). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Language+Detection). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/language-detection/Language_Detection_and_Indentification.ipynb) And the [LanguageDetectorDLTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/ld/dl/LanguageDetectorDLTestSpec.scala). diff --git a/docs/en/annotator_entries/Lemmatizer.md b/docs/en/annotator_entries/Lemmatizer.md index 4e0a9fc37236..8933dec26ca8 100644 --- a/docs/en/annotator_entries/Lemmatizer.md +++ b/docs/en/annotator_entries/Lemmatizer.md @@ -4,7 +4,7 @@ Lemmatizer {%- capture model_description -%} Instantiated Model of the Lemmatizer. For usage and examples, please see the documentation of that class. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Lemmatization). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Lemmatization). {%- endcapture -%} {%- capture model_input_anno -%} @@ -53,7 +53,7 @@ Retrieves the significant part of a word. A dictionary of predefined lemmas must The dictionary can be set as a delimited text file. Pretrained models can be loaded with `LemmatizerModel.pretrained`. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Lemmatization). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Lemmatization). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Train-Lemmatizer-Italian.ipynb). {%- endcapture -%} diff --git a/docs/en/annotator_entries/MultiClassifierDL.md b/docs/en/annotator_entries/MultiClassifierDL.md index 07ece4dfd29b..3a21c50a30c2 100644 --- a/docs/en/annotator_entries/MultiClassifierDL.md +++ b/docs/en/annotator_entries/MultiClassifierDL.md @@ -24,7 +24,7 @@ The default model is `"multiclassifierdl_use_toxic"`, if no name is provided. It [UniversalSentenceEncoder](/docs/en/transformers#universalsentenceencoder) and classifies toxic comments. The data is based on the [Jigsaw Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/overview). -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label diff --git a/docs/en/annotator_entries/NerCrf.md b/docs/en/annotator_entries/NerCrf.md index ce6f637409e2..3d7dca81f647 100644 --- a/docs/en/annotator_entries/NerCrf.md +++ b/docs/en/annotator_entries/NerCrf.md @@ -22,7 +22,7 @@ val nerTagger = NerCrfModel.pretrained() .setOutputCol("ner" ``` The default model is `"ner_crf"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/crf-ner/ner_dl_crf.ipynb). {%- endcapture -%} diff --git a/docs/en/annotator_entries/NerDL.md b/docs/en/annotator_entries/NerDL.md index 670db27d4fce..ded6c656cf5b 100644 --- a/docs/en/annotator_entries/NerDL.md +++ b/docs/en/annotator_entries/NerDL.md @@ -18,8 +18,8 @@ val nerModel = NerDLModel.pretrained() ``` The default model is `"ner_dl"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). -Additionally, pretrained pipelines are available for this module, see [Pipelines](https://nlp.johnsnowlabs.com/docs/en/pipelines). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). +Additionally, pretrained pipelines are available for this module, see [Pipelines](https://sparknlp.org/docs/en/pipelines). Note that some pretrained models require specific types of embeddings, depending on which they were trained on. For example, the default model `"ner_dl"` requires the diff --git a/docs/en/annotator_entries/NorvigSweeting.md b/docs/en/annotator_entries/NorvigSweeting.md index 91a12010cdd9..7a1e45ec192f 100644 --- a/docs/en/annotator_entries/NorvigSweeting.md +++ b/docs/en/annotator_entries/NorvigSweeting.md @@ -21,7 +21,7 @@ val spellChecker = NorvigSweetingModel.pretrained() .setDoubleVariants(true) ``` The default model is `"spellcheck_norvig"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Spell+Check). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Spell+Check). For extended examples of see the [NorvigSweetingTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/norvig/NorvigSweetingTestSpec.scala). diff --git a/docs/en/annotator_entries/Perceptron.md b/docs/en/annotator_entries/Perceptron.md index 6467c4df3732..8a32acf6e7ab 100644 --- a/docs/en/annotator_entries/Perceptron.md +++ b/docs/en/annotator_entries/Perceptron.md @@ -17,8 +17,8 @@ val posTagger = PerceptronModel.pretrained() ``` The default model is `"pos_anc"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Part+of+Speech+Tagging). -Additionally, pretrained pipelines are available for this module, see [Pipelines](https://nlp.johnsnowlabs.com/docs/en/pipelines). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Part+of+Speech+Tagging). +Additionally, pretrained pipelines are available for this module, see [Pipelines](https://sparknlp.org/docs/en/pipelines). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/french/Train-Perceptron-French.ipynb). {%- endcapture -%} diff --git a/docs/en/annotator_entries/RegexMatcher.md b/docs/en/annotator_entries/RegexMatcher.md index 893d0ca790f0..2a5c659938ba 100644 --- a/docs/en/annotator_entries/RegexMatcher.md +++ b/docs/en/annotator_entries/RegexMatcher.md @@ -40,7 +40,7 @@ Rules must be provided by either `setRules` (followed by `setDelimiter`) or an e To use an external file, a dictionary of predefined regular expressions must be provided with `setExternalRules`. The dictionary can be set as a delimited text file. -Pretrained pipelines are available for this module, see [Pipelines](https://nlp.johnsnowlabs.com/docs/en/pipelines). +Pretrained pipelines are available for this module, see [Pipelines](https://sparknlp.org/docs/en/pipelines). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/regex-matcher/Matching_Text_with_RegexMatcher.ipynb) and the [RegexMatcherTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/RegexMatcherTestSpec.scala). diff --git a/docs/en/annotator_entries/SentenceDetectorDL.md b/docs/en/annotator_entries/SentenceDetectorDL.md index b66c495aad8c..4c93b0b0c175 100644 --- a/docs/en/annotator_entries/SentenceDetectorDL.md +++ b/docs/en/annotator_entries/SentenceDetectorDL.md @@ -15,7 +15,7 @@ val sentenceDL = SentenceDetectorDLModel.pretrained() .setOutputCol("sentencesDL") ``` The default model is `"sentence_detector_dl"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Sentence+Detection). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Sentence+Detection). Each extracted sentence can be returned in an Array or exploded to separate rows, if `explodeSentences` is set to `true`. diff --git a/docs/en/annotator_entries/SentimentDL.md b/docs/en/annotator_entries/SentimentDL.md index 166793c57099..2e641375c6e3 100644 --- a/docs/en/annotator_entries/SentimentDL.md +++ b/docs/en/annotator_entries/SentimentDL.md @@ -19,7 +19,7 @@ val sentiment = SentimentDLModel.pretrained() ``` The default model is `"sentimentdl_use_imdb"`, if no name is provided. It is english sentiment analysis trained on the IMDB dataset. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Sentiment+Analysis). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Sentiment+Analysis). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb) and the [SentimentDLTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/SentimentDLTestSpec.scala). diff --git a/docs/en/annotator_entries/StopWordsCleaner.md b/docs/en/annotator_entries/StopWordsCleaner.md index 03e760e5bb5c..3cd3d9426257 100644 --- a/docs/en/annotator_entries/StopWordsCleaner.md +++ b/docs/en/annotator_entries/StopWordsCleaner.md @@ -17,7 +17,7 @@ val stopWords = StopWordsCleaner.pretrained() .setCaseSensitive(false) // will load the default pretrained model `"stopwords_en"`. ``` -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Stop+Words+Removal). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Stop+Words+Removal). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb) and [StopWordsCleanerTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/StopWordsCleanerTestSpec.scala). @@ -64,7 +64,7 @@ val stopWordsCleaner = new StopWordsCleaner() val stopWordsCleaner = StopWordsCleaner.pretrained() .setInputCols("token") .setOutputCol("cleanTokens") - .setCaseSensitive(false) + .setCaseSensitive(false) ``` {%- endcapture -%} diff --git a/docs/en/annotator_entries/SymmetricDelete.md b/docs/en/annotator_entries/SymmetricDelete.md index 2eb9eb269e21..991636e0a2ac 100644 --- a/docs/en/annotator_entries/SymmetricDelete.md +++ b/docs/en/annotator_entries/SymmetricDelete.md @@ -18,7 +18,7 @@ val spell = SymmetricDeleteModel.pretrained() .setOutputCol("spell") ``` The default model is `"spellcheck_sd"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Spell+Check). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Spell+Check). See [SymmetricDeleteModelTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModelTestSpec.scala) for further reference. {%- endcapture -%} diff --git a/docs/en/annotator_entries/TypedDependencyParser.md b/docs/en/annotator_entries/TypedDependencyParser.md index a260b32df309..94c21b490476 100644 --- a/docs/en/annotator_entries/TypedDependencyParser.md +++ b/docs/en/annotator_entries/TypedDependencyParser.md @@ -19,7 +19,7 @@ val typedDependencyParser = TypedDependencyParserModel.pretrained() .setOutputCol("dependency_type") ``` The default model is `"dependency_typed_conllu"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb) and the [TypedDependencyModelTestSpec](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/annotators/parser/typdep/TypedDependencyModelTestSpec.scala). diff --git a/docs/en/annotator_entries/Word2Vec.md b/docs/en/annotator_entries/Word2Vec.md index 54411cefb44c..e63177ed501a 100644 --- a/docs/en/annotator_entries/Word2Vec.md +++ b/docs/en/annotator_entries/Word2Vec.md @@ -24,7 +24,7 @@ val embeddings = Word2VecModel.pretrained() ``` The default model is `"word2vec_gigaword_300"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models). **Sources** : diff --git a/docs/en/annotator_entries/WordEmbeddings.md b/docs/en/annotator_entries/WordEmbeddings.md index d140159af55f..ee241513a7e1 100644 --- a/docs/en/annotator_entries/WordEmbeddings.md +++ b/docs/en/annotator_entries/WordEmbeddings.md @@ -14,7 +14,7 @@ val embeddings = WordEmbeddingsModel.pretrained() .setOutputCol("embeddings") ``` The default model is `"glove_100d"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). There are also two convenient functions to retrieve the embeddings coverage with respect to the transformed dataset: - `withCoverageColumn(dataset, embeddingsCol, outputCol)`: diff --git a/docs/en/annotator_entries/WordSegmenter.md b/docs/en/annotator_entries/WordSegmenter.md index 6abf58c7f003..e0e2ab771206 100644 --- a/docs/en/annotator_entries/WordSegmenter.md +++ b/docs/en/annotator_entries/WordSegmenter.md @@ -40,7 +40,7 @@ val wordSegmenter = WordSegmenterModel.pretrained() The default model is `"wordseg_pku"`, default language is `"zh"`, if no values are provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Word+Segmentation). +[Models Hub](https://sparknlp.org/models?task=Word+Segmentation). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/jupyter/annotation/chinese/word_segmentation/words_segmenter_demo.ipynb) and the [WordSegmenterTest](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/WordSegmenterTest.scala). diff --git a/docs/en/best_practices_pretrained_models.md b/docs/en/best_practices_pretrained_models.md deleted file mode 100644 index 685b198d790a..000000000000 --- a/docs/en/best_practices_pretrained_models.md +++ /dev/null @@ -1,304 +0,0 @@ ---- -layout: docs -header: true -title: Best Practices Using Pretrained Models Together -permalink: /docs/en/best_practices_pretrained_models -key: docs-concepts -modify_date: "2021-08-31" -use_language_switcher: "Python-Scala" -show_nav: true -sidebar: - nav: sparknlp-healthcare ---- - -
- -## Entity Resolver Models and Features - -In the table below, all **Entity Resolver** models, their features, appropriate **embeddings**, and AUX info are illustrated. - -For instance, features of `sbertresolve_ner_model_finder` are under **FEATURES** column and it is trained using `sbert_jsl_medium_uncased` embeddings. Auxiliary info can be found under the **AUX** column if it is present. - -*NOTE*: This table is shared just to give you a rough idea about which pretrained models can be used together. You can get better or worse performance by playing out with different models. - -
-
- - -{:.table-model-big.db} -| NO |
MODEL NAME
|
FEATURES
|
EMBEDDINGS
|
LANGUAGE
|
AUX
| -| -- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ | -------- | -------------------------------------------------------------------------------------------- | -| 1 | [sbertresolve\_ner\_model\_finder](https://nlp.johnsnowlabs.com/2021/11/24/sbertresolve_ner_model_finder_en.html) | • Maps clinical entities (NER) to the most appropriate NER model
• sbert\_jsl\_medium\_uncased embeddings
• Returns a list of pretrained NER models | sbert\_jsl\_medium\_uncased | EN | | -| 2 | [sbiobertresolve\_clinical\_abbreviation\_acronym](https://nlp.johnsnowlabs.com/2022/01/03/sbiobertresolve_clinical_abbreviation_acronym_en.html) | • Maps  clinical abbreviations and acronyms to their meanings
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 3 | [sbiobertresolve\_cpt](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_cpt_en.html) | • CPT Codes
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 4 | [sbiobertresolve\_cpt\_augmented](https://nlp.johnsnowlabs.com/2021/05/30/sbiobertresolve_cpt_augmented_en.html) | • Augmented version of sbiobertresolve\_cpt model
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 5 | [sbiobertresolve\_cpt\_procedures\_augmented](https://nlp.johnsnowlabs.com/2021/06/15/sbiobertresolve_cpt_procedures_augmented_en.html) | • Procedures to CPT Codes
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 6 | [sbiobertresolve\_cpt\_procedures\_measurements\_augmented](https://nlp.johnsnowlabs.com/2021/07/02/sbiobertresolve_cpt_procedures_measurements_augmented_en.html) | • Procedure and Measurements to CPT Codes
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 7 | [sbiobertresolve\_hcc\_augmented](https://nlp.johnsnowlabs.com/2021/05/30/sbiobertresolve_hcc_augmented_en.html) | • HCC Codes
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 8 | [sbiobertresolve\_icd10cm](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_icd10cm_en.html) | • ICD-10-CM codes
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 9 | [sbertresolve\_icd10gm](https://nlp.johnsnowlabs.com/2021/09/16/sbertresolve_icd10gm_de.html) | • German ICD10-GM Codes
• sent\_bert\_base\_cased (DE) | sent\_bert\_base\_cased (DE) | DE | | -| 10 | [sbiobertresolve\_icd10cm\_augmented](https://nlp.johnsnowlabs.com/2021/10/31/sbiobertresolve_icd10cm_augmented_en.html) | • ICD-10-CM codes
• sbiobert\_base\_cased\_mli embeddings
• Augmented version of sbiobertresolve\_icd10cm model with synonyms, four times richer. | sbiobert\_base\_cased\_mli | EN | | -| 11 | [sbiobertresolve\_icd10cm\_augmented\_billable\_hcc](https://nlp.johnsnowlabs.com/2021/11/01/sbiobertresolve_icd10cm_augmented_billable_hcc_en.html) | • ICD-10-CM codes
• sbiobert\_base\_cased\_mli embeddings
• Provides HCC information of the codes in all\_k\_aux\_labels column
• This column can be divided to get further details: billable status - hcc status - hcc score. | sbiobert\_base\_cased\_mli | EN | HCC and Billable Information | -| 12 | [sbiobertresolve\_icd10cm\_generalised](https://nlp.johnsnowlabs.com/2021/09/29/sbiobertresolve_icd10cm_generalised_en.html) | • ICD-10-CM codes up to 3 Characters (general type of injury or disease)
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 13 | [sbiobertresolve\_icd10cm\_slim\_billable\_hcc](https://nlp.johnsnowlabs.com/2021/08/26/sbiobertresolve_icd10cm_slim_billable_hcc_en.html) | • ICD-10-CM codes
• sbiobert\_base\_cased\_mli embeddings
• Slim version (synonyms having low cosine similarity to unnormalized terms are dropped)
• Provides  the official resolution text within the brackets
• Provides  HCC information of the codes in all\_k\_aux\_labels column.
• This column can be divided to get further details: billable status - hcc status - hcc score. | sbiobert\_base\_cased\_mli | EN | HCC and Billable Information | -| 14 | [sbertresolve\_icd10cm\_slim\_billable\_hcc\_med](https://nlp.johnsnowlabs.com/2021/08/26/sbertresolve_icd10cm_slim_billable_hcc_med_en.html) | • ICD-10-CM codes
• sbert\_jsl\_medium\_uncased embeddings
• Slim version (synonyms having low cosine similarity to unnormalized terms are dropped)
• Provides  the official resolution text within the brackets inside the metadata.
• Provides  HCC information of the codes in all\_k\_aux\_labels column
• This column can be divided to get further details: billable status - hcc status - hcc score. | sbert\_jsl\_medium\_uncased | EN | HCC and Billable Information | -| 15 | [sbiobertresolve\_icd10cm\_slim\_normalized](https://nlp.johnsnowlabs.com/2021/05/17/sbiobertresolve_icd10cm_slim_normalized_en.html) | • ICD-10-CM codes
• sbiobert\_base\_cased\_mli embeddings
• Slim version (synonyms having low cosine similarity to unnormalized terms are dropped)
• Provides  the official resolution text within the brackets inside the metadata. | sbiobert\_base\_cased\_mli | EN | | -| 16 | [sbiobertresolve\_icd10pcs](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_icd10pcs_en.html) | • ICD-10-PCS codes
• sbiobert\_base\_cased\_mli embeddings | sbiobert\_base\_cased\_mli | EN | | -| 17 | [sbiobertresolve\_icdo](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_icdo_en.html) | • ICD-O Codes (International Classification of Diseases for Oncology code)
• Provides topography codes and morphology codes comprising of Histology and Behavior codes in all\_k\_aux\_labels columns
• sbiobert\_base\_cased\_mli embeddings
• More granularity with respect to body parts | sbiobert\_base\_cased\_mli | EN | Topography codes, Morphology codes comprising of Histology and Behavior codes | -| 18 | [sbiobertresolve\_icdo\_base](https://nlp.johnsnowlabs.com/2021/07/02/sbiobertresolve_icdo_base_en.html) | • ICD-O Codes (International Classification of Diseases for Oncology code)
• Provides topography codes and morphology codes comprising of Histology and Behavior codes in all\_k\_aux\_labels columns
• sbiobert\_base\_cased\_mli embeddings
• More granularity with respect to body parts | sbiobert\_base\_cased\_mli | EN | Topography codes, Morphology codes comprising of Histology and Behavior codes | -| 19 | [sbiobertresolve\_icdo\_augmented](https://nlp.johnsnowlabs.com/2021/06/22/sbiobertresolve_icdo_augmented_en.html) | • ICD-O Codes (International Classification of Diseases for Oncology code)
• Provides topography codes and morphology codes comprising of Histology and Behavior codes in all\_k\_aux\_labels columns
• sbiobert\_base\_cased\_mli embeddings
• More granularity with respect to body parts
• Augmented using the site information coming from ICD10 and synonyms coming from SNOMED vocabularies. | sbiobert\_base\_cased\_mli | EN | Topography codes, Morphology codes comprising of Histology and Behavior codes | -| 20 | [sbiobertresolve\_rxnorm](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_rxnorm_en.html) | • RxNorm Codes for Drugs/ Ingredients
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 21 | [sbiobertresolve\_rxnorm\_augmented](https://nlp.johnsnowlabs.com/2022/01/03/sbiobertresolve_rxnorm_augmented_en.html) | • RxNorm Codes for Drugs/ Ingredients
• sbiobert\_base\_cased\_mli  embeddings
• Provides concept classes of the drugs in all\_k\_aux\_labels column.
• Augmented version of sbiobertresolve\_rxnorm model | sbiobert\_base\_cased\_mli | EN | Concept classes of the drugs | -| 22 | [sbiobertresolve\_rxnorm\_augmented\_cased](https://nlp.johnsnowlabs.com/2021/12/28/sbiobertresolve_rxnorm_augmented_cased_en.html) | • RxNorm Codes for Drugs/ Ingredients
• sbiobert\_base\_cased\_mli  embeddings
• Provides concept classes of the drugs in all\_k\_aux\_labels column.
• Cased (unlowered) concept names
• Augmented version of sbiobertresolve\_rxnorm model | sbiobert\_base\_cased\_mli | EN | Concept classes of the drugs | -| 23 | [sbiobertresolve\_rxnorm\_disposition](https://nlp.johnsnowlabs.com/2021/08/12/sbiobertresolve_rxnorm_disposition_en.html) | • RxNorm Codes for Drugs/ Ingredients
• sbiobert\_base\_cased\_mli  embeddings
• Provides dispositions of the RxNorm codes in all\_k\_aux\_labels column. | sbiobert\_base\_cased\_mli | EN | Provides dispositions of the RxNorm codes in all\_k\_aux\_labels column. | -| 24 | [sbertresolve\_rxnorm\_disposition](https://nlp.johnsnowlabs.com/2021/08/28/sbertresolve_rxnorm_disposition_en.html) | • Medication entities (like drugs/ingredients) to RxNorm codes
• Provides the dispositions of the codes in all\_k\_aux\_labels column
• sbert\_jsl\_medium\_uncased embeddings (light) | sbert\_jsl\_medium\_uncased | EN | Disposition Information | -| 25 | [sbiobertresolve\_jsl\_rxnorm\_augmented](https://nlp.johnsnowlabs.com/2021/12/27/sbiobertresolve_jsl_rxnorm_augmented_en.html) | • RxNorm Codes for Drugs/ Ingredients
• sbiobert\_jsl\_rxnorm\_cased  embeddings
• Provides concept classes of the drugs in all\_k\_aux\_labels column.
• Augmented version of sbiobertresolve\_rxnorm model | sbiobert\_jsl\_rxnorm\_cased | EN | Concept classes of the drugs | -| 26 | [sbluebertresolve\_rxnorm\_augmented\_uncased](https://nlp.johnsnowlabs.com/2021/12/28/sbluebertresolve_rxnorm_augmented_uncased_en.html) | • RxNorm Codes for Drugs/ Ingredients
• sbluebert\_base\_uncased\_mli  embeddings
• Provides concept classes of the drugs in all\_k\_aux\_labels column.
• Augmented version of sbiobertresolve\_rxnorm model | sbluebert\_base\_uncased\_mli | EN | Concept classes of the drugs | -| 27 | [sbertresolve\_jsl\_rxnorm\_augmented\_med](https://nlp.johnsnowlabs.com/2021/12/28/sbertresolve_jsl_rxnorm_augmented_med_en.html) | • RxNorm Codes for Drugs/ Ingredients
• sbert\_jsl\_medium\_rxnorm\_uncased  embeddings
• Provides concept classes of the drugs in all\_k\_aux\_labels column.
• Augmented version of sbiobertresolve\_rxnorm model | sbert\_jsl\_medium\_rxnorm\_uncased | EN | Concept classes of the drugs | -| 28 | [sbiobertresolve\_rxcui](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_rxcui_en.html) | • RxCUI Codes
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 29 | [sbluebertresolve\_loinc](https://nlp.johnsnowlabs.com/2021/04/29/sbluebertresolve_loinc_en.html) | • LOINC Codes
• sbluebert\_base\_uncased\_mli  embeddings | sbluebert\_base\_uncased\_mli | EN | | -| 30 | [sbiobertresolve\_loinc](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_loinc_en.html) | • LOINC Codes
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 31 | [sbiobertresolve\_loinc\_augmented](https://nlp.johnsnowlabs.com/2021/11/23/sbiobertresolve_loinc_augmented_en.html) | • LOINC Codes
• sbiobert\_base\_cased\_mli  embeddings
• Augmented version of sbiobertresolve\_loinc | sbiobert\_base\_cased\_mli | EN | | -| 32 | [sbiobertresolve\_loinc\_cased](https://nlp.johnsnowlabs.com/2021/12/24/sbiobertresolve_loinc_cased_en.html) | • LOINC Codes
• sbiobert\_base\_cased\_mli  embeddings
• Cased (unlowered) concept names
• Augmented version of sbiobertresolve\_loinc | sbiobert\_base\_cased\_mli | EN | | -| 33 | [sbluebertresolve\_loinc\_uncased](https://nlp.johnsnowlabs.com/2021/12/31/sbluebertresolve_loinc_uncased_en.html) | • LOINC Codes
• sbluebert\_base\_uncased\_mli  embeddings
• Uncased (lowercased) concept names
• Augmented version of sbiobertresolve\_loinc | sbluebert\_base\_uncased\_mli | EN | | -| 34 | [sbiobertresolve\_mesh](https://nlp.johnsnowlabs.com/2021/11/14/sbiobertresolve_mesh_en.html) | • MeSH Codes
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 35 | [sbiobertresolve\_ndc](https://nlp.johnsnowlabs.com/2021/11/27/sbiobertresolve_ndc_en.html) | • NDC Codes
• sbiobert\_base\_cased\_mli  embeddings
• If a drug has more than one NDC code, it returns all other codes in the all\_k\_aux\_label column | sbiobert\_base\_cased\_mli | EN | If drugs have multiple NDC code, it returns all other codes in the all\_k\_aux\_label column | -| 36 | [sbiobertresolve\_hcpcs](https://nlp.johnsnowlabs.com/2021/09/29/sbiobertresolve_hcpcs_en.html) | • HCPCS Codes
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | Domain Information | -| 37 | [sbiobertresolve\_HPO](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_HPO_en.html) | • Maps phenotypic abnormalities encountered in human diseases to Human Phenotype Ontology (HPO)
• Provides associated codes from the following vocabularies for each HPO code: - MeSH (Medical Subject Headings)- SNOMED- UMLS (Unified Medical Language System ) - ORPHA (international reference resource for information on rare diseases and orphan drugs) - OMIM (Online Mendelian Inheritance in Man) in all\_k\_aux\_labels column | sbiobert\_base\_cased\_mli | EN | SNOMED, MeSH, UMLS, ORPHA, OMIM Codes | -| 38 | [sbiobertresolve\_snomed\_auxConcepts](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_snomed_auxConcepts_en.html) | • SNOMED Codes
• sbiobert\_base\_cased\_mli  embeddings
• Capable of extracting Morph Abnormality, Procedure, Substance, Physical Object, and Body Structure concepts of Snomed codes | sbiobert\_base\_cased\_mli | EN | | -| 39 | [sbiobertresolve\_snomed\_auxConcepts\_int](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_snomed_auxConcepts_int_en.html) | • SNOMED Codes (INT Version)
• sbiobert\_base\_cased\_mli  embeddings
• Capable of extracting Morph Abnormality, Procedure, Substance, Physical Object, and Body Structure concepts of Snomed codes | sbiobert\_base\_cased\_mli | EN | | -| 40 | [sbiobertresolve\_snomed\_bodyStructure](https://nlp.johnsnowlabs.com/2021/07/08/sbiobertresolve_snomed_bodyStructure_en.html) | • SNOMED Codes for body structure
• sbiobert\_base\_cased\_mli  embeddings
• Anatomical structures to body structure SNOMED codes | sbiobert\_base\_cased\_mli | EN | | -| 41 | [sbertresolve\_snomed\_bodyStructure\_med](https://nlp.johnsnowlabs.com/2021/07/08/sbertresolve_snomed_bodyStructure_med_en.html) | • SNOMED Codes for body structure
• sbert\_jsl\_medium\_uncased  embeddings
• Anatomical structures to body structure SNOMED codes | sbert\_jsl\_medium\_uncased | EN | | -| 42 | [sbiobertresolve\_snomed\_drug](https://nlp.johnsnowlabs.com/2022/01/01/sbiobertresolve_snomed_drug_en.html) | • SNOMED Codes (drug version)
• sbiobert\_base\_cased\_mli  embeddings
• Drug entities to SNOMED codes | sbiobert\_base\_cased\_mli | EN | | -| 43 | [sbiobertresolve\_snomed\_findings](https://nlp.johnsnowlabs.com/2021/06/15/sbiobertresolve_snomed_findings_en.html) | • SNOMED Codes (CT version)
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 44 | [sbiobertresolve\_snomed\_findings\_int](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_snomed_findings_int_en.html) | • SNOMED Codes (INT version)
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 45 | [sbiobertresolve\_snomed\_findings\_aux\_concepts](https://nlp.johnsnowlabs.com/2021/07/14/sbiobertresolve_snomed_findings_aux_concepts_en.html) | • SNOMED Codes
• sbiobert\_base\_cased\_mli  embeddings
• Capable of extracting Morph Abnormality, Procedure, Substance, Physical Object, and Body Structure concepts of Snomed codes
• Both Aux and CT versions together | sbiobert\_base\_cased\_mli | EN | | -| 46 | [sbiobertresolve\_snomed\_procedures\_measurements](https://nlp.johnsnowlabs.com/2021/11/11/sbiobertresolve_snomed_procedures_measurements_en.html) | • SNOMED Codes for procedure and measurements
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 47 | [sbiobertresolve\_clinical\_snomed\_procedures\_measurements](https://nlp.johnsnowlabs.com/2021/11/15/sbiobertresolve_clinical_snomed_procedures_measurements_en.html) | • SNOMED Codes for procedure and measurements
• sbiobert\_base\_cased\_mli  embeddings | sent\_biobert\_clinical\_base\_cased | EN | | -| 48 | [sbertresolve\_snomed\_conditions](https://nlp.johnsnowlabs.com/2021/08/28/sbertresolve_snomed_conditions_en.html) | • Conditions to SNOMED Codes
• sbert\_jsl\_medium\_uncased embeddings | sbert\_jsl\_medium\_uncased | EN | | -| 49 | [robertaresolve\_snomed](https://nlp.johnsnowlabs.com/2021/11/03/robertaresolve_snomed_es.html) | • Spanish SNOMED Codes
• Roberta Clinical Word Embeddings (roberta\_base\_biomedical\_es)
• averaged with SentenceEmbeddings. | roberta\_base\_biomedical\_es | ES | | -| 50 | [sbertresolve\_snomed](https://nlp.johnsnowlabs.com/2021/09/16/sbertresolve_snomed_de.html) | • German SNOMED Codes
• sent\_bert\_base\_cased (DE) | sent\_bert\_base\_cased (DE) | DE | | -| 51 | [sbiobertresolve\_umls\_clinical\_drugs](https://nlp.johnsnowlabs.com/2021/10/11/sbiobertresolve_umls_clinical_drugs_en.html) | • UMLS CUI Codes for Clinical Drugs
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 52 | [sbiobertresolve\_umls\_disease\_syndrome](https://nlp.johnsnowlabs.com/2021/10/11/sbiobertresolve_umls_disease_syndrome_en.html) | • UMLS CUI Codes for Disease and Syndrom entities
• sbiobert\_base\_cased\_mli  embeddings | sbiobert\_base\_cased\_mli | EN | | -| 53 | [sbiobertresolve\_umls\_drug\_substance](https://nlp.johnsnowlabs.com/2021/12/06/sbiobertresolve_umls_drug_substance_en.html) | • UMLS CUI Codes for Drug and Substance entities
• sbiobert\_base\_cased\_mli  embeddings
• Clinical Drugs, Pharmacologic Substance, Antibiotic, Hazardous or Poisonous Substance to UMLS CUI Codes | sbiobert\_base\_cased\_mli | EN | | -| 54 | [sbiobertresolve\_umls\_findings](https://nlp.johnsnowlabs.com/2021/10/03/sbiobertresolve_umls_findings_en.html) | • UMLS CUI Codes for clinical entities and concepts
• sbiobert\_base\_cased\_mli  embeddings
• 4 major categories of UMLS CUI codes | sbiobert\_base\_cased\_mli | EN | | -| 55 | [sbiobertresolve\_umls\_major\_concepts](https://nlp.johnsnowlabs.com/2021/10/03/sbiobertresolve_umls_major_concepts_en.html) | • UMLS CUI Codes for clinical entities and concepts
• sbiobert\_base\_cased\_mli  embeddings
• 4 major categories (Clinical Findings, Medical Devices, Anatomical Structures, and Injuries & Poisoning terms) of UMLS CUI codes | sbiobert\_base\_cased\_mli | EN | | - | - -
-
- -## Entity Resolver Model and NER Model Pairs - -In the table below, you can find **Entity Resolver** models as well as its appropriate **NER** models and labels, that can return optimal results. - -For instance, `sbiobertresolve_hcc_augmented` resolver model must be used with `sbiobert_base_cased_mli` as **embeddings**, `ner_clinical` as **NER model**, `PROBLEM` set in **setWhiteList()**. - -*NOTE*: This table is shared just to give you a rough idea about which pretrained models can be used together. You can get better or worse performance by playing out with different models. - -
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ENTITY RESOLVER MODELSENTENCE EMBEDDINGSNER MODELNER MODEL WHITELIST LABELMERGE CHUNKS (ChunkMergeApproach)
sbiobertresolve_HPOsbiobert_base_cased_mliner_human_phenotype_gene_clinicalNo need to set whiteList
sbiobertresolve_cpt_procedures_measurements_augmentedsbiobert_base_cased_mliner_jslProcedureMerge ner_jsl and ner_measurements_clinical model chunks
ner_measurements_clinicalMeasurements
sbiobertresolve_hcc_augmentedsbiobert_base_cased_mliner_clinicalPROBLEM
sbiobertresolve_hcpcssbiobert_base_cased_mliner_jslProcedure
sbiobertresolve_icd10cm_augmented_billable_hccsbiobert_base_cased_mliner_clinicalPROBLEM
sbiobertresolve_icd10cm_generalisedsbiobert_base_cased_mliner_clinicalPROBLEM
sbiobertresolve_icd10pcssbiobert_base_cased_mliner_jslProcedure
sbiobertresolve_icdo_basesbiobert_base_cased_mliner_jslOncological
sbiobertresolve_loinc_augmentedsbiobert_base_cased_mliner_jslTest
BMI
HDL
LDL
Medical_Device
Temperature
Total_Cholesterol
Triglycerides
Blood_Pressure
sbiobertresolve_meshsbiobert_base_cased_mliner_clinicalNo need to set whiteList
sbiobertresolve_rxcuisbiobert_base_cased_mliner_posologyDRUG
sbiobertresolve_rxnorm_augmentedsbiobert_base_cased_mliner_posologyDRUG
sbiobertresolve_rxnorm_dispositionsbiobert_base_cased_mliner_posologyDRUG
sbiobertresolve_snomed_bodyStructuresbiobert_base_cased_mliner_jslDisease_Syndrome_Disorder
External_body_part_or_region		Merge ner_jsl and ner_anatomy_coarse model chunks
ner_anatomy_coarseNo need to set whiteList
sbiobertresolve_snomed_procedures_measurementssbiobert_base_cased_mliner_jslProcedure
Test
BMI
HDL
LDL
Temperature
Total_Cholesterol
Triglycerides
Blood_Pressure		Merge ner_jsl and ner_measurements_clinical model chunks
ner_measurements_clinicalMeasurements
sbiobertresolve_snomed_findingssbiobert_base_cased_mliner_clinicalNo need to set whiteList
sbiobertresolve_umls_disease_syndromesbiobert_base_cased_mliner_jslCerebrovascular_Disease
Communicable_Disease
Diabetes
Disease_Syndrome_Disorder
Heart_Disease
Hyperlipidemia
Hypertension
Injury_or_Poisoning
Kidney_Disease
Obesity
Oncological
Overweight
Psychological_Condition
Symptom
VS_Finding
ImagingFindings
EKG_Findings
sbiobertresolve_umls_clinical_drugssbiobert_base_cased_mliner_posologyDRUG
sbiobertresolve_umls_major_conceptssbiobert_base_cased_mliner_jslCerebrovascular_Disease
Communicable_Disease
Diabetes
Disease_Syndrome_Disorder
Heart_Disease
Hyperlipidemia
Hypertension
Injury_or_Poisoning
Kidney_Disease
Medical-Device
Obesity
Oncological
Overweight
Psychological_Condition
Symptom
VS_Finding
ImagingFindings
EKG_Findings
sbiobertresolve_ndcsbiobert_base_cased_mliner_posology_greedyDRUG
- -
-
- -## Relation Extraction Models and Relation Pairs Table - -In the table below, available **Relation Extraction** models, its labels, optimal **NER model**, and meaningful relation pairs are illustrated. - -For instance, `re_bodypart_proceduretest` RE model returns **(0,1)** labels (binary), works optimally with `ner_jsl` NER model, and outputs relation pairs under the **RE PAIRS** column. - -*NOTE*: This table is shared just to give you a rough idea about which pretrained models can be used together. You can get better or worse performance by playing out with different models. - -
-
- - -{:.table-model-big.db} -| NO | RE MODEL | RE MODEL LABELS | NER MODEL | RE PAIRS | -| -- | -------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| 1 | [re\_bodypart\_proceduretest](https://nlp.johnsnowlabs.com/2021/01/18/re_bodypart_proceduretest_en.html) | 0,1 | ner\_jsl | \["external\_body\_part\_or\_region-test",
 "test-external\_body\_part\_or\_region",
"internal\_organ\_or\_component-test",
"test-internal\_organ\_or\_component",
"external\_body\_part\_or\_region-procedure",
"procedure-external\_body\_part\_or\_region",
"procedure-internal\_organ\_or\_component",
"internal\_organ\_or\_component-procedure"\] | -| 2 | [re\_ade\_clinical](https://nlp.johnsnowlabs.com/2021/07/12/re_ade_clinical_en.html) | 0,1 | ner\_ade\_clinical | \["ade-drug",
 "drug-ade"\] | -| 3 | [redl\_chemprot\_biobert](https://nlp.johnsnowlabs.com/2021/07/24/redl_chemprot_biobert_en.html) | CPR:1, CPR:2, CPR:3, CPR:4, CPR:5, CPR:6, CPR:7, CPR:8, CPR:9, CPR:10 | ner\_chemprot\_clinical | \["No need to set pairs."\] | -| 4 | [re\_human\_phenotype\_gene\_clinical](https://nlp.johnsnowlabs.com/2020/09/30/re_human_phenotype_gene_clinical_en.html) | 0,1 | ner\_human\_phenotype\_gene\_clinical | \["No need to set pairs."\] | -| 5 | [re\_bodypart\_directions](https://nlp.johnsnowlabs.com/2021/01/18/re_bodypart_directions_en.html) | 0,1 | ner\_jsl | \["direction-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-direction",
"direction-internal\_organ\_or\_component",
"internal\_organ\_or\_component-direction"\] | -| 6 | [re\_bodypart\_problem](https://nlp.johnsnowlabs.com/models?q=re_bodypart_problem&task=Relation+Extraction) | 0,1 | ner\_jsl | \["internal\_organ\_or\_component-cerebrovascular\_disease", "cerebrovascular\_disease-internal\_organ\_or\_component",
"internal\_organ\_or\_component-communicable\_disease", "communicable\_disease-internal\_organ\_or\_component",
"internal\_organ\_or\_component-diabetes", "diabetes-internal\_organ\_or\_component",
"internal\_organ\_or\_component-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-internal\_organ\_or\_component",
"internal\_organ\_or\_component-ekg\_findings", "ekg\_findings-internal\_organ\_or\_component",
"internal\_organ\_or\_component-heart\_disease", "heart\_disease-internal\_organ\_or\_component",
"internal\_organ\_or\_component-hyperlipidemia", "hyperlipidemia-internal\_organ\_or\_component",
"internal\_organ\_or\_component-hypertension", "hypertension-internal\_organ\_or\_component",
"internal\_organ\_or\_component-imagingfindings", "imagingfindings-internal\_organ\_or\_component",
"internal\_organ\_or\_component-injury\_or\_poisoning", "injury\_or\_poisoning-internal\_organ\_or\_component",
"internal\_organ\_or\_component-kidney\_disease", "kidney\_disease-internal\_organ\_or\_component",
"internal\_organ\_or\_component-oncological", "oncological-internal\_organ\_or\_component",
"internal\_organ\_or\_component-psychological\_condition", "psychological\_condition-internal\_organ\_or\_component",
"internal\_organ\_or\_component-symptom", "symptom-internal\_organ\_or\_component",
"internal\_organ\_or\_component-vs\_finding", "vs\_finding-internal\_organ\_or\_component",
"external\_body\_part\_or\_region-communicable\_disease", "communicable\_disease-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-diabetes", "diabetes-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-hypertension", "hypertension-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-imagingfindings", "imagingfindings-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-injury\_or\_poisoning", "injury\_or\_poisoning-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-obesity", "obesity-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-oncological", "oncological-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-overweight", "overweight-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-symptom", "symptom-external\_body\_part\_or\_region",
"external\_body\_part\_or\_region-vs\_finding", "vs\_finding-external\_body\_part\_or\_region"\] | -| 7 | [re\_drug\_drug\_interaction\_clinical](https://nlp.johnsnowlabs.com/2020/09/03/re_drug_drug_interaction_clinical_en.html) | DDI-advise, DDI-effect, DDI-mechanism, DDI-int, DDI-false | ner\_posology | \["drug-drug"\] | -| 8 | [re\_clinical](https://nlp.johnsnowlabs.com/2020/09/24/re_clinical_en.html) | TrIP, TrWP, TrCP, TrAP, TrAP, TeRP, TeCP, PIP | ner\_clinical | \["No need to set pairs."\] | -| 9 | [re\_temporal\_events\_clinical](https://nlp.johnsnowlabs.com/2020/09/28/re_temporal_events_clinical_en.html) | AFTER, BEFORE, OVERLAP | ner\_events\_clinical | \["No need to set pairs."\] | -| 10 | [re\_temporal\_events\_enriched\_clinical](https://nlp.johnsnowlabs.com/2020/09/28/re_temporal_events_enriched_clinical_en.html) | BEFORE, AFTER, SIMULTANEOUS, BEGUN\_BY, ENDED\_BY, DURING, BEFORE\_OVERLAP | ner\_events\_clinical | \["No need to set pairs."\] | -| 11 | [re\_test\_problem\_finding](https://nlp.johnsnowlabs.com/2021/04/19/re_test_problem_finding_en.html) | 0,1 | ner\_jsl | \["test-cerebrovascular\_disease", "cerebrovascular\_disease-test",
"test-communicable\_disease", "communicable\_disease-test",
"test-diabetes", "diabetes-test",
"test-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-test",
"test-heart\_disease", "heart\_disease-test",
"test-hyperlipidemia", "hyperlipidemia-test",
"test-hypertension", "hypertension-test",
"test-injury\_or\_poisoning", "injury\_or\_poisoning-test",
"test-kidney\_disease", "kidney\_disease-test",
"test-obesity", "obesity-test",
"test-oncological", "oncological-test",
"test-psychological\_condition", "psychological\_condition-test",
"test-symptom", "symptom-test",
"ekg\_findings-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-ekg\_findings",
"ekg\_findings-heart\_disease", "heart\_disease-ekg\_findings",
"ekg\_findings-symptom", "symptom-ekg\_findings",
"imagingfindings-cerebrovascular\_disease", "cerebrovascular\_disease-imagingfindings",
"imagingfindings-communicable\_disease", "communicable\_disease-imagingfindings",
"imagingfindings-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-imagingfindings",
"imagingfindings-heart\_disease", "heart\_disease-imagingfindings",
"imagingfindings-hyperlipidemia", "hyperlipidemia-imagingfindings",
"imagingfindings-hypertension", "hypertension-imagingfindings",
"imagingfindings-injury\_or\_poisoning", "injury\_or\_poisoning-imagingfindings",
"imagingfindings-kidney\_disease", "kidney\_disease-imagingfindings",
"imagingfindings-oncological", "oncological-imagingfindings",
"imagingfindings-psychological\_condition", "psychological\_condition-imagingfindings",
"imagingfindings-symptom", "symptom-imagingfindings",
"vs\_finding-cerebrovascular\_disease", "cerebrovascular\_disease-vs\_finding",
"vs\_finding-communicable\_disease", "communicable\_disease-vs\_finding",
"vs\_finding-diabetes", "diabetes-vs\_finding",
"vs\_finding-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-vs\_finding",
"vs\_finding-heart\_disease", "heart\_disease-vs\_finding",
"vs\_finding-hyperlipidemia", "hyperlipidemia-vs\_finding",
"vs\_finding-hypertension", "hypertension-vs\_finding",
"vs\_finding-injury\_or\_poisoning", "injury\_or\_poisoning-vs\_finding",
"vs\_finding-kidney\_disease", "kidney\_disease-vs\_finding",
"vs\_finding-obesity", "obesity-vs\_finding",
"vs\_finding-oncological", "oncological-vs\_finding",
"vs\_finding-overweight", "overweight-vs\_finding",
"vs\_finding-psychological\_condition", "psychological\_condition-vs\_finding",
"vs\_finding-symptom", "symptom-vs\_finding"\] | -| 12 | [re\_test\_result\_date](https://nlp.johnsnowlabs.com/2021/02/24/re_test_result_date_en.html) | is\_finding\_of, is\_result\_of, is\_date\_of, O | ner\_jsl | \["test-test\_result", "test\_result-test",
"test-date", "date-test",
"test-imagingfindings", "imagingfindings-test",
"test-ekg\_findings", "ekg\_findings-test",
"date-test\_result", "test\_result-date",
"date-imagingfindings", "imagingfindings-date",
"date-ekg\_findings", "ekg\_findings-date"\] | -| 13 | [re\_date\_clinical](https://nlp.johnsnowlabs.com/2021/01/18/re_date_clinical_en.html) | 0,1 | ner\_jsl | \["date-admission\_discharge", "admission\_discharge-date",
"date-alcohol", "alcohol-date",
"date-allergen", "allergen-date",
"date-bmi", "bmi-date",
"date-birth\_entity", "birth\_entity-date",
"date-blood\_pressure", "blood\_pressure-date",
"date-cerebrovascular\_disease", "cerebrovascular\_disease-date",
"date-clinical\_dept", "clinical\_dept-date",
"date-communicable\_disease", "communicable\_disease-date",
"date-death\_entity", "death\_entity-date",
"date-diabetes", "diabetes-date",
"date-diet", "diet-date",
"date-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-date",
"date-drug\_brandname", "drug\_brandname-date",
"date-drug\_ingredient", "drug\_ingredient-date",
"date-ekg\_findings", "ekg\_findings-date",
"date-external\_body\_part\_or\_region", "external\_body\_part\_or\_region-date",
"date-fetus\_newborn", "fetus\_newborn-date",
"date-hdl", "hdl-date",
"date-heart\_disease", "heart\_disease-date",
"date-height", "height-date",
"date-hyperlipidemia", "hyperlipidemia-date",
"date-hypertension", "hypertension-date",
"date-imagingfindings", "imagingfindings-date",
"date-imaging\_technique", "imaging\_technique-date",
"date-injury\_or\_poisoning", "injury\_or\_poisoning-date",
"date-internal\_organ\_or\_component", "internal\_organ\_or\_component-date",
"date-kidney\_disease", "kidney\_disease-date",
"date-ldl", "ldl-date",
"date-modifier", "modifier-date",
"date-o2\_saturation", "o2\_saturation-date",
"date-obesity", "obesity-date",
"date-oncological", "oncological-date",
"date-overweight", "overweight-date",
"date-oxygen\_therapy", "oxygen\_therapy-date",
"date-pregnancy", "pregnancy-date",
"date-procedure", "procedure-date",
"date-psychological\_condition", "psychological\_condition-date",
"date-pulse", "pulse-date",
"date-respiration", "respiration-date",
"date-smoking", "smoking-date",
"date-substance", "substance-date",
"date-substance\_quantity", "substance\_quantity-date",
"date-symptom", "symptom-date",
"date-temperature", "temperature-date",
"date-test", "test-date",
"date-test\_result", "test\_result-date",
"date-total\_cholesterol", "total\_cholesterol-date",
"date-treatment", "treatment-date",
"date-triglycerides", "triglycerides-date",
"date-vs\_finding", "vs\_finding-date",
"date-vaccine", "vaccine-date",
"date-vital\_signs\_header", "vital\_signs\_header-date",
"date-weight", "weight-date",
"time-admission\_discharge", "admission\_discharge-time",
"time-alcohol", "alcohol-time",
"time-allergen", "allergen-time",
"time-bmi", "bmi-time",
"time-birth\_entity", "birth\_entity-time",
"time-blood\_pressure", "blood\_pressure-time",
"time-cerebrovascular\_disease", "cerebrovascular\_disease-time",
"time-clinical\_dept", "clinical\_dept-time",
"time-communicable\_disease", "communicable\_disease-time",
"time-death\_entity", "death\_entity-time",
"time-diabetes", "diabetes-time",
"time-diet", "diet-time",
"time-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-time",
"time-drug\_brandname", "drug\_brandname-time",
"time-drug\_ingredient", "drug\_ingredient-time",
"time-ekg\_findings", "ekg\_findings-time",
"time-external\_body\_part\_or\_region", "external\_body\_part\_or\_region-time",
"time-fetus\_newborn", "fetus\_newborn-time",
"time-hdl", "hdl-time",
"time-heart\_disease", "heart\_disease-time",
"time-height", "height-time",
"time-hyperlipidemia", "hyperlipidemia-time",
"time-hypertension", "hypertension-time",
"time-imagingfindings", "imagingfindings-time",
"time-imaging\_technique", "imaging\_technique-time",
"time-injury\_or\_poisoning", "injury\_or\_poisoning-time",
"time-internal\_organ\_or\_component", "internal\_organ\_or\_component-time",
"time-kidney\_disease", "kidney\_disease-time",
"time-ldl", "ldl-time",
"time-modifier", "modifier-time",
"time-o2\_saturation", "o2\_saturation-time",
"time-obesity", "obesity-time",
"time-oncological", "oncological-time",
"time-overweight", "overweight-time",
"time-oxygen\_therapy", "oxygen\_therapy-time",
"time-pregnancy", "pregnancy-time",
"time-procedure", "procedure-time",
"time-psychological\_condition", "psychological\_condition-time",
"time-pulse", "pulse-time",
"time-respiration", "respiration-time",
"time-smoking", "smoking-time",
"time-substance", "substance-time",
"time-substance\_quantity", "substance\_quantity-time",
"time-symptom", "symptom-time",
"time-temperature", "temperature-time",
"time-test", "test-time",
"time-test\_result", "test\_result-time",
"time-total\_cholesterol", "total\_cholesterol-time",
"time-treatment", "treatment-time",
"time-triglycerides", "triglycerides-time",
"time-vs\_finding", "vs\_finding-time",
"time-vaccine", "vaccine-time",
"time-vital\_signs\_header", "vital\_signs\_header-time",
"time-weight", "weight-time",
"relativedate-admission\_discharge", "admission\_discharge-relativedate",
"relativedate-alcohol", "alcohol-relativedate",
"relativedate-allergen", "allergen-relativedate",
"relativedate-bmi", "bmi-relativedate",
"relativedate-birth\_entity", "birth\_entity-relativedate",
"relativedate-blood\_pressure", "blood\_pressure-relativedate",
"relativedate-cerebrovascular\_disease", "cerebrovascular\_disease-relativedate",
"relativedate-clinical\_dept", "clinical\_dept-relativedate",
"relativedate-communicable\_disease", "communicable\_disease-relativedate",
"relativedate-death\_entity", "death\_entity-relativedate",
"relativedate-diabetes", "diabetes-relativedate",
"relativedate-diet", "diet-relativedate",
"relativedate-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-relativedate",
"relativedate-drug\_brandname", "drug\_brandname-relativedate",
"relativedate-drug\_ingredient", "drug\_ingredient-relativedate",
"relativedate-ekg\_findings", "ekg\_findings-relativedate",
"relativedate-external\_body\_part\_or\_region", "external\_body\_part\_or\_region-relativedate",
"relativedate-fetus\_newborn", "fetus\_newborn-relativedate",
"relativedate-hdl", "hdl-relativedate",
"relativedate-heart\_disease", "heart\_disease-relativedate",
"relativedate-height", "height-relativedate",
"relativedate-hyperlipidemia", "hyperlipidemia-relativedate",
"relativedate-hypertension", "hypertension-relativedate",
"relativedate-imagingfindings", "imagingfindings-relativedate",
"relativedate-imaging\_technique", "imaging\_technique-relativedate",
"relativedate-injury\_or\_poisoning", "injury\_or\_poisoning-relativedate",
"relativedate-internal\_organ\_or\_component", "internal\_organ\_or\_component-relativedate",
"relativedate-kidney\_disease", "kidney\_disease-relativedate",
"relativedate-ldl", "ldl-relativedate",
"relativedate-modifier", "modifier-relativedate",
"relativedate-o2\_saturation", "o2\_saturation-relativedate",
"relativedate-obesity", "obesity-relativedate",
"relativedate-oncological", "oncological-relativedate",
"relativedate-overweight", "overweight-relativedate",
"relativedate-oxygen\_therapy", "oxygen\_therapy-relativedate",
"relativedate-pregnancy", "pregnancy-relativedate",
"relativedate-procedure", "procedure-relativedate",
"relativedate-psychological\_condition", "psychological\_condition-relativedate",
"relativedate-pulse", "pulse-relativedate",
"relativedate-respiration", "respiration-relativedate",
"relativedate-smoking", "smoking-relativedate",
"relativedate-substance", "substance-relativedate",
"relativedate-substance\_quantity", "substance\_quantity-relativedate",
"relativedate-symptom", "symptom-relativedate",
"relativedate-temperature", "temperature-relativedate",
"relativedate-test", "test-relativedate",
"relativedate-test\_result", "test\_result-relativedate",
"relativedate-total\_cholesterol", "total\_cholesterol-relativedate",
"relativedate-treatment", "treatment-relativedate",
"relativedate-triglycerides", "triglycerides-relativedate",
"relativedate-vs\_finding", "vs\_finding-relativedate",
"relativedate-vaccine", "vaccine-relativedate",
"relativedate-vital\_signs\_header", "vital\_signs\_header-relativedate",
"relativedate-weight", "weight-relativedate",
"relativetime-admission\_discharge", "admission\_discharge-relativetime",
"relativetime-alcohol", "alcohol-relativetime",
"relativetime-allergen", "allergen-relativetime",
"relativetime-bmi", "bmi-relativetime",
"relativetime-birth\_entity", "birth\_entity-relativetime",
"relativetime-blood\_pressure", "blood\_pressure-relativetime",
"relativetime-cerebrovascular\_disease", "cerebrovascular\_disease-relativetime",
"relativetime-clinical\_dept", "clinical\_dept-relativetime",
"relativetime-communicable\_disease", "communicable\_disease-relativetime",
"relativetime-death\_entity", "death\_entity-relativetime",
"relativetime-diabetes", "diabetes-relativetime",
"relativetime-diet", "diet-relativetime",
"relativetime-disease\_syndrome\_disorder", "disease\_syndrome\_disorder-relativetime",
"relativetime-drug\_brandname", "drug\_brandname-relativetime",
"relativetime-drug\_ingredient", "drug\_ingredient-relativetime",
"relativetime-ekg\_findings", "ekg\_findings-relativetime",
"relativetime-external\_body\_part\_or\_region", "external\_body\_part\_or\_region-relativetime",
"relativetime-fetus\_newborn", "fetus\_newborn-relativetime",
"relativetime-hdl", "hdl-relativetime",
"relativetime-heart\_disease", "heart\_disease-relativetime",
"relativetime-height", "height-relativetime",
"relativetime-hyperlipidemia", "hyperlipidemia-relativetime",
"relativetime-hypertension", "hypertension-relativetime",
"relativetime-imagingfindings", "imagingfindings-relativetime",
"relativetime-imaging\_technique", "imaging\_technique-relativetime",
"relativetime-injury\_or\_poisoning", "injury\_or\_poisoning-relativetime",
"relativetime-internal\_organ\_or\_component", "internal\_organ\_or\_component-relativetime",
"relativetime-kidney\_disease", "kidney\_disease-relativetime",
"relativetime-ldl", "ldl-relativetime",
"relativetime-modifier", "modifier-relativetime",
"relativetime-o2\_saturation", "o2\_saturation-relativetime",
"relativetime-obesity", "obesity-relativetime",
"relativetime-oncological", "oncological-relativetime",
"relativetime-overweight", "overweight-relativetime",
"relativetime-oxygen\_therapy", "oxygen\_therapy-relativetime",
"relativetime-pregnancy", "pregnancy-relativetime",
"relativetime-procedure", "procedure-relativetime",
"relativetime-psychological\_condition", "psychological\_condition-relativetime",
"relativetime-pulse", "pulse-relativetime",
"relativetime-respiration", "respiration-relativetime",
"relativetime-smoking", "smoking-relativetime",
"relativetime-substance", "substance-relativetime",
"relativetime-substance\_quantity", "substance\_quantity-relativetime",
"relativetime-symptom", "symptom-relativetime",
"relativetime-temperature", "temperature-relativetime",
"relativetime-test", "test-relativetime",
"relativetime-test\_result", "test\_result-relativetime",
"relativetime-total\_cholesterol", "total\_cholesterol-relativetime",
"relativetime-treatment", "treatment-relativetime",
"relativetime-triglycerides", "triglycerides-relativetime",
"relativetime-vs\_finding", "vs\_finding-relativetime",
"relativetime-vaccine", "vaccine-relativetime",
"relativetime-vital\_signs\_header", "vital\_signs\_header-relativetime",
"relativetime-weight", "weight-relativetime"\] | -| 14 | [redl\_drugprot\_biobert ](https://nlp.johnsnowlabs.com/2022/01/05/redl_drugprot_biobert_en.html) | INHIBITOR, DIRECT-REGULATOR, SUBSTRATE, ACTIVATOR, INDIRECT-UPREGULATOR, INDIRECT-DOWNREGULATOR, ANTAGONIST, PRODUCT-OF, PART-OF, AGONIST | ner\_drugprot\_clinical | \["checmical-gene", "chemical-gene\_and\_chemical", "gene\_and\_chemical-gene"\] | -| 15 | [re\_drugprot\_clinical ](https://nlp.johnsnowlabs.com/2022/01/05/re_drugprot_clinical_en.html) | INHIBITOR, DIRECT-REGULATOR, SUBSTRATE, ACTIVATOR, INDIRECT-UPREGULATOR, INDIRECT-DOWNREGULATOR, ANTAGONIST, PRODUCT-OF, PART-OF, AGONIST | ner\_drugprot\_clinical | \["checmical-gene", "chemical-gene\_and\_chemical", "gene\_and\_chemical-gene"\] | -| 16 | [redl\_nihss\_biobert](https://nlp.johnsnowlabs.com/2021/11/16/redl_nihss_biobert_en.html) | Has\_Value, 0 | ner\_nihss | \["No need to set pairs."\] | - -
diff --git a/docs/en/display.md b/docs/en/display.md index b99fbd0e2cdc..4391cdc96077 100644 --- a/docs/en/display.md +++ b/docs/en/display.md @@ -43,7 +43,7 @@ A complete guideline on how to use the Spark NLP Display library is available DependencyParserApproach you can use the following code. +For visualizing a dependency trees generated with DependencyParserApproach you can use the following code. ```bash diff --git a/docs/en/graph.md b/docs/en/graph.md index aef11382b1a5..cda4287ee4f4 100644 --- a/docs/en/graph.md +++ b/docs/en/graph.md @@ -31,7 +31,7 @@ All of these graphs use an LSTM of size 128 and number of chars 100 In case, your train dataset has a different number of tags, embeddings dimension, number of chars and LSTM size combinations shown in the table above, `NerDLApproach` will raise an **IllegalArgumentException** exception during runtime with the message below: -*Graph [parameter] should be [value]: Could not find a suitable tensorflow graph for embeddings dim: [value] tags: [value] nChars: [value]. Check https://nlp.johnsnowlabs.com/docs/en/graph for instructions to generate the required graph.* +*Graph [parameter] should be [value]: Could not find a suitable tensorflow graph for embeddings dim: [value] tags: [value] nChars: [value]. Check https://sparknlp.org/docs/en/graph for instructions to generate the required graph.* To overcome this exception message we have to follow these steps: diff --git a/docs/en/hardware_acceleration.md b/docs/en/hardware_acceleration.md index 4a93b374e2b5..6ebe1d2c44b4 100644 --- a/docs/en/hardware_acceleration.md +++ b/docs/en/hardware_acceleration.md @@ -28,7 +28,7 @@ The following benchmarks have been done by using a single Dell Server with the f ### GPU -Perhaps the best and the easiest way in Spark NLP to massively improve a DL-based task(s) is to use GPU. Spark NLP comes with a zero-code change feature to run seamlessly on both CPU and GPU by simply enabling GPU via `sparknlp.start(gpu=True)` or using directly the Maven package that is for GPU `spark-nlp-gpu`. ([more details](https://nlp.johnsnowlabs.com/docs/en/install)) +Perhaps the best and the easiest way in Spark NLP to massively improve a DL-based task(s) is to use GPU. Spark NLP comes with a zero-code change feature to run seamlessly on both CPU and GPU by simply enabling GPU via `sparknlp.start(gpu=True)` or using directly the Maven package that is for GPU `spark-nlp-gpu`. ([more details](https://sparknlp.org/docs/en/install)) Since the new Transformer models such as BERT for Word and Sentence embeddings are the most computationally available downstream tasks in Spark NLP, we will show a benchmark for inference (prediction) to compare CPU (without any accelerations) to GPU: diff --git a/docs/en/install.md b/docs/en/install.md index 4de942cecae4..0a777d03bef0 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -756,7 +756,7 @@ Either create a conda env for python 3.6, install *pyspark==3.3.1 spark-nlp nump Spark NLP library and all the pre-trained models/pipelines can be used entirely offline with no access to the Internet. If you are behind a proxy or a firewall with no access to the Maven repository (to download packages) or/and no access to S3 (to automatically download models and pipelines), you can simply follow the instructions to have Spark NLP without any limitations offline: - Instead of using the Maven package, you need to load our Fat JAR -- Instead of using PretrainedPipeline for pretrained pipelines or the `.pretrained()` function to download pretrained models, you will need to manually download your pipeline/model from [Models Hub](https://nlp.johnsnowlabs.com/models), extract it, and load it. +- Instead of using PretrainedPipeline for pretrained pipelines or the `.pretrained()` function to download pretrained models, you will need to manually download your pipeline/model from [Models Hub](https://sparknlp.org/models), extract it, and load it. Example of `SparkSession` with Fat JAR to have Spark NLP offline: diff --git a/docs/en/mlflow.md b/docs/en/mlflow.md index 834bfba7c18e..31156de33bb1 100644 --- a/docs/en/mlflow.md +++ b/docs/en/mlflow.md @@ -62,7 +62,7 @@ We will also need MLFlow (this example was tested with version 1.21.0) `!pip install mlflow` -Finally, make sure you follow the Spark NLP installation, available [here](https://nlp.johnsnowlabs.com/docs/en/install#python) +Finally, make sure you follow the Spark NLP installation, available [here](https://sparknlp.org/docs/en/install#python)
diff --git a/docs/en/nlp_server/nlp_server_versions/release_notes_0_4_0.md b/docs/en/nlp_server/nlp_server_versions/release_notes_0_4_0.md index 73c32e6b0350..e1d987a9ec15 100644 --- a/docs/en/nlp_server/nlp_server_versions/release_notes_0_4_0.md +++ b/docs/en/nlp_server/nlp_server_versions/release_notes_0_4_0.md @@ -17,7 +17,7 @@ sidebar: ### Highlights -- This version of NLP Server offers support for licensed models and annotators. Users can now upload a Spark NLP for Healthcare license file and get access to a wide range of additional [annotators and transformers](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators). A valid license key also gives access to more than [400 state-of-the-art healthcare models](https://nlp.johnsnowlabs.com/models?edition=Spark+NLP+for+Healthcare). Those can be used via easy to learn NLU spells or via API calls. +- This version of NLP Server offers support for licensed models and annotators. Users can now upload a Spark NLP for Healthcare license file and get access to a wide range of additional [annotators and transformers](https://sparknlp.org/docs/en/licensed_annotators). A valid license key also gives access to more than [400 state-of-the-art healthcare models](https://sparknlp.org/models?edition=Spark+NLP+for+Healthcare). Those can be used via easy to learn NLU spells or via API calls. - NLP Server now supports better handling of large amounts of data to quickly analyze via UI by offering support for uploading CSV files. - Support for floating licenses. Users can now take advantage of the floating license flexibility and use those inside of the NLP Server. diff --git a/docs/en/pipelines.md b/docs/en/pipelines.md index a22a7d9051bd..4322c4804d36 100644 --- a/docs/en/pipelines.md +++ b/docs/en/pipelines.md @@ -13,7 +13,7 @@ sidebar: Pretrained Pipelines have moved to Models Hub. Please follow this link for the updated list of all models and pipelines: -[Models Hub](https://nlp.johnsnowlabs.com/modelss) +[Models Hub](https://sparknlp.org/modelss) {:.success} {:.success} diff --git a/docs/en/production-readiness.md b/docs/en/production-readiness.md deleted file mode 100644 index 4b4ad6a3c0a5..000000000000 --- a/docs/en/production-readiness.md +++ /dev/null @@ -1,860 +0,0 @@ ---- -layout: docs -header: true -seotitle: Spark NLP - Production -title: Productionizing Spark NLP -permalink: /docs/en/production-readiness -key: docs-experiment_tracking -modify_date: "2021-11-21" -show_nav: true -sidebar: - nav: sparknlp ---- - -
- -## Productionizing Spark NLP in Databricks -This documentation page will describe how to use Databricks to run Spark NLP Pipelines for production purposes. - -
- -## About Databricks -Databricks is an enterprise software company founded by the creators of Apache Spark. The company has also created MLflow, the Serialization and Experiment tracking library you can use (inside or outside databricks), as described in the section "Experiment Tracking". - -Databricks develops a web-based platform for working with Spark, that provides automated cluster management and IPython-style notebooks. Their infrastructured is provided for training and production purposes, and is integrated in cloud platforms as Azure and AWS. - -Spark NLP is a proud partner of Databricks and we offer a seamless integration with them - see [Install on Databricks](https://www.johnsnowlabs.com/databricks/). All Spark NLP capabilities run in Databricks, including MLFlow serialization and Experiment tracking, what can be used for serving Spark NLP for production purposes. - -
- -## About MLFlow -MLFlow is a serialization and Experiment Tracking platform, which also natively suports Spark NLP. We have a documentation entry about MLFlow in the "Experiment Tracking" section. It's highly recommended that you take a look before moving forward in this document, since we will use some of the concepts explained there. - -We will use MLFlow serialization to serve our Spark NLP models. - -
- -## Creating a cluster in Databricks -As mentioned before, Spark NLP offers a seamless integration with Databricks. To create a cluster, please follow the instructions in [Install on Databricks](https://www.johnsnowlabs.com/databricks/). - -That cluster can be then replicated (cloned) for production purposes later on. - -
- -## Configuring Databricks for Spark NLP and MLFlow -In `Databricks Runtime Version`, select any **Standard** runtime, **not ML** ones.. These ones add their version of MLFlow, and some incompatibilities may arise. For this example, we have used `8.3 (includes Apache Spark 3.1.1, Scala 2.12)` - -The cluster instantiated is prepared to use Spark NLP, but to make it production-ready using MLFlow, we need to add the MLFlow jar, in addition to the Spark NLP jar, as shown in the "Experiment Tracking" section. - -In that case, we did it instantiating adding both jars (`"spark.jars.packages":" com.johnsnowlabs.nlp:spark-nlp_2.12:3.3.2,org.mlflow:mlflow-spark:1.21.0"`) into the SparkSession. However, in Databricks, you don't instantiate programatically a session, but you configure it in the Compute screen, selecting your Spark NLP cluster, and then going to `Configuration -> Advanced Options -> Sparl -> Spark Config`, as shown in the following image: - -![](/assets/images/production-readiness/db1.png) - -In addition to Spark Config, we need to add the Spark NLP and MLFlow libraries to the Cluster. You can do that by going to `Libraries` inside your cluster. Make sure you have `spark-nlp` and `mlflow`. If not, you can install them either using PyPI or Maven artifacts. In the image below you can see the PyPI alternative: - -![](/assets/images/production-readiness/db2.png) - -
- -## Creating a notebook -You are ready to create a notebook in Databricks and attach it to the recently created cluster. To do that, go to `Create - Notebook`, and select the cluster you want in the dropdown above your notebook. Make sure you have selected the cluster with the right Spark NLP + MLFlow configuration. - -To check everything is ok, run the following lines: -1) To check the session is running: -``` -spark -``` -2) To check jars are in the session: -``` -spark.sparkContext.getConf().get('spark.jars.packages') -``` - -You should see the following output from the last line (versions may differ depending on which ones you used to configure your cluster) -``` -Out[2]: 'com.johnsnowlabs.nlp:spark-nlp_2.12:3.3.2,org.mlflow:mlflow-spark:1.21.0' -``` - -
- -## Logging the experiment in Databricks using MLFlow -As explained in the "Experiment Tracking" section, MLFlow can log Spark MLLib / NLP Pipelines as experiments, to carry out runs on them, track versions, etc. - -MLFlow is natively integrated in Databricks, so we can leverage the `mlflow.spark.log_model()` function of the Spark flavour of MLFlow, to start tracking our Spark NLP pipelines. - -Let's first import our libraries... -``` -import mlflow -import sparknlp -from sparknlp.base import * -from sparknlp.annotator import * -from pyspark.ml import Pipeline -import pandas as pd -from sparknlp.training import CoNLL -import pyspark -from pyspark.sql import SparkSession -``` - -Then, create a Lemmatization pipeline: -``` -documentAssembler = DocumentAssembler()\ - .setInputCol("text")\ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols(["document"]) \ - .setOutputCol("token") - -lemmatizer = LemmatizerModel.pretrained() \ - .setInputCols(["token"]) \ - .setOutputCol("prediction") # It's mandatory to call it prediction - -pipeline = Pipeline(stages=[ - documentAssembler, - tokenizer, - lemmatizer - ]) -``` - -IMPORTANT: Last output column of the last component in the pipeline should be called **prediction**. - -Finally, let's log the experiment. In the "Experiment Tracking" section, we used the `pip_requirements` parameter in the `log_model()` function to set the required libraries: - -![](/assets/images/production-readiness/db5.png) - -But we mentioned using conda is also available. Let's use conda in this example: - -``` -conda_env = { - 'channels': ['conda-forge'], - 'dependencies': [ - 'python=3.8.8', - { - "pip": [ - 'pyspark==3.1.1', - 'mlflow==1.21.0', - 'spark-nlp==3.3.2' - ] - } - ], - 'name': 'mlflow-env' -} -``` - -With this conda environment, we are ready to log our pipeline: - -``` -mlflow.spark.log_model(p_model, "lemmatizer", conda_env=conda_env) -``` - -You should see an output similar to this one: -``` -(6) Spark Jobs -(1) MLflow run -Logged 1 run to an experiment in MLflow. Learn more -``` - -
- -## Experiment UI -On the top right corner of your notebook, you will see the Experiment widget, and inside, as shown in the image below. - -![](/assets/images/production-readiness/db3.png) - -You can also access Experiments UI if you switch your environment from "Data Science & Engineering" to "Machine Learning", on the left panel... - -![](/assets/images/production-readiness/db4.png) - - -... or clicking on the "experiment" word in the cell output (it's a link!) - -Once in the experiment UI, you will see the following screen, where your experiments are tracked. - -![](/assets/images/production-readiness/MicrosoftTeams-image.png) - -If you click on the Start Time cell of your experiment, you will reach the registered MLFlow run. - -![](/assets/images/production-readiness/db7.png) - -On the left panel you will see the MLFlow model and some other artifacts, as the `conda.yml` and `pip_requirements.txt` that manage the dependencies of your models. - -On the right panel, you will see two snippets, about how to call to the model for inference internally from Databricks. - -1) Snippet for calling with a Pandas Dataframe: -``` -import mlflow -logged_model = 'runs:/a8cf070528564792bbf66d82211db0a0/lemmatizer' - -# Load model as a Spark UDF. -loaded_model = mlflow.pyfunc.spark_udf(spark, model_uri=logged_model) - -# Predict on a Spark DataFrame. -columns = list(df.columns) -df.withColumn('predictions', loaded_model(*columns)).collect() -``` - -2) Snippet for calling with a Spark Dataframe. We won't include it in this documentation because that snippet does not include SPark NLP specificities. To make it work, the correct snippet should be: -``` -import mlflow -logged_model = 'runs:/a8cf070528564792bbf66d82211db0a0/lemmatizer' - -loaded_model = mlflow.pyfunc.load_model(model_uri=logged_model) - -# Predict on a Spark DataFrame. -res_spark = loaded_model.predict(df_1_spark.rdd) -``` - -IMPORTANT: You will only get the last column (`prediction`) results, which is a list of Rows of Annotation Types. To convert the result list into a Spark Dataframe, use the following schema: - -``` -import pyspark.sql.types as T -import pyspark.sql.functions as f - -annotationType = T.StructType([ - T.StructField('annotatorType', T.StringType(), False), - T.StructField('begin', T.IntegerType(), False), - T.StructField('end', T.IntegerType(), False), - T.StructField('result', T.StringType(), False), - T.StructField('metadata', T.MapType(T.StringType(), T.StringType()), False), - T.StructField('embeddings', T.ArrayType(T.FloatType()), False) - ]) -``` - -And then, get the results (for example, in `res_spark`) and apply the schema: - -``` -spark_res = spark.createDataFrame(res_pandas[0], schema=annotationType) -``` - -
- -## Calling the experiment for production purposes - -### 1. Internally, if the data is in Databricks -If your data lies in Datalake, in Spark Tables, or any other internal storage in Databricks, you just need to use the previous snippets (depending if you want to use Pandas or Spark Dataframes), and you are ready to go. Example for Spark Dataframes: - -![](/assets/images/production-readiness/db8.png) - -Try to use Spark Dataframes by default, since converting from Spark Dataframes into Pandas triggers a `collect()` first, removing all the parallelism capabilities of Spark Dataframes. - -The next logical step is to create Notebooks to be called programatically using the snippets above, running into production clusters. There are two ways to do this: using Batch Inference or using Jobs. - -### 2. Internally, using Batch Inference (with Spark Tables) -If we come back to the experiment ui, you will see, above the Pandas and Spark snippets, a button with the text "Register Model". If you do that, you will register the experiment to be called externally, either for Batch Inference or with a REST API (we will get there!). - -After clicking the Register Model button, you will see a link instead of the button, that will enabled after some seconds. By clicking that link, you will be redirected to the Model Inference screen. - -![](/assets/images/production-readiness/db10.png) - -This new screen has a button on the top right, that says "Use model for inference". By clicking on it, you will see two options: Batch Inference or REST API. Batch inference requires a Spark Table for input, and another for output, and after configuring them, what you will see is an auto-generated notebook to be executed on-demand, programatically or with crons, that is prepared to load the environment and do the inference, getting the text fron the input table and storing the results in the output table. - -This is an example of how the notebook looks like: - -![](/assets/images/production-readiness/db9.png) - -### 3. Externally, with the MLFlow Serve REST API -Instead of chosing a Batch Inference, you can select REST API. This will lead you to another screen, when the model will be loaded for production purposes in an independent cluster. Once deployed, you will be able to: -1) Check the endpoint URL to consume the model externally; -2) Test the endpoint writing a json (in our example, 'text' is our first input col of the pipeline, so it shoud look similar to: -``` -{"text": "This is a test of how the lemmatizer works"} -``` -You can see the response in the same screen. -3) Check what is the Python code or cURL command to do that very same thing programatically. - -![](/assets/images/production-readiness/db12.gif) - -By just using that Python code, you can already consume it for production purposes from any external web app. - -IMPORTANT: As per 26/11/2021, there is an issue being studied by Databricks team, regarding the creation on the fly of job clusters to serve MLFlow models. There is not a way to configure the Spark Session, so the jars are not loaded and the model fails to start. This will be fixed in later versions of Databricks. In the meantime, see a workaround in point 4. - -### 4. Databricks Jobs asynchronous REST API -#### Creating the notebook for the job -And last, but not least, another approach to consume models for production purposes. the Jobs API. - -Databricks has its own API for managing jobs, that allows you to instantiate any notebook or script as a job, run it, stop it, and manage all the life cycle. And you can configure the cluster where this job will run before hand, what prevents having the issue described in point 3. - -To do that: -1) Create a new production cluster, as described before, cloning you training environment but adapting it to your needs for production purposes. Make sure the Spark Config is right, as described at the beginning of this documentation. -2) Create a new notebook. -Always check that the jars are in the session: - -``` -spark.sparkContext.getConf().get('spark.jars.packages') -``` -3) Add the Spark NLP imports. -``` -import mlflow -import sparknlp -from sparknlp.base import * -from sparknlp.annotator import * -from pyspark.ml import Pipeline -import pandas as pd -from sparknlp.training import CoNLL -import pyspark -from pyspark.sql import SparkSession -import pyspark.sql.types as T -import pyspark.sql.functions as f -import json -``` -4) Let's define that an input param called `text` will be sent in the request. Let's get the text from that parameter using `dbutils`. -``` -input = "" -try: - input = dbutils.widgets.get("text") - print('"text" input found: ' + input) -except: - print('Unable to run: dbutils.widgets.get("text"). Setting it to NOT_SET') - input = "NOT_SET" -``` - -Right now, the input text will be in `input` var. You can trigger an exception or set the input to some default value if the parameter does not come in the request. - -5) Let's create a Spark Dataframe with the input -``` -df = spark.createDataFrame([[input]]).toDF('text') -``` - -6) And now, we just need to use the snippet for Spark Dataframe to consume MLFlow models, described above: -``` -import mlflow -logged_model = 'runs:/a8cf070528564792bbf66d82211db0a0/lemmatizer' - -loaded_model = mlflow.pyfunc.load_model(model_uri=logged_model) - -# Predict on a Spark DataFrame. -res_spark = loaded_model.predict(df_1_spark.rdd) - -import pyspark.sql.types as T -import pyspark.sql.functions as f - -annotationType = T.StructType([ - T.StructField('annotatorType', T.StringType(), False), - T.StructField('begin', T.IntegerType(), False), - T.StructField('end', T.IntegerType(), False), - T.StructField('result', T.StringType(), False), - T.StructField('metadata', T.MapType(T.StringType(), T.StringType()), False), - T.StructField('embeddings', T.ArrayType(T.FloatType()), False) - ]) - -spark_res = spark.createDataFrame(res_spark[0], schema=annotationType) -``` - -7) Let's transform our lemmatized tokens from the Dataframe into a list of strings: -``` -l = spark_res.select("result").collect() -txt_results = [x['result'] for x in l] -``` - -8) And finally, let's use again `dbutils` to tell Databricks to spin off the run and return an exit parameter: the list of token strings. - -``` -dbutils.notebook.exit(json.dumps({ - "status": "OK", - "results": txt_results -})) -``` -#### Configuring the job - -Last, but not least. We need to precreate the job, so that we run it from the API. We could do that using the API as well, but we will show you how to do it using the UI. - -On the left panel, go to `Jobs` and then `Create Job`. - -![](/assets/images/production-readiness/db11.png) - -In the jobs screen, you will see you job created. It's not running, it's prepared to be called on demand, programatically or in the interface, with a `text` input param. Let's see how to do that: - -#### Running the job - -1) In the jobs screen, if you click on the job, you will enter the Job screen, and be able to set your `text` input parameter and run the job manually. - -![](/assets/images/production-readiness/db13.png) - -You can use this for testing purpores, but the interesting part is calling it externally, using the Databricks Jobs API. - -2) Using the Databricks Jobs API, from for example, Postman. -``` -POST HTTP request -URL: https://[your_databricks_instance]/api/2.1/jobs/run-now -Authorization: [use Bearer Token. You can get it from Databricks, Settings, User Settings, Generate New Token.] -Body: -{ - "job_id": [job_id, check it in the Jobs screen], - "notebook_params": {"text": "This is an example of how well the lemmatizer works"} -} -``` - -As it's an asynchronous call, it will return the number a number of run, but no results. You will need to query for results using the number of the run and the following url https://[your_databricks_instance]/2.1/jobs/runs/get-output - -You will get a big json, but the most relevant info, the output, will be up to the end: - -``` -{"notebook_output": { - "status": "OK", - "results": ["This", "is", "a", "example", "of", "how", "lemmatizer", "work"] -}} -``` - -The notebook will be prepared in the job, but `idle`, until you call it programatically, what will instantiate a run. - -Check the Jobs [API](https://docs.databricks.com/dev-tools/api/latest/jobs.html) for more information about what you can do with it and how to adapt it to your solutions for production purposes. - -
-
- -## Productionizing Spark NLP using Synapse ML - -![Rest API for John Snow Labs’ Spark NLP](https://cdn-images-1.medium.com/max/2118/1*I_lIG3imZDUAkS8aM4i4yA.png) - -This is the first article of the “Serving Spark NLP via API” series, showcasing how to serve Sparkl NLP using Synapse ML and Fast API. There is another article in this series, that showcases how to serve Spark NLP using [Databricks ](https://databricks.com/)Jobs and [MLFlow ](https://mlflow.org/)Rest APIs, available here. - -
- -## Background - -[Spark NLP](https://towardsdatascience.com/introduction-to-spark-nlp-foundations-and-basic-components-part-i-c83b7629ed59) is a Natural Language Understanding Library built on top of Apache Spark, leveranging Spark MLLib pipelines, that allows you to run NLP models at scale, including SOTA Transformers. Therefore, it’s the only production-ready NLP platform that allows you to go from a simple PoC on 1 driver node, to scale to multiple nodes in a cluster, to process big amounts of data, in a matter of minutes. - -Before starting, if you want to know more about all the advantages of using Spark NLP (as the ability to work at scale on [air-gapped environments](https://nlp.johnsnowlabs.com/docs/en/install#offline), for instance) we recommend you to take a look at the following resources: - -* [John Snow Labs webpage](https://www.johnsnowlabs.com/); - -* The official [technical documentation of Spark NLP](https://nlp.johnsnowlabs.com/); - -* [Spark NLP channel on Medium](https://medium.com/spark-nlp); - -* Also, follow [Veysel Kocaman](https://vkocaman.medium.com/), Data Scientist Lead and Head of Spark NLP for Healthcare, for the latests tips. - -
- -## Motivation - -Spark NLP is server-agnostic, what means it does not come with an integrated API server, but offers a lot of options to serve NLP models using Rest APIs. - -This is first of a series of 2 articles that explain four options you can use to serve Spark NLP models via Rest API: - - 1. **Using Microsoft’s Synapse ML;** - - 2. **Using FastAPI and LightPipelines;** - - 3. Using Databricks Batch API (see Part 2/2 here); - - 4. Using MLFlow serve API in Databricks (see Part 2/2 here); - -All of them have their Strengths and weaknesses, so let’s go over them in detail. - -
- -## Microsoft’s Synapse ML - -![SynapseML serving of Spark NLP pipelines](https://cdn-images-1.medium.com/max/5120/1*kMSVrOL2fTq4AX93lIOPZQ.jpeg) - -[Synapse ML](https://microsoft.github.io/SynapseML/docs/about/) (previously named SparkMML) is, as they state in their official webpage: -> … an ecosystem of tools aimed towards expanding the distributed computing framework [Apache Spark](https://github.com/apache/spark) in several new directions. - -They offer a seamless integratation with OpenCV, LightGBM, Microsoft Cognitive Tool and, the most relevant for our use case, *Spark Serving*, an extension of *Spark Streaming *with an integrated server and a Load Balancer, that can attend multiple requests via Rest API, balance and attend them leveraging the capabilities of a Spark Cluster. That means that you can sin up a server and attend requests that will be distributed transparently over a Spark NLP cluster, in a very effortless way. - -
- -### Strengths - -* *Ready-to-use server* - -* *Includes a Load Balancer* - -* *Distributes the work over a Spark Cluster* - -* *Can be used for both Spark NLP and Spark OCR* - -
- -### Weaknesses - -* *For small use cases that don’t require big cluster processing, other approaches may be faster (as FastAPI using LightPipelines)* - -* *Requires using an external Framework* - -* *This approach does not allow you to customize your endpoints, it uses Synapse ML ones* - -
- -### How to set up Synapse ML to serve Spark NLP pipelines - -We will skip here how to install Spark NLP. If you need to do that, please follow this official webpage about how to install [Spark NLP](https://nlp.johnsnowlabs.com/docs/en/install) or, if [Spark NLP for Healthcare](https://nlp.johnsnowlabs.com/docs/en/licensed_install) if you are using the Healthcare library. - -Synapse ML recommends using at least Spark 3.2, so first of all, let’s configure the Spark Session with the required jars packages(both for Synapse ML and Spark) with the the proper Spark version (take a look at the suffix spark-nlp-spark**32**) and also, very important, add to jars.repository the Maven repository for SynapseML. - - **sparknlpjsl_jar =** "spark-nlp-jsl.jar" - - **from** pyspark.sql **import** SparkSession - - **spark =** *SparkSession***.**builder \ - **.**appName("Spark") \ - **.**master("local[*]") \ - **.***config*("spark.driver.memory", "16G") \ - **.***config*("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \ - **.***config*("spark.kryoserializer.buffer.max", "2000M") \ - **.***config*("**spark.jars.packages**", "com.microsoft.azure:synapseml_2.12:0.9.5,com.johnsnowlabs.nlp:spark-nlp-spark32_2.12:3.4.0")\ - **.***config*("**spark.jars**", sparknlpjsl_jar)\ - **.***config*("**spark.jars.repositories**", "https://mmlspark.azureedge.net/maven")\ - **.**getOrCreate() - -After the initialization, add your required imports (Spark NLP) and add to them the SynapseML-specific ones: - - **import** sparknlp - **import** sparknlp_jsl - ... - - **import** synapse.ml - **from** synapse.ml.io **import** ***** - -Now, let’s create a Spark NLP for Healthcare pipeline to carry out Entity Resolution. - - **document_assembler =** *DocumentAssembler*()\ - **.**setInputCol("text")\ - **.**setOutputCol("document") - - **sentenceDetectorDL =** *SentenceDetectorDLModel***.**pretrained("sentence_detector_dl_healthcare", "en", 'clinical/models') \ - **.**setInputCols(["document"]) \ - **.**setOutputCol("sentence") - - **tokenizer =** *Tokenizer*()\ - **.**setInputCols(["sentence"])\ - **.**setOutputCol("token") - - **word_embeddings =** *WordEmbeddingsModel***.**pretrained("embeddings_clinical", "en", "clinical/models")\ - **.**setInputCols(["sentence", "token"])\ - **.**setOutputCol("word_embeddings") - - **clinical_ner =** *MedicalNerModel***.**pretrained("ner_clinical", "en", "clinical/models") \ - **.**setInputCols(["sentence", "token", "word_embeddings"]) \ - **.**setOutputCol("ner") - - **ner_converter_icd =** *NerConverterInternal*() \ - **.**setInputCols(["sentence", "token", "ner"]) \ - **.**setOutputCol("ner_chunk")\ - **.**setWhiteList(['PROBLEM'])\ - **.**setPreservePosition(**False**) - - **c2doc =** *Chunk2Doc*()\ - **.**setInputCols("ner_chunk")\ - **.**setOutputCol("ner_chunk_doc") - - **sbert_embedder =** *BertSentenceEmbeddings***.**pretrained('sbiobert_base_cased_mli', 'en','clinical/models')\ - **.**setInputCols(["ner_chunk_doc"])\ - **.**setOutputCol("sentence_embeddings")\ - **.**setCaseSensitive(**False**) - - **icd_resolver =** *SentenceEntityResolverModel***.**pretrained("sbiobertresolve_icd10cm_augmented_billable_hcc","en", "clinical/models") \ - **.**setInputCols(["ner_chunk", "sentence_embeddings"]) \ - **.**setOutputCol("icd10cm_code")\ - **.**setDistanceFunction("EUCLIDEAN") - - **resolver_pipeline =** *Pipeline*( - stages **=** [ - document_assembler, - sentenceDetectorDL, - tokenizer, - word_embeddings, - clinical_ner, - ner_converter_icd, - c2doc, - sbert_embedder, - icd_resolver - ]) - -Let’s use a clinical note to test Synapse ML. - - **clinical_note =** """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting. Two weeks prior to presentation, she was treated with a five-day course of amoxicillin for a respiratory tract infection. She was on metformin, glipizide, and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG. She had been on dapagliflozin for six months at the time of presentation. Physical examination on presentation was significant for dry oral mucosa; significantly, her abdominal examination was benign with no tenderness, guarding, or rigidity.""" - -Since SynapseML serves a RestAPI, we will be sending JSON requests. Let’s define a simple json with the clinical note: - - **data_json =** {"*text*": clinical_note } - -Now, let’s spin up a server using Synapse ML Spark Serving. It will consist of: - - 1. a streaming server that will receive a json and transform it into a Spark Dataframe - - 2. a call to Spark NLP transform on the dataframe, using the pipeline - - 3. a write operation returning the output also in json format. - - **#1: Creating the streaming server and transforming json to Spark Dataframe** - **serving_input =** spark**.**readStream**.**server() \ - **.**address("localhost", 9999, "benchmark_api") \ - **.**option("name", "benchmark_api") \ - **.**load() \ - **.**parseRequest("benchmark_api", data**.**schema) - - - **#2: Applying transform to the dataframe using our Spark NLP pipeline - serving_output =** resolver_p_model**.**transform(serving_input) \ - **.**makeReply("icd10cm_code") - - - **#3: Returning the response in json format** - **server =** serving_output**.**writeStream \ - **.**server() \ - **.**replyTo("benchmark_api") \ - **.**queryName("benchmark_query") \ - **.**option("checkpointLocation", "file:///tmp/checkpoints-{}"**.**format(uuid**.**uuid1())) \ - **.**start() - -And we are ready to test the endpoint using the requests library. - - **import** requests - res **=** requests**.**post("http://localhost:9999/benchmark_api", data= json**.**dumps(data_json)) - -And last, but not least, let’s check the results: - - **for** i **in** range (0, len(response_list**.**json())): - print(response_list**.**json()[i]['result']) - - >>O2441 O2411 P702 K8520 B159 E669 Z6841 R35 R631 R630 R111... - -
- -## Productionizing Spark NLP using FastAPI and LightPipelines - -![Fast API serving of Spark NLP pipelines](https://cdn-images-1.medium.com/max/2046/1*du7p50wS_fIsaC_lR18qsg.png) - -[FastAPI](https://fastapi.tiangolo.com/) is, as defined by the creators… -> …a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. - -FastAPI provides with a very good latency and response times that, all along witht the good performance of Spark NLP LightPipelines, makes this option the quickest one of the four described in the article. - -Read more about the performance advantages of using *LightPipelines *in [this article](https://medium.com/spark-nlp/spark-nlp-101-lightpipeline-a544e93f20f1) created by John Snow Labs Data Scientist Lead [Veysel Kocaman](https://vkocaman.medium.com/). - -
- -### Strengths - -* *Quickest approach* - -* *Adds flexibility to build and adapt a custom API for your models* - -
- -### **Weaknesses** - -* *LightPipelines are executed sequentially and don’t leverage the distributed computation that Spark Clusters provide.* - -* *As an alternative, you can use FastAPI with default pipelines and a custom LoadBalancer, to distribute the calls over your cluster nodes.* - -You can serve SparkNLP + FastAPI on Docker. To do that, we will create a project with the following files: - -* Dockerfile: Image for creating a SparkNLP + FastAPI Docker image - -* requirements.txt: PIP Requirements - -* entrypoint.sh: Dockerfile entrypoint - -* content/: folder containing FastAPI webapp and SparkNLP keys - -* content/main.py: FastAPI webapp, entrypoint - -* content/sparknlp_keys.json: SparkNLP keys (for Healthcare or OCR) - -
- -### Dockerfile - -The aim of this file is to create a suitable Docker Image with all the OS and Python libraries required to run SparkNLP. Also, adds a entry endpoint for the FastAPI server (see below) and a main folder containing the actual code to run a pipeline on an input text and return the expected values. - - **FROM **ubuntu:18.04 - **RUN **apt-get update && apt-get -y update - - **RUN **apt-get -y update \ - && apt-get install -y wget \ - && apt-get install -y jq \ - && apt-get install -y lsb-release \ - && apt-get install -y openjdk-8-jdk-headless \ - && apt-get install -y build-essential python3-pip \ - && pip3 -q install pip --upgrade \ - && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* \ - /usr/share/man /usr/share/doc /usr/share/doc-base - - **ENV **PYSPARK_DRIVER_PYTHON=python3 - **ENV **PYSPARK_PYTHON=python3 - - **ENV **LC_ALL=C.UTF-8 - **ENV **LANG=C.UTF-8 - - **# We expose the FastAPI default port 8515** - **EXPOSE **8515 - - **# Install all Python required libraries** - **COPY **requirements.txt / - **RUN **pip install -r /requirements.txt - - **# Adds the entrypoint to the FastAPI server** - **COPY **entrypoint.sh / - **RUN **chmod +x /entrypoint.sh - - **# In /content folder we will have our main.py and the license files - COPY **./content/ /content/ - **WORKDIR **content/ - - **# We tell Docker to run this file when a container is instantiated** - **ENTRYPOINT **["/entrypoint.sh"] - -
- -### requirements.txt - -This file describes which Python libraries will be required when creating the Docker image to run Spark NLP on FastAPI. - - **pyspark**==3.1.2 - **fastapi**==0.70.1 - **uvicorn**==0.16 - **wget**==3.2 - **pandas**==1.4.1 - -
- -### entrypoint.sh - -This file is the entry point of our Docker container, which carries out the following actions: - - 1. Takes the sparknlp_keys.json and exports its values as environment variables, as required by Spark NLP for Healthcare. - - 2. Installs the proper version of Spark NLP for Healthcare, getting the values from the license keys we have just exported in the previous step. - - 3. Runs the main.py file, that will load the pipelines and create and endpoint to serve them. - - *#!/bin/bash* - - **# Load the license from sparknlp_keys.json and export the values as OS variables - ***export_json* () { - for s in $(echo $values | jq -r 'to_entries|map("\(.key)=\(.value|tostring)")|.[]' $1 ); do - export $s - done - } - - **export_json **"/content/sparknlp_keys.json" - - - **# Installs the proper version of Spark NLP for Healthcare - pip install **--upgrade spark-nlp-jsl==$JSL_VERSION --user --extra-index-url https://pypi.johnsnowlabs.com/$SECRET - - if [ $? != 0 ]; - then - exit 1 - fi - - **# Script to create FastAPI endpoints and preloading pipelines for inference - python3 **/content/main.py - -***content/main.py*: Serving 2 pipelines in a FastAPI endpoint** - -To maximize the performance and minimize the latency, we are going to store two Spark NLP pipelines in memory, so that we load only once (at server start) and we just use them everytime we get an API request to infer. - -To do this, let’s create a **content/main.py** Python script to download the required resources, store them in memory and serve them in Rest API endpoints. - -First, the import section - - **import** uvicorn, json, os - **from** fastapi **import** FastAPI - **from** sparknlp.annotator **import** ***** - **from **sparknlp_jsl.annotator **import ******* - **from** sparknlp.base **import** ***** - **import **sparknlp, sparknlp_jsl - **from **sparknlp.pretrained **import** PretrainedPipeline - - app **=** FastAPI() - pipelines **=** {} - -Then, let’s define the endpoint to serve the pipeline: - - **@app.get("/benchmark/pipeline")** - **async** **def** get_one_sequential_pipeline_result(modelname, text**=**''): - **return** pipelines[modelname]**.**annotate(text) - -Then, the startup event to preload the pipelines and start a Spark NLP Session: - - **@app.on_event("startup")** - **async** **def** startup_event(): - **with** open('/content/sparknlp_keys.json', 'r') **as** f: - license_keys **=** json**.**load(f) - - ** spark =** sparknlp_jsl**.**start(secret**=**license_keys['SECRE - - **pipelines**['ner_profiling_clinical'] **=** *PretrainedPipeline*('ner_profiling_clinical', 'en', 'clinical/models') - - **pipelines**['clinical_deidentification'] **=** *PretrainedPipeline*("clinical_deidentification", "en", "clinical/models") - -Finally, let’s run a uvicorn server, listening on port 8515 to the endpoints declared before: - - **if __name__ == "__main__":** - uvicorn**.**run('main:app', host**=**'0.0.0.0', port**=**8515) - -**content/sparknlp_keys.json** - -For using Spark NLP for Healthcare, please add your Spark NLP for Healthcare license keys to content/sparknlp_keys.jsonDThe file is ready, you only need to fulfill with your own values taken from the json file John Snow Labs has provided you with. - - { - "**AWS_ACCESS_KEY_ID**": "", - "**AWS_SECRET_ACCESS_KEY**": "", - "**SECRET**": "", - "**SPARK_NLP_LICENSE**": "", - "**JSL_VERSION**": "", - "**PUBLIC_VERSION**": "" - } - -And now, let’s run the server! - - 1. **Creating the Docker image and running the container** - - **docker build** -t johnsnowlabs/sparknlp:sparknlp_api . - - **docker run **-v jsl_keys.json:/content/sparknlp_keys.json -p 8515:8515 -it johnsnowlabs/sparknlp:sparknlp_api - -**2. Consuming the API using a Python script** - -Lets import some libraries - - **import** requests - **import** time - -Then, let’s create a clinical note - - **ner_text =** """ - *A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. - He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day.* - """ - -We have preloaded and served two Pretrained Pipelines: clinical_deidentification and ner_profiling_clinical . In *modelname*, let’s set which one we want to check - - # Change this line to execute any of the two pipelines - **modelname =** '*clinical_deidentification*' - *# modelname = 'ner_profiling_clinical'* - -And finally, let’s use the requestslibrary to send a test request to the endpoint and get the results. - - **query =** f"?modelname={modelname}&text={ner_text}" - **url =** f"http://localhost:8515/benchmark/pipeline{query}" - - **print**(requests**.**get(url)) - - >> {'sentence': ..., 'masked': ..., 'ner_chunk': ..., } - -You can also prettify the json using the following function with the result of the annotate() function: - - **def explode_annotate(ann_result):** - ''' - Function to convert result object to json - input: raw result - output: processed result dictionary - ''' - result = {} - for column, ann in ann_result[0].items(): - result[column] = [] - for lines in ann: - content = { - "result": lines.result, - "begin": lines.begin, - "end": lines.end, - "metadata": dict(lines.metadata), - } - result[column].append(content) - return result - -
diff --git a/docs/en/quickstart.md b/docs/en/quickstart.md index 04ff8a3de94c..b9492dd1711c 100644 --- a/docs/en/quickstart.md +++ b/docs/en/quickstart.md @@ -31,10 +31,10 @@ Please refer to Spark [documentation](http://spark.apache.org/docs/latest/index. Install Spark NLP in {:.btn-list} -* [Python](https://nlp.johnsnowlabs.com/docs/en/install#python) -* [Scala and Java](https://nlp.johnsnowlabs.com/docs/en/install#scala-and-java) -* [Databricks](https://nlp.johnsnowlabs.com/docs/en/install#databricks-support) -* [EMR](https://nlp.johnsnowlabs.com/docs/en/install#emr-support) +* [Python](https://sparknlp.org/docs/en/install#python) +* [Scala and Java](https://sparknlp.org/docs/en/install#scala-and-java) +* [Databricks](https://sparknlp.org/docs/en/install#databricks-support) +* [EMR](https://sparknlp.org/docs/en/install#emr-support)
diff --git a/docs/en/serving_spark_nlp_via_api_databricks_mlflow.md b/docs/en/serving_spark_nlp_via_api_databricks_mlflow.md index beb3b097c30f..d70332560e9e 100644 --- a/docs/en/serving_spark_nlp_via_api_databricks_mlflow.md +++ b/docs/en/serving_spark_nlp_via_api_databricks_mlflow.md @@ -14,18 +14,18 @@ sidebar:
This is the first article of the “Serving Spark NLP via API” series, showcasing how to serve Spark NLP using [Databricks](https://databricks.com/) Jobs and [MLFlow](https://www.mlflow.org/) Serve APIs. -You can find two more approaches (first, using FastAPI and second, using SynapseML) in the Spark NLP for Healthcare [documentation](https://nlp.johnsnowlabs.com/docs/en/licensed_install) page. +You can find two more approaches (first, using FastAPI and second, using SynapseML) in the Spark NLP for Healthcare [documentation](https://sparknlp.org/docs/en/licensed_install) page.
## Background [Spark NLP](https://towardsdatascience.com/introduction-to-spark-nlp-foundations-and-basic-components-part-i-c83b7629ed59) is a Natural Language Understanding Library built on top of Apache Spark, leveranging Spark MLLib pipelines, that allows you to run NLP models at scale, including SOTA Transformers. Therefore, it’s the only production-ready NLP platform that allows you to go from a simple PoC on 1 driver node, to scale to multiple nodes in a cluster, to process big amounts of data, in a matter of minutes. -Before starting, if you want to know more about all the advantages of using Spark NLP (as the ability to work at scale on [air-gapped environments](https://nlp.johnsnowlabs.com/docs/en/install#offline), for instance) we recommend you to take a look at the following resources: +Before starting, if you want to know more about all the advantages of using Spark NLP (as the ability to work at scale on [air-gapped environments](https://sparknlp.org/docs/en/install#offline), for instance) we recommend you to take a look at the following resources: * [John Snow Labs webpage](https://www.johnsnowlabs.com/); -* The official [technical documentation of Spark NLP](https://nlp.johnsnowlabs.com/); +* The official [technical documentation of Spark NLP](https://sparknlp.org/); * [Spark NLP channel on Medium](https://medium.com/spark-nlp); @@ -448,9 +448,9 @@ Check the Jobs [API](https://docs.databricks.com/dev-tools/api/latest/jobs.html) ## Do you want to know more? -* Check how to productionize Spark NLP in our official documentation [here](https://nlp.johnsnowlabs.com/docs/en/production-readiness) +* Check how to productionize Spark NLP in our official documentation [here](https://sparknlp.org/docs/en/production-readiness) -* Visit [John Snow Labs](https://www.johnsnowlabs.com/) and [Spark NLP Technical Documentation](https://nlp.johnsnowlabs.com/) websites +* Visit [John Snow Labs](https://www.johnsnowlabs.com/) and [Spark NLP Technical Documentation](https://sparknlp.org/) websites * Follow us on Medium: [Spark NLP](https://medium.com/spark-nlp) and [Veysel Kocaman](https://vkocaman.medium.com/) diff --git a/docs/en/spark_nlp.md b/docs/en/spark_nlp.md index b308fc05b843..64c5dfde2a6b 100644 --- a/docs/en/spark_nlp.md +++ b/docs/en/spark_nlp.md @@ -39,10 +39,10 @@ Please refer to Spark [documentation](http://spark.apache.org/docs/latest/index. Install Spark NLP in {:.btn-list} -* [Python](https://nlp.johnsnowlabs.com/docs/en/install#python) -* [Scala and Java](https://nlp.johnsnowlabs.com/docs/en/install#scala-and-java) -* [Databricks](https://nlp.johnsnowlabs.com/docs/en/install#databricks-support) -* [EMR](https://nlp.johnsnowlabs.com/docs/en/install#emr-support) +* [Python](https://sparknlp.org/docs/en/install#python) +* [Scala and Java](https://sparknlp.org/docs/en/install#scala-and-java) +* [Databricks](https://sparknlp.org/docs/en/install#databricks-support) +* [EMR](https://sparknlp.org/docs/en/install#emr-support)
diff --git a/docs/en/training.md b/docs/en/training.md index e675bfb71cc9..e3319e31096e 100644 --- a/docs/en/training.md +++ b/docs/en/training.md @@ -125,7 +125,7 @@ All of these graphs use an LSTM of size 128 and number of chars 100 In case, your train dataset has a different number of tags, embeddings dimension, number of chars and LSTM size combinations shown in the table above, `NerDLApproach` will raise an **IllegalArgumentException** exception during runtime with the message below: -*Graph [parameter] should be [value]: Could not find a suitable tensorflow graph for embeddings dim: [value] tags: [value] nChars: [value]. Check https://nlp.johnsnowlabs.com/docs/en/graph for instructions to generate the required graph.* +*Graph [parameter] should be [value]: Could not find a suitable tensorflow graph for embeddings dim: [value] tags: [value] nChars: [value]. Check https://sparknlp.org/docs/en/graph for instructions to generate the required graph.* To overcome this exception message we have to follow these steps: diff --git a/docs/en/transformer_entries/AlbertForQuestionAnswering.md b/docs/en/transformer_entries/AlbertForQuestionAnswering.md index 1b2e1554f1e0..f4838bc89784 100644 --- a/docs/en/transformer_entries/AlbertForQuestionAnswering.md +++ b/docs/en/transformer_entries/AlbertForQuestionAnswering.md @@ -16,7 +16,7 @@ val spanClassifier = AlbertForQuestionAnswering.pretrained() The default model is `"albert_base_qa_squad2"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Question+Answering). +[Models Hub](https://sparknlp.org/models?task=Question+Answering). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the diff --git a/docs/en/transformer_entries/AlbertForSequenceClassification.md b/docs/en/transformer_entries/AlbertForSequenceClassification.md index ef031ec72162..6ac4dac84cee 100644 --- a/docs/en/transformer_entries/AlbertForSequenceClassification.md +++ b/docs/en/transformer_entries/AlbertForSequenceClassification.md @@ -14,9 +14,9 @@ val sequenceClassifier = AlbertForSequenceClassification.pretrained() ``` The default model is `"albert_base_sequence_classifier_imdb"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). -Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are +Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the [AlbertForSequenceClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForSequenceClassificationTestSpec.scala). {%- endcapture -%} diff --git a/docs/en/transformer_entries/AlbertForTokenClassification.md b/docs/en/transformer_entries/AlbertForTokenClassification.md index 455ae9e56ee9..4e8775c8f734 100644 --- a/docs/en/transformer_entries/AlbertForTokenClassification.md +++ b/docs/en/transformer_entries/AlbertForTokenClassification.md @@ -14,7 +14,7 @@ val tokenClassifier = AlbertForTokenClassification.pretrained() ``` The default model is `"albert_base_token_classifier_conll03"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). and the [AlbertForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForTokenClassificationTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/BertEmbeddings.md b/docs/en/transformer_entries/BertEmbeddings.md index 80bfdbaf0067..fa1ae450dab5 100644 --- a/docs/en/transformer_entries/BertEmbeddings.md +++ b/docs/en/transformer_entries/BertEmbeddings.md @@ -14,7 +14,7 @@ val embeddings = BertEmbeddings.pretrained() ``` The default model is `"small_bert_L2_768"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_bert.ipynb) and the [BertEmbeddingsTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddingsTestSpec.scala). diff --git a/docs/en/transformer_entries/BertForQuestionAnswering.md b/docs/en/transformer_entries/BertForQuestionAnswering.md index 0b56b48007c7..23f69907cb97 100644 --- a/docs/en/transformer_entries/BertForQuestionAnswering.md +++ b/docs/en/transformer_entries/BertForQuestionAnswering.md @@ -16,7 +16,7 @@ val spanClassifier = BertForQuestionAnswering.pretrained() The default model is `"bert_base_cased_qa_squad2"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Question+Answering). +[Models Hub](https://sparknlp.org/models?task=Question+Answering). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the diff --git a/docs/en/transformer_entries/BertForSequenceClassification.md b/docs/en/transformer_entries/BertForSequenceClassification.md index 8e784e2927b9..e6482c1037b4 100644 --- a/docs/en/transformer_entries/BertForSequenceClassification.md +++ b/docs/en/transformer_entries/BertForSequenceClassification.md @@ -14,9 +14,9 @@ val sequenceClassifier = BertForSequenceClassification.pretrained() ``` The default model is `"bert_base_sequence_classifier_imdb"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). -Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are +Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the [BertForSequenceClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassificationTestSpec.scala). {%- endcapture -%} diff --git a/docs/en/transformer_entries/BertForTokenClassification.md b/docs/en/transformer_entries/BertForTokenClassification.md index a624227b6b61..8b01d73ff230 100644 --- a/docs/en/transformer_entries/BertForTokenClassification.md +++ b/docs/en/transformer_entries/BertForTokenClassification.md @@ -14,7 +14,7 @@ val labels = BertForTokenClassification.pretrained() ``` The default model is `"bert_base_token_classifier_conll03"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). and the [BertForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForTokenClassificationTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/BertSentenceEmbeddings.md b/docs/en/transformer_entries/BertSentenceEmbeddings.md index f80909dce44d..469eec7aead8 100644 --- a/docs/en/transformer_entries/BertSentenceEmbeddings.md +++ b/docs/en/transformer_entries/BertSentenceEmbeddings.md @@ -14,7 +14,7 @@ val embeddings = BertSentenceEmbeddings.pretrained() ``` The default model is `"sent_small_bert_L2_768"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb) and the [BertSentenceEmbeddingsTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertSentenceEmbeddingsTestSpec.scala). diff --git a/docs/en/transformer_entries/CamemBertEmbeddings.md b/docs/en/transformer_entries/CamemBertEmbeddings.md index cee96f986471..dd059c12ab6b 100644 --- a/docs/en/transformer_entries/CamemBertEmbeddings.md +++ b/docs/en/transformer_entries/CamemBertEmbeddings.md @@ -17,7 +17,7 @@ val embeddings = CamemBertEmbeddings.pretrained() The default model is `"camembert_base"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +[Models Hub](https://sparknlp.org/models?task=Embeddings). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_bert.ipynb) diff --git a/docs/en/transformer_entries/CamemBertForSequenceClassification.md b/docs/en/transformer_entries/CamemBertForSequenceClassification.md index 4dfa8b36d180..13eb99395274 100644 --- a/docs/en/transformer_entries/CamemBertForSequenceClassification.md +++ b/docs/en/transformer_entries/CamemBertForSequenceClassification.md @@ -18,7 +18,7 @@ val sequenceClassifier = CamemBertForSequenceClassification.pretrained() The default model is `camembert_base_sequence_classifier_allocine"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +[Models Hub](https://sparknlp.org/models?task=Text+Classification). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended diff --git a/docs/en/transformer_entries/CamemBertForTokenClassification.md b/docs/en/transformer_entries/CamemBertForTokenClassification.md index f2552d5f5253..7d8b44643331 100644 --- a/docs/en/transformer_entries/CamemBertForTokenClassification.md +++ b/docs/en/transformer_entries/CamemBertForTokenClassification.md @@ -16,7 +16,7 @@ val tokenClassifier = CamemBertForTokenClassification.pretrained() The default model is `"camembert_base_token_classifier_wikiner"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). +[Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). and the [CamemBertForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForTokenClassificationTestSpec.scala). @@ -48,7 +48,7 @@ tokenClassifier = CamemBertForTokenClassification.pretrained() \\ .setInputCols(["token", "document"]) \\ .setOutputCol("label") \\ .setCaseSensitive(True) - + pipeline = Pipeline().setStages([ documentAssembler, tokenizer, diff --git a/docs/en/transformer_entries/ConvNextForImageClassification.md b/docs/en/transformer_entries/ConvNextForImageClassification.md index 302918971619..9e664a7bb4ca 100644 --- a/docs/en/transformer_entries/ConvNextForImageClassification.md +++ b/docs/en/transformer_entries/ConvNextForImageClassification.md @@ -21,7 +21,7 @@ val imageClassifier = ConvNextForImageClassification.pretrained() The default model is `"image_classifier_convnext_tiny_224_local"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Image+Classification). +[Models Hub](https://sparknlp.org/models?task=Image+Classification). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see diff --git a/docs/en/transformer_entries/DeBertaForQuestionAnswering.md b/docs/en/transformer_entries/DeBertaForQuestionAnswering.md index 60918ac62507..b6ac208d4272 100644 --- a/docs/en/transformer_entries/DeBertaForQuestionAnswering.md +++ b/docs/en/transformer_entries/DeBertaForQuestionAnswering.md @@ -16,7 +16,7 @@ val spanClassifier = DeBertaForQuestionAnswering.pretrained() The default model is `"deverta_v3_xsmall_qa_squad2"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Question+Answering). +[Models Hub](https://sparknlp.org/models?task=Question+Answering). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the diff --git a/docs/en/transformer_entries/DistilBertEmbeddings.md b/docs/en/transformer_entries/DistilBertEmbeddings.md index 38489c00df33..8826c621fb88 100644 --- a/docs/en/transformer_entries/DistilBertEmbeddings.md +++ b/docs/en/transformer_entries/DistilBertEmbeddings.md @@ -13,7 +13,7 @@ val embeddings = DistilBertEmbeddings.pretrained() .setOutputCol("embeddings") ``` The default model is `"distilbert_base_cased"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DistilBERT.ipynb) and the [DistilBertEmbeddingsTestSpec](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/DistilBertEmbeddingsTestSpec.scala). diff --git a/docs/en/transformer_entries/DistilBertForQuestionAnswering.md b/docs/en/transformer_entries/DistilBertForQuestionAnswering.md index 123b549aa991..ee2e0a2a54fd 100644 --- a/docs/en/transformer_entries/DistilBertForQuestionAnswering.md +++ b/docs/en/transformer_entries/DistilBertForQuestionAnswering.md @@ -16,7 +16,7 @@ val spanClassifier = DistilBertForQuestionAnswering.pretrained() The default model is `"distilbert_base_cased_qa_squad2"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Question+Answering). +[Models Hub](https://sparknlp.org/models?task=Question+Answering). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the diff --git a/docs/en/transformer_entries/DistilBertForSequenceClassification.md b/docs/en/transformer_entries/DistilBertForSequenceClassification.md index 43df11c51565..bc56ccd3a61c 100644 --- a/docs/en/transformer_entries/DistilBertForSequenceClassification.md +++ b/docs/en/transformer_entries/DistilBertForSequenceClassification.md @@ -14,9 +14,9 @@ val sequenceClassifier = DistilBertForSequenceClassification.pretrained() ``` The default model is `"distilbert_base_sequence_classifier_imdb"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). -Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are +Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the [DistilBertForSequenceClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForSequenceClassificationTestSpec.scala). {%- endcapture -%} diff --git a/docs/en/transformer_entries/DistilBertForTokenClassification.md b/docs/en/transformer_entries/DistilBertForTokenClassification.md index cfd7f53f78cb..4d0c3a829fa0 100644 --- a/docs/en/transformer_entries/DistilBertForTokenClassification.md +++ b/docs/en/transformer_entries/DistilBertForTokenClassification.md @@ -14,7 +14,7 @@ val labels = DistilBertForTokenClassification.pretrained() ``` The default model is `"distilbert_base_token_classifier_conll03"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). and the [DistilBertForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForTokenClassificationTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/ElmoEmbeddings.md b/docs/en/transformer_entries/ElmoEmbeddings.md index cc623fd0506f..c4da92c9811b 100644 --- a/docs/en/transformer_entries/ElmoEmbeddings.md +++ b/docs/en/transformer_entries/ElmoEmbeddings.md @@ -21,7 +21,7 @@ elmo = ElmoEmbeddings.load("/elmo_en_2.4.0_2.4_1580488815299") \ ``` The default model is `"elmo"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). The pooling layer can be set with `setPoolingLayer` to the following values: - `"word_emb"`: the character-based word representations with shape `[batch_size, max_length, 512]`. diff --git a/docs/en/transformer_entries/GPT2Transformer.md b/docs/en/transformer_entries/GPT2Transformer.md index a11cdae5e4c3..90e5d9af7102 100644 --- a/docs/en/transformer_entries/GPT2Transformer.md +++ b/docs/en/transformer_entries/GPT2Transformer.md @@ -26,7 +26,7 @@ val gpt2 = GPT2Transformer.pretrained() .setOutputCol("generation") ``` The default model is `"gpt2"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?q=gpt2). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?q=gpt2). For extended examples of usage, see [GPT2TestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/GPT2TestSpec.scala). diff --git a/docs/en/transformer_entries/HubertForCTC.md b/docs/en/transformer_entries/HubertForCTC.md index ff45f5235737..839afd97935a 100644 --- a/docs/en/transformer_entries/HubertForCTC.md +++ b/docs/en/transformer_entries/HubertForCTC.md @@ -23,7 +23,7 @@ val speechToText = HubertForCTC.pretrained() The default model is `"asr_hubert_large_ls960"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models). +[Models Hub](https://sparknlp.org/models). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended diff --git a/docs/en/transformer_entries/LongformerEmbeddings.md b/docs/en/transformer_entries/LongformerEmbeddings.md index 64f183f98e9d..1e0043dce029 100644 --- a/docs/en/transformer_entries/LongformerEmbeddings.md +++ b/docs/en/transformer_entries/LongformerEmbeddings.md @@ -14,7 +14,7 @@ val embeddings = LongformerEmbeddings.pretrained() .setOutputCol("embeddings") ``` The default model is `"longformer_base_4096"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). For some examples of usage, see [LongformerEmbeddingsTestSpec](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/LongformerEmbeddingsTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/LongformerForQuestionAnswering.md b/docs/en/transformer_entries/LongformerForQuestionAnswering.md index a3733e2cd250..69ac0299360a 100644 --- a/docs/en/transformer_entries/LongformerForQuestionAnswering.md +++ b/docs/en/transformer_entries/LongformerForQuestionAnswering.md @@ -16,7 +16,7 @@ val spanClassifier = LongformerForQuestionAnswering.pretrained() The default model is `"longformer_base_base_qa_squad2"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Question+Answering). +[Models Hub](https://sparknlp.org/models?task=Question+Answering). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the diff --git a/docs/en/transformer_entries/LongformerForSequenceClassification.md b/docs/en/transformer_entries/LongformerForSequenceClassification.md index e0bea36d25a6..dedfe06997a4 100644 --- a/docs/en/transformer_entries/LongformerForSequenceClassification.md +++ b/docs/en/transformer_entries/LongformerForSequenceClassification.md @@ -14,9 +14,9 @@ val sequenceClassifier = LongformerForSequenceClassification.pretrained() ``` The default model is `"longformer_base_sequence_classifier_imdb"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). -Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are +Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the [LongformerForSequenceClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForSequenceClassificationTestSpec.scala). {%- endcapture -%} diff --git a/docs/en/transformer_entries/LongformerForTokenClassification.md b/docs/en/transformer_entries/LongformerForTokenClassification.md index e39bad248a61..db659606521a 100644 --- a/docs/en/transformer_entries/LongformerForTokenClassification.md +++ b/docs/en/transformer_entries/LongformerForTokenClassification.md @@ -14,7 +14,7 @@ val tokenClassifier = LongformerForTokenClassification.pretrained() ``` The default model is `"longformer_base_token_classifier_conll03"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). and the [LongformerForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForTokenClassificationTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/MarianTransformer.md b/docs/en/transformer_entries/MarianTransformer.md index 95a869f8bf4a..cee944479a29 100644 --- a/docs/en/transformer_entries/MarianTransformer.md +++ b/docs/en/transformer_entries/MarianTransformer.md @@ -20,7 +20,7 @@ val marian = MarianTransformer.pretrained() .setOutputCol("translation") ``` The default model is `"opus_mt_en_fr"`, default language is `"xx"` (meaning multi-lingual), if no values are provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Translation). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Translation). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/multilingual/Translation_Marian.ipynb) and the [MarianTransformerTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/MarianTransformerTestSpec.scala). diff --git a/docs/en/transformer_entries/RoBertaEmbeddings.md b/docs/en/transformer_entries/RoBertaEmbeddings.md index 370cb9ba07bf..fc92f5d5d1db 100644 --- a/docs/en/transformer_entries/RoBertaEmbeddings.md +++ b/docs/en/transformer_entries/RoBertaEmbeddings.md @@ -16,7 +16,7 @@ val embeddings = RoBertaEmbeddings.pretrained() .setOutputCol("embeddings") ``` The default model is `"roberta_base"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBERTa.ipynb) and the [RoBertaEmbeddingsTestSpec](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/RoBertaEmbeddingsTestSpec.scala). diff --git a/docs/en/transformer_entries/RoBertaForQuestionAnswering.md b/docs/en/transformer_entries/RoBertaForQuestionAnswering.md index 10d80050e8b7..89fabab31b74 100644 --- a/docs/en/transformer_entries/RoBertaForQuestionAnswering.md +++ b/docs/en/transformer_entries/RoBertaForQuestionAnswering.md @@ -16,7 +16,7 @@ val spanClassifier = RoBertaForQuestionAnswering.pretrained() The default model is `"roberta_base_qa_squad2"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Question+Answering). +[Models Hub](https://sparknlp.org/models?task=Question+Answering). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the diff --git a/docs/en/transformer_entries/RoBertaForSequenceClassification.md b/docs/en/transformer_entries/RoBertaForSequenceClassification.md index 5f69bdd6303e..8d353d9e82b4 100644 --- a/docs/en/transformer_entries/RoBertaForSequenceClassification.md +++ b/docs/en/transformer_entries/RoBertaForSequenceClassification.md @@ -14,7 +14,7 @@ val sequenceClassifier = RoBertaForSequenceClassification.pretrained() ``` The default model is `"roberta_base_sequence_classifier_imdb"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. diff --git a/docs/en/transformer_entries/RoBertaForTokenClassification.md b/docs/en/transformer_entries/RoBertaForTokenClassification.md index 0767d598e5d3..55b04cf0cbe9 100644 --- a/docs/en/transformer_entries/RoBertaForTokenClassification.md +++ b/docs/en/transformer_entries/RoBertaForTokenClassification.md @@ -14,7 +14,7 @@ val tokenClassifier = RoBertaForTokenClassification.pretrained() ``` The default model is `"roberta_base_token_classifier_conll03"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). and the [RoBertaForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForTokenClassificationTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/RoBertaSentenceEmbeddings.md b/docs/en/transformer_entries/RoBertaSentenceEmbeddings.md index d7c635939b89..5983b6be14f3 100644 --- a/docs/en/transformer_entries/RoBertaSentenceEmbeddings.md +++ b/docs/en/transformer_entries/RoBertaSentenceEmbeddings.md @@ -16,7 +16,7 @@ val embeddings = RoBertaSentenceEmbeddings.pretrained() .setOutputCol("sentence_embeddings") ``` The default model is `"sent_roberta_base"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/SpanBertCoref.md b/docs/en/transformer_entries/SpanBertCoref.md index 921161e400a6..888e444cca2c 100644 --- a/docs/en/transformer_entries/SpanBertCoref.md +++ b/docs/en/transformer_entries/SpanBertCoref.md @@ -18,7 +18,7 @@ val dependencyParserApproach = SpanBertCorefModel.pretrained() .setOutputCol("corefs") ``` The default model is `"spanbert_base_coref"`, if no name is provided. For available pretrained -models please see the [Models Hub](https://nlp.johnsnowlabs.com/models). +models please see the [Models Hub](https://sparknlp.org/models). **References:** https://github.com/mandarjoshi90/coref diff --git a/docs/en/transformer_entries/SwinForImageClassification.md b/docs/en/transformer_entries/SwinForImageClassification.md index b0c12703e85b..23e09bf5b20d 100644 --- a/docs/en/transformer_entries/SwinForImageClassification.md +++ b/docs/en/transformer_entries/SwinForImageClassification.md @@ -25,7 +25,7 @@ The default model is `"image_classifier_swin_base_patch_4_window_7_224"`, if no provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Image+Classification). +[Models Hub](https://sparknlp.org/models?task=Image+Classification). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended diff --git a/docs/en/transformer_entries/T5Transformer.md b/docs/en/transformer_entries/T5Transformer.md index f3049285ea45..1b2e0f9438fe 100644 --- a/docs/en/transformer_entries/T5Transformer.md +++ b/docs/en/transformer_entries/T5Transformer.md @@ -20,7 +20,7 @@ val t5 = T5Transformer.pretrained() .setOutputCol("summaries") ``` The default model is `"t5_small"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?q=t5). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?q=t5). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/question-answering/Question_Answering_and_Summarization_with_T5.ipynb) and the [T5TestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/T5TestSpec.scala). diff --git a/docs/en/transformer_entries/TapasForQuestionAnswering.md b/docs/en/transformer_entries/TapasForQuestionAnswering.md index d33a344e35d6..1c16dbf550f1 100644 --- a/docs/en/transformer_entries/TapasForQuestionAnswering.md +++ b/docs/en/transformer_entries/TapasForQuestionAnswering.md @@ -17,7 +17,7 @@ val tapas = TapasForQuestionAnswering.pretrained() The default model is `"table_qa_tapas_base_finetuned_wtq"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Table+Question+Understanding). +[Models Hub](https://sparknlp.org/models?task=Table+Question+Understanding). {%- endcapture -%} {%- capture input_anno -%} diff --git a/docs/en/transformer_entries/UniversalSentenceEncoder.md b/docs/en/transformer_entries/UniversalSentenceEncoder.md index b1ffd8e0b678..ff45d0c48f15 100644 --- a/docs/en/transformer_entries/UniversalSentenceEncoder.md +++ b/docs/en/transformer_entries/UniversalSentenceEncoder.md @@ -12,7 +12,7 @@ val useEmbeddings = UniversalSentenceEncoder.pretrained() .setOutputCol("sentence_embeddings") ``` The default model is `"tfhub_use"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb) and the [UniversalSentenceEncoderTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/UniversalSentenceEncoderTestSpec.scala). diff --git a/docs/en/transformer_entries/ViTForImageClassification.md b/docs/en/transformer_entries/ViTForImageClassification.md index 3998163d481d..f482e2064ba6 100644 --- a/docs/en/transformer_entries/ViTForImageClassification.md +++ b/docs/en/transformer_entries/ViTForImageClassification.md @@ -17,7 +17,7 @@ val imageClassifier = ViTForImageClassification.pretrained() The default model is `"image_classifier_vit_base_patch16_224"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Image+Classification). +[Models Hub](https://sparknlp.org/models?task=Image+Classification). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended diff --git a/docs/en/transformer_entries/Wav2Vec2ForCTC.md b/docs/en/transformer_entries/Wav2Vec2ForCTC.md index 31fa145e5b5e..f4b1e4d8f1b1 100644 --- a/docs/en/transformer_entries/Wav2Vec2ForCTC.md +++ b/docs/en/transformer_entries/Wav2Vec2ForCTC.md @@ -22,7 +22,7 @@ val speechToText = Wav2Vec2ForCTC.pretrained() The default model is `"asr_wav2vec2_base_960h"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models). +[Models Hub](https://sparknlp.org/models). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended diff --git a/docs/en/transformer_entries/XlmRoBertaEmbeddings.md b/docs/en/transformer_entries/XlmRoBertaEmbeddings.md index a88d2593c92a..ff80964a345d 100644 --- a/docs/en/transformer_entries/XlmRoBertaEmbeddings.md +++ b/docs/en/transformer_entries/XlmRoBertaEmbeddings.md @@ -16,7 +16,7 @@ val embeddings = XlmRoBertaEmbeddings.pretrained() .setOutputCol("embeddings") ``` The default model is `"xlm_roberta_base"`, default language is `"xx"` (meaning multi-lingual), if no values are provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). For extended examples of usage, see the [Examples](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XLM-RoBERTa.ipynb) and the [XlmRoBertaEmbeddingsTestSpec](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaEmbeddingsTestSpec.scala). diff --git a/docs/en/transformer_entries/XlmRoBertaForQuestionAnswering.md b/docs/en/transformer_entries/XlmRoBertaForQuestionAnswering.md index 6920b51fb201..1d6382a8a740 100644 --- a/docs/en/transformer_entries/XlmRoBertaForQuestionAnswering.md +++ b/docs/en/transformer_entries/XlmRoBertaForQuestionAnswering.md @@ -16,7 +16,7 @@ val spanClassifier = XlmRoBertaForQuestionAnswering.pretrained() The default model is `"xlm_roberta_base_qa_squad2"`, if no name is provided. For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Question+Answering). +[Models Hub](https://sparknlp.org/models?task=Question+Answering). To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the diff --git a/docs/en/transformer_entries/XlmRoBertaForSequenceClassification.md b/docs/en/transformer_entries/XlmRoBertaForSequenceClassification.md index 01030b984765..6ee37eddca6a 100644 --- a/docs/en/transformer_entries/XlmRoBertaForSequenceClassification.md +++ b/docs/en/transformer_entries/XlmRoBertaForSequenceClassification.md @@ -14,9 +14,9 @@ val sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained() ``` The default model is `"xlm_roberta_base_sequence_classifier_imdb"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). -Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are +Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the [XlmRoBertaForSequenceClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForSequenceClassificationTestSpec.scala). {%- endcapture -%} diff --git a/docs/en/transformer_entries/XlmRoBertaForTokenClassification.md b/docs/en/transformer_entries/XlmRoBertaForTokenClassification.md index 3f6a89d6a5e6..f5e34506f21c 100644 --- a/docs/en/transformer_entries/XlmRoBertaForTokenClassification.md +++ b/docs/en/transformer_entries/XlmRoBertaForTokenClassification.md @@ -14,7 +14,7 @@ val tokenClassifier = XlmRoBertaForTokenClassification.pretrained() ``` The default model is `"xlm_roberta_base_token_classifier_conll03"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). and the [XlmRoBertaForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForTokenClassificationTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/XlmRoBertaSentenceEmbeddings.md b/docs/en/transformer_entries/XlmRoBertaSentenceEmbeddings.md index 61ba131b8837..0017debdc974 100644 --- a/docs/en/transformer_entries/XlmRoBertaSentenceEmbeddings.md +++ b/docs/en/transformer_entries/XlmRoBertaSentenceEmbeddings.md @@ -16,7 +16,7 @@ val embeddings = XlmRoBertaSentenceEmbeddings.pretrained() .setOutputCol("sentence_embeddings") ``` The default model is `"sent_xlm_roberta_base"`, default language is `"xx"` (meaning multi-lingual), if no values are provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Embeddings). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/XlnetForSequenceClassification.md b/docs/en/transformer_entries/XlnetForSequenceClassification.md index 20f62a7ab22d..892d57430893 100644 --- a/docs/en/transformer_entries/XlnetForSequenceClassification.md +++ b/docs/en/transformer_entries/XlnetForSequenceClassification.md @@ -14,9 +14,9 @@ val sequenceClassifier = XlnetForSequenceClassification.pretrained() ``` The default model is `"xlnet_base_sequence_classifier_imdb"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Text+Classification). -Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are +Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the [XlnetForSequenceClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForSequenceClassificationTestSpec.scala). {%- endcapture -%} diff --git a/docs/en/transformer_entries/XlnetForTokenClassification.md b/docs/en/transformer_entries/XlnetForTokenClassification.md index 959da56ce00b..655311b80d49 100644 --- a/docs/en/transformer_entries/XlnetForTokenClassification.md +++ b/docs/en/transformer_entries/XlnetForTokenClassification.md @@ -14,7 +14,7 @@ val tokenClassifier = XlnetForTokenClassification.pretrained() ``` The default model is `"xlnet_base_token_classifier_conll03"`, if no name is provided. -For available pretrained models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition). +For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Named+Entity+Recognition). and the [XlnetForTokenClassificationTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForTokenClassificationTestSpec.scala). Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see [Import Transformers into Spark NLP 🚀](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669). diff --git a/docs/en/transformer_entries/ZeroShotNer.md b/docs/en/transformer_entries/ZeroShotNer.md index 1d7a96e15289..5423862eb1a5 100644 --- a/docs/en/transformer_entries/ZeroShotNer.md +++ b/docs/en/transformer_entries/ZeroShotNer.md @@ -23,7 +23,7 @@ val zeroShotNer = ZeroShotNerModel.pretrained() ``` For available pretrained models please see the -[Models Hub](https://nlp.johnsnowlabs.com/models?task=Zero-Shot-NER). +[Models Hub](https://sparknlp.org/models?task=Zero-Shot-NER). {%- endcapture -%} {%- capture input_anno -%} diff --git a/examples/python/annotation/audio/asr-wav2vec2/Automatic_Speech_Recognition_Wav2Vec2_(Wav2Vec2ForCTC).ipynb b/examples/python/annotation/audio/asr-wav2vec2/Automatic_Speech_Recognition_Wav2Vec2_(Wav2Vec2ForCTC).ipynb index 874498e61b05..ccd789ab7a3c 100644 --- a/examples/python/annotation/audio/asr-wav2vec2/Automatic_Speech_Recognition_Wav2Vec2_(Wav2Vec2ForCTC).ipynb +++ b/examples/python/annotation/audio/asr-wav2vec2/Automatic_Speech_Recognition_Wav2Vec2_(Wav2Vec2ForCTC).ipynb @@ -20,8 +20,8 @@ "\n", "Note that this annotator is currently not supported on Apple Silicon processors such as the M1. This is due to the processor not supporting instructions for XLA.\n", "\n", - "- List of all available ASR [models](https://nlp.johnsnowlabs.com/models?task=Automatic+Speech+Recognition&type=model)\n", - "- List of all available ASR [pipelines](https://nlp.johnsnowlabs.com/models?task=Automatic+Speech+Recognition&type=pipeline)" + "- List of all available ASR [models](https://sparknlp.org/models?task=Automatic+Speech+Recognition&type=model)\n", + "- List of all available ASR [pipelines](https://sparknlp.org/models?task=Automatic+Speech+Recognition&type=pipeline)" ] }, { @@ -234,7 +234,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Simplest and fastest way is to use a pre-trained [pipeline for ASR](https://nlp.johnsnowlabs.com/models?task=Automatic+Speech+Recognition&type=pipeline):\n", + "### Simplest and fastest way is to use a pre-trained [pipeline for ASR](https://sparknlp.org/models?task=Automatic+Speech+Recognition&type=pipeline):\n", "\n", "\n", "\n" diff --git a/examples/python/annotation/image/ViTForImageClassification.ipynb b/examples/python/annotation/image/ViTForImageClassification.ipynb index 84478d8a0118..94529b50ba77 100644 --- a/examples/python/annotation/image/ViTForImageClassification.ipynb +++ b/examples/python/annotation/image/ViTForImageClassification.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/image/ViTForImageClassification.ipynb)" ] diff --git a/examples/python/annotation/text/chinese/word_segmentation/words_segmenter_demo.ipynb b/examples/python/annotation/text/chinese/word_segmentation/words_segmenter_demo.ipynb index b8517d9265de..b0e31bcc0944 100644 --- a/examples/python/annotation/text/chinese/word_segmentation/words_segmenter_demo.ipynb +++ b/examples/python/annotation/text/chinese/word_segmentation/words_segmenter_demo.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)" + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)" ] }, { diff --git a/examples/python/annotation/text/english/MultiDateMatcherMultiLanguage_en.ipynb b/examples/python/annotation/text/english/MultiDateMatcherMultiLanguage_en.ipynb index 7dbb59f18e17..ec5165673dba 100644 --- a/examples/python/annotation/text/english/MultiDateMatcherMultiLanguage_en.ipynb +++ b/examples/python/annotation/text/english/MultiDateMatcherMultiLanguage_en.ipynb @@ -6,7 +6,7 @@ "id": "1abcab5d", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/MultiDateMatcherMultiLanguage_en.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/Text_Preprocessing_with_SparkNLP.ipynb b/examples/python/annotation/text/english/Text_Preprocessing_with_SparkNLP.ipynb index 1f8c1890603f..1163a2285d60 100644 --- a/examples/python/annotation/text/english/Text_Preprocessing_with_SparkNLP.ipynb +++ b/examples/python/annotation/text/english/Text_Preprocessing_with_SparkNLP.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/tutorials/Certification_Trainings/Public/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/chunking/Chunk_Extraction_with_Chunker.ipynb b/examples/python/annotation/text/english/chunking/Chunk_Extraction_with_Chunker.ipynb index 8f750909625d..bb3cdb2c0431 100644 --- a/examples/python/annotation/text/english/chunking/Chunk_Extraction_with_Chunker.ipynb +++ b/examples/python/annotation/text/english/chunking/Chunk_Extraction_with_Chunker.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/chunking/Chunk_Extraction_with_Chunker.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/chunking/NgramGenerator.ipynb b/examples/python/annotation/text/english/chunking/NgramGenerator.ipynb index 53a642aeb031..fa90a5e8116a 100644 --- a/examples/python/annotation/text/english/chunking/NgramGenerator.ipynb +++ b/examples/python/annotation/text/english/chunking/NgramGenerator.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/annotation/english/chunking/NgramGenerator.ipynb)\n", "\n", @@ -57,7 +57,7 @@ "\n", "**Example:**\n", "\n", - "Refer to the [NGramGenerator](https://nlp.johnsnowlabs.com/api/index#com.johnsnowlabs.nlp.annotators.NGramGenerator) Scala docs for more details on the API.\n", + "Refer to the [NGramGenerator](https://sparknlp.org/api/index#com.johnsnowlabs.nlp.annotators.NGramGenerator) Scala docs for more details on the API.\n", "\n", "```python\n", "ngrams_cum = NGramGenerator() \\\n", diff --git a/examples/python/annotation/text/english/document-assembler/Loading_Documents_With_DocumentAssembler.ipynb b/examples/python/annotation/text/english/document-assembler/Loading_Documents_With_DocumentAssembler.ipynb index 228a5fa14cd5..d5b68f73c318 100644 --- a/examples/python/annotation/text/english/document-assembler/Loading_Documents_With_DocumentAssembler.ipynb +++ b/examples/python/annotation/text/english/document-assembler/Loading_Documents_With_DocumentAssembler.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/document-assembler/Loading_Documents_With_DocumentAssembler.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/document-assembler/Loading_Multiple_Documents.ipynb b/examples/python/annotation/text/english/document-assembler/Loading_Multiple_Documents.ipynb index cd13b79c96f7..d3ae89b2e55e 100644 --- a/examples/python/annotation/text/english/document-assembler/Loading_Multiple_Documents.ipynb +++ b/examples/python/annotation/text/english/document-assembler/Loading_Multiple_Documents.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/document-assembler/Loading_Multiple_Documents.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/document-normalizer/document_normalizer_notebook.ipynb b/examples/python/annotation/text/english/document-normalizer/document_normalizer_notebook.ipynb index 99066ae0e9f0..6afbc6fe4745 100644 --- a/examples/python/annotation/text/english/document-normalizer/document_normalizer_notebook.ipynb +++ b/examples/python/annotation/text/english/document-normalizer/document_normalizer_notebook.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/document-normalizer/document_normalizer_notebook.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/embeddings/ChunkEmbeddings.ipynb b/examples/python/annotation/text/english/embeddings/ChunkEmbeddings.ipynb index bd912ef066b0..5c2746747979 100644 --- a/examples/python/annotation/text/english/embeddings/ChunkEmbeddings.ipynb +++ b/examples/python/annotation/text/english/embeddings/ChunkEmbeddings.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/embeddings/ChunkEmbeddings.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/graph-extraction/graph_extraction.ipynb b/examples/python/annotation/text/english/graph-extraction/graph_extraction.ipynb index c22fe3434489..4b0d17f81725 100644 --- a/examples/python/annotation/text/english/graph-extraction/graph_extraction.ipynb +++ b/examples/python/annotation/text/english/graph-extraction/graph_extraction.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/graph-extraction/graph_extraction.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/graph-extraction/graph_extraction_explode_entities.ipynb b/examples/python/annotation/text/english/graph-extraction/graph_extraction_explode_entities.ipynb index 59e4ef415d7d..14d3eaa9889d 100644 --- a/examples/python/annotation/text/english/graph-extraction/graph_extraction_explode_entities.ipynb +++ b/examples/python/annotation/text/english/graph-extraction/graph_extraction_explode_entities.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/graph-extraction/graph_extraction_explode_entities.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/graph-extraction/graph_extraction_helper_display.ipynb b/examples/python/annotation/text/english/graph-extraction/graph_extraction_helper_display.ipynb index 4401e4c6d6f2..ef262bae0caa 100644 --- a/examples/python/annotation/text/english/graph-extraction/graph_extraction_helper_display.ipynb +++ b/examples/python/annotation/text/english/graph-extraction/graph_extraction_helper_display.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/prediction/english/graph_extraction_helper_display.ipynb)" ] diff --git a/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb b/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb index ff4fa32bd71e..ab3691118f6b 100644 --- a/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb +++ b/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/graph-extraction/graph_extraction_roots_paths.ipynb b/examples/python/annotation/text/english/graph-extraction/graph_extraction_roots_paths.ipynb index 321cb1cb257d..6739ffeb5ee4 100644 --- a/examples/python/annotation/text/english/graph-extraction/graph_extraction_roots_paths.ipynb +++ b/examples/python/annotation/text/english/graph-extraction/graph_extraction_roots_paths.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/prediction/english/graph_extraction_roots_paths.ipynb)" ] diff --git a/examples/python/annotation/text/english/keyword-extraction/Keyword_Extraction_YAKE.ipynb b/examples/python/annotation/text/english/keyword-extraction/Keyword_Extraction_YAKE.ipynb index 855467f91aa7..4931864f9035 100644 --- a/examples/python/annotation/text/english/keyword-extraction/Keyword_Extraction_YAKE.ipynb +++ b/examples/python/annotation/text/english/keyword-extraction/Keyword_Extraction_YAKE.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)" + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)" ] }, { diff --git a/examples/python/annotation/text/english/language-detection/Language_Detection_and_Indentification.ipynb b/examples/python/annotation/text/english/language-detection/Language_Detection_and_Indentification.ipynb index 01acf19bcf1c..20fcfa615592 100644 --- a/examples/python/annotation/text/english/language-detection/Language_Detection_and_Indentification.ipynb +++ b/examples/python/annotation/text/english/language-detection/Language_Detection_and_Indentification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/language-detection/Language_Detection_and_Indentification.ipynb)\n", "\n", @@ -76,7 +76,7 @@ "source": [ "## LanguageDetectorDL Pre-trained Models & Pipelines\n", "\n", - "* Available pre-trained pipelines: https://nlp.johnsnowlabs.com/models?tag=language_detection\n", + "* Available pre-trained pipelines: https://sparknlp.org/models?tag=language_detection\n", "\n", "\n", "| Model | Name | Build | Lang \n", diff --git a/examples/python/annotation/text/english/model-downloader/Create custom pipeline - NerDL.ipynb b/examples/python/annotation/text/english/model-downloader/Create custom pipeline - NerDL.ipynb index d3049467a7e1..217305ba54e3 100644 --- a/examples/python/annotation/text/english/model-downloader/Create custom pipeline - NerDL.ipynb +++ b/examples/python/annotation/text/english/model-downloader/Create custom pipeline - NerDL.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/model-downloader/Create%20custom%20pipeline%20-%20NerDL.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/model-downloader/ModelDownloaderExample.ipynb b/examples/python/annotation/text/english/model-downloader/ModelDownloaderExample.ipynb index d1d8bb281704..13f3a16e3cb9 100644 --- a/examples/python/annotation/text/english/model-downloader/ModelDownloaderExample.ipynb +++ b/examples/python/annotation/text/english/model-downloader/ModelDownloaderExample.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/model-downloader/ModelDownloaderExample.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/named-entity-recognition/Named entity recognition - OntoNotes.ipynb b/examples/python/annotation/text/english/named-entity-recognition/Named entity recognition - OntoNotes.ipynb index a8d1c13d013b..e410fc8b0979 100644 --- a/examples/python/annotation/text/english/named-entity-recognition/Named entity recognition - OntoNotes.ipynb +++ b/examples/python/annotation/text/english/named-entity-recognition/Named entity recognition - OntoNotes.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/named-entity-recognition/Named%20entity%20recognition%20-%20OntoNotes.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/named-entity-recognition/ZeroShot_NER.ipynb b/examples/python/annotation/text/english/named-entity-recognition/ZeroShot_NER.ipynb index 85dc68c4e9ce..d7b6987dbe1b 100644 --- a/examples/python/annotation/text/english/named-entity-recognition/ZeroShot_NER.ipynb +++ b/examples/python/annotation/text/english/named-entity-recognition/ZeroShot_NER.ipynb @@ -6,7 +6,7 @@ "id": "36f39d62", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/named-entity-recognition/ZeroShot_NER.ipynb)" ] diff --git a/examples/python/annotation/text/english/pretrained-pipelines/Explain Document DL.ipynb b/examples/python/annotation/text/english/pretrained-pipelines/Explain Document DL.ipynb index 54f6568bb88a..60eade18e01a 100644 --- a/examples/python/annotation/text/english/pretrained-pipelines/Explain Document DL.ipynb +++ b/examples/python/annotation/text/english/pretrained-pipelines/Explain Document DL.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/pretrained-pipelines/Explain%20Document%20DL.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchDateTime-Pipeline.ipynb b/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchDateTime-Pipeline.ipynb index 790531850b75..b2c06351dde5 100644 --- a/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchDateTime-Pipeline.ipynb +++ b/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchDateTime-Pipeline.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchDateTime-Pipeline.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchPattern-Pipeline.ipynb b/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchPattern-Pipeline.ipynb index 056be1d309f5..b72f444b6575 100644 --- a/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchPattern-Pipeline.ipynb +++ b/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchPattern-Pipeline.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples//home/root/Workspace/scala/spark-nlp/examples/python/annotation/text/english/pretrained-pipelines/Pretrained-MatchPattern-Pipeline.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/pretrained-pipelines/explain_document_ml.ipynb b/examples/python/annotation/text/english/pretrained-pipelines/explain_document_ml.ipynb index bbfffb677aa6..f7b7d0267263 100644 --- a/examples/python/annotation/text/english/pretrained-pipelines/explain_document_ml.ipynb +++ b/examples/python/annotation/text/english/pretrained-pipelines/explain_document_ml.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/annotation/english/explain-document-ml/explain_document_ml.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/question-answering/Question_Answering_and_Summarization_with_T5.ipynb b/examples/python/annotation/text/english/question-answering/Question_Answering_and_Summarization_with_T5.ipynb index c07130e43783..9b69e9622d48 100644 --- a/examples/python/annotation/text/english/question-answering/Question_Answering_and_Summarization_with_T5.ipynb +++ b/examples/python/annotation/text/english/question-answering/Question_Answering_and_Summarization_with_T5.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)" + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)" ] }, { @@ -63,16 +63,16 @@ "\n", "|index|model|lang|\n", "|-----:|:-----|----|\n", - "| 1| [t5_active_to_passive_styletransfer](https://nlp.johnsnowlabs.com/2022/05/31/t5_active_to_passive_styletransfer_en_3_0.html) |en|\n", - "| 2| [t5_base](https://nlp.johnsnowlabs.com/2022/05/31/t5_base_en_3_0.html) |en|\n", - "| 3| [t5_base_mediqa_mnli](https://nlp.johnsnowlabs.com/2021/02/19/t5_base_mediqa_mnli_en.html) |en|\n", - "| 4| [t5_formal_to_informal_styletransfer](https://nlp.johnsnowlabs.com/2022/05/31/t5_formal_to_informal_styletransfer_en_3_0.html) |en|\n", - "| 5| [t5_grammar_error_corrector](https://nlp.johnsnowlabs.com/2022/01/12/t5_grammar_error_corrector_en.html) |en|\n", - "| 6| [t5_informal_to_formal_styletransfer](https://nlp.johnsnowlabs.com/2022/05/31/t5_informal_to_formal_styletransfer_en_3_0.html) |en|\n", - "| 7| [t5_passive_to_active_styletransfer](https://nlp.johnsnowlabs.com/2022/05/31/t5_passive_to_active_styletransfer_en_3_0.html) |en|\n", - "| 8| [t5_question_generation_small](https://nlp.johnsnowlabs.com/2022/07/05/t5_question_generation_small_en_3_0.html) |en|\n", - "| 9| [t5_small](https://nlp.johnsnowlabs.com/2022/05/31/t5_small_en_3_0.html) |en|\n", - "| 10| [t5_small_wikiSQL](https://nlp.johnsnowlabs.com/2022/05/31/t5_small_wikiSQL_en_3_0.html) |en|\n", + "| 1| [t5_active_to_passive_styletransfer](https://sparknlp.org/2022/05/31/t5_active_to_passive_styletransfer_en_3_0.html) |en|\n", + "| 2| [t5_base](https://sparknlp.org/2022/05/31/t5_base_en_3_0.html) |en|\n", + "| 3| [t5_base_mediqa_mnli](https://sparknlp.org/2021/02/19/t5_base_mediqa_mnli_en.html) |en|\n", + "| 4| [t5_formal_to_informal_styletransfer](https://sparknlp.org/2022/05/31/t5_formal_to_informal_styletransfer_en_3_0.html) |en|\n", + "| 5| [t5_grammar_error_corrector](https://sparknlp.org/2022/01/12/t5_grammar_error_corrector_en.html) |en|\n", + "| 6| [t5_informal_to_formal_styletransfer](https://sparknlp.org/2022/05/31/t5_informal_to_formal_styletransfer_en_3_0.html) |en|\n", + "| 7| [t5_passive_to_active_styletransfer](https://sparknlp.org/2022/05/31/t5_passive_to_active_styletransfer_en_3_0.html) |en|\n", + "| 8| [t5_question_generation_small](https://sparknlp.org/2022/07/05/t5_question_generation_small_en_3_0.html) |en|\n", + "| 9| [t5_small](https://sparknlp.org/2022/05/31/t5_small_en_3_0.html) |en|\n", + "| 10| [t5_small_wikiSQL](https://sparknlp.org/2022/05/31/t5_small_wikiSQL_en_3_0.html) |en|\n", "\n", "" ] diff --git a/examples/python/annotation/text/english/regex-matcher/Matching_Text_with_RegexMatcher.ipynb b/examples/python/annotation/text/english/regex-matcher/Matching_Text_with_RegexMatcher.ipynb index f65d387b46ca..a843d6991e47 100644 --- a/examples/python/annotation/text/english/regex-matcher/Matching_Text_with_RegexMatcher.ipynb +++ b/examples/python/annotation/text/english/regex-matcher/Matching_Text_with_RegexMatcher.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/regex-matcher/Matching_Text_with_RegexMatcher.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/regex-tokenizer/regex_tokenizer_examples.ipynb b/examples/python/annotation/text/english/regex-tokenizer/regex_tokenizer_examples.ipynb index 9efc28c987ae..8ec9267d7e1f 100644 --- a/examples/python/annotation/text/english/regex-tokenizer/regex_tokenizer_examples.ipynb +++ b/examples/python/annotation/text/english/regex-tokenizer/regex_tokenizer_examples.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/regex-tokenizer/regex_tokenizer_examples.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/sentence-detection/SentenceDetector_advanced_examples.ipynb b/examples/python/annotation/text/english/sentence-detection/SentenceDetector_advanced_examples.ipynb index 0f39f3f53d0b..645a47b76656 100644 --- a/examples/python/annotation/text/english/sentence-detection/SentenceDetector_advanced_examples.ipynb +++ b/examples/python/annotation/text/english/sentence-detection/SentenceDetector_advanced_examples.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/sentence-detection/SentenceDetector_advanced_examples.ipynb)\n" ] @@ -14,7 +14,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# [Sentence Detector](https://nlp.johnsnowlabs.com/docs/en/annotators#sentencedetector)\n", + "# [Sentence Detector](https://sparknlp.org/docs/en/annotators#sentencedetector)\n", "\n", "Sentence Detector is an annotator that detects sentence boundaries using regular\n", "expressions.\n", diff --git a/examples/python/annotation/text/english/sentiment-detection/sentiment_rb.ipynb b/examples/python/annotation/text/english/sentiment-detection/sentiment_rb.ipynb index 1cd1d1a58243..8d47c717c356 100644 --- a/examples/python/annotation/text/english/sentiment-detection/sentiment_rb.ipynb +++ b/examples/python/annotation/text/english/sentiment-detection/sentiment_rb.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/sentiment-detection/sentiment_rb.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/spark-nlp-basics/playground-dataFrames.ipynb b/examples/python/annotation/text/english/spark-nlp-basics/playground-dataFrames.ipynb index b139e4191897..a4e2372ebaf1 100644 --- a/examples/python/annotation/text/english/spark-nlp-basics/playground-dataFrames.ipynb +++ b/examples/python/annotation/text/english/spark-nlp-basics/playground-dataFrames.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/spark-nlp-basics/playground-dataFrames.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/spark-nlp-basics/spark_nlp_basics_functions.ipynb b/examples/python/annotation/text/english/spark-nlp-basics/spark_nlp_basics_functions.ipynb index 8c747f162f1d..b83461fe544b 100644 --- a/examples/python/annotation/text/english/spark-nlp-basics/spark_nlp_basics_functions.ipynb +++ b/examples/python/annotation/text/english/spark-nlp-basics/spark_nlp_basics_functions.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/spark-nlp-basics/spark_nlp_basics_functions.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/spell-check-ml-pipeline/Pretrained-SpellCheckML-Pipeline.ipynb b/examples/python/annotation/text/english/spell-check-ml-pipeline/Pretrained-SpellCheckML-Pipeline.ipynb index 84d5c7909992..ef5e428a62fb 100644 --- a/examples/python/annotation/text/english/spell-check-ml-pipeline/Pretrained-SpellCheckML-Pipeline.ipynb +++ b/examples/python/annotation/text/english/spell-check-ml-pipeline/Pretrained-SpellCheckML-Pipeline.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/annotation/english/spell-check-ml-pipeline/Pretrained-SpellCheckML-Pipeline.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/stemmer/Word_Stemming_with_Stemmer.ipynb b/examples/python/annotation/text/english/stemmer/Word_Stemming_with_Stemmer.ipynb index 713e247325d6..3893491eb2c9 100644 --- a/examples/python/annotation/text/english/stemmer/Word_Stemming_with_Stemmer.ipynb +++ b/examples/python/annotation/text/english/stemmer/Word_Stemming_with_Stemmer.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/stemmer/Word_Stemming_with_Stemmer.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb b/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb index b1aa879c1a38..08ab18c50c41 100644 --- a/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb +++ b/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/text-matcher-pipeline/extractor.ipynb b/examples/python/annotation/text/english/text-matcher-pipeline/extractor.ipynb index e0b3dbd3d479..687f4521b40b 100644 --- a/examples/python/annotation/text/english/text-matcher-pipeline/extractor.ipynb +++ b/examples/python/annotation/text/english/text-matcher-pipeline/extractor.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/text-matcher-pipeline/extractor.ipynb\n", ")\n", diff --git a/examples/python/annotation/text/english/text-similarity/Spark_NLP_Spark_ML_Text_Similarity.ipynb b/examples/python/annotation/text/english/text-similarity/Spark_NLP_Spark_ML_Text_Similarity.ipynb index 654bfa8e87e7..9867c44444fc 100644 --- a/examples/python/annotation/text/english/text-similarity/Spark_NLP_Spark_ML_Text_Similarity.ipynb +++ b/examples/python/annotation/text/english/text-similarity/Spark_NLP_Spark_ML_Text_Similarity.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/text-similarity/Spark_NLP_Spark_ML_Text_Similarity.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/english/token-assembler/Assembling_Tokens_to_Documents.ipynb b/examples/python/annotation/text/english/token-assembler/Assembling_Tokens_to_Documents.ipynb index 33af57b1afef..089f7768f82a 100644 --- a/examples/python/annotation/text/english/token-assembler/Assembling_Tokens_to_Documents.ipynb +++ b/examples/python/annotation/text/english/token-assembler/Assembling_Tokens_to_Documents.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/token-assembler/Assembling_Tokens_to_Documents.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/french/MultiDateMatcherMultiLanguage_fr.ipynb b/examples/python/annotation/text/french/MultiDateMatcherMultiLanguage_fr.ipynb index db0795764e6f..a31e3ab16c14 100644 --- a/examples/python/annotation/text/french/MultiDateMatcherMultiLanguage_fr.ipynb +++ b/examples/python/annotation/text/french/MultiDateMatcherMultiLanguage_fr.ipynb @@ -6,7 +6,7 @@ "id": "6324b870", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/french/MultiDateMatcherMultiLanguage_fr.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/french/date_matcher_multi_language_fr.ipynb b/examples/python/annotation/text/french/date_matcher_multi_language_fr.ipynb index 7219df098e05..96a7c46c2af0 100644 --- a/examples/python/annotation/text/french/date_matcher_multi_language_fr.ipynb +++ b/examples/python/annotation/text/french/date_matcher_multi_language_fr.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/french/date_matcher_multi_language_fr.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/german/MultiDateMatcherMultiLanguage_de.ipynb b/examples/python/annotation/text/german/MultiDateMatcherMultiLanguage_de.ipynb index 3975073b6f4c..72518e74f3c0 100644 --- a/examples/python/annotation/text/german/MultiDateMatcherMultiLanguage_de.ipynb +++ b/examples/python/annotation/text/german/MultiDateMatcherMultiLanguage_de.ipynb @@ -6,7 +6,7 @@ "id": "91762c58", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/german/MultiDateMatcherMultiLanguage_de.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/german/date_matcher_multi_language_de.ipynb b/examples/python/annotation/text/german/date_matcher_multi_language_de.ipynb index 45d1df5c9adb..a9846585c2e1 100644 --- a/examples/python/annotation/text/german/date_matcher_multi_language_de.ipynb +++ b/examples/python/annotation/text/german/date_matcher_multi_language_de.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/german/date_matcher_multi_language_de.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/german/pretrained_german_models.ipynb b/examples/python/annotation/text/german/pretrained_german_models.ipynb index 86cd7810fbc5..6bb09a5124b5 100644 --- a/examples/python/annotation/text/german/pretrained_german_models.ipynb +++ b/examples/python/annotation/text/german/pretrained_german_models.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/german/pretrained_german_models.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/italian/MultiDateMatcherMultiLanguage_it.ipynb b/examples/python/annotation/text/italian/MultiDateMatcherMultiLanguage_it.ipynb index 3791d2f576c0..10d52d4595b1 100644 --- a/examples/python/annotation/text/italian/MultiDateMatcherMultiLanguage_it.ipynb +++ b/examples/python/annotation/text/italian/MultiDateMatcherMultiLanguage_it.ipynb @@ -5,7 +5,7 @@ "id": "cf3d94d0", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/italian/MultiDateMatcherMultiLanguage_it.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/italian/date_matcher_multi_language_it.ipynb b/examples/python/annotation/text/italian/date_matcher_multi_language_it.ipynb index 6f67aa07ad65..3291ce92e51c 100644 --- a/examples/python/annotation/text/italian/date_matcher_multi_language_it.ipynb +++ b/examples/python/annotation/text/italian/date_matcher_multi_language_it.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/italian/date_matcher_multi_language_it.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/multilingual/SentenceDetectorDL.ipynb b/examples/python/annotation/text/multilingual/SentenceDetectorDL.ipynb index e6e41e1c4829..226b883c9f3f 100644 --- a/examples/python/annotation/text/multilingual/SentenceDetectorDL.ipynb +++ b/examples/python/annotation/text/multilingual/SentenceDetectorDL.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)" + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)" ] }, { diff --git a/examples/python/annotation/text/multilingual/Translation_Marian.ipynb b/examples/python/annotation/text/multilingual/Translation_Marian.ipynb index 58283d707df1..22262ca2d0d5 100644 --- a/examples/python/annotation/text/multilingual/Translation_Marian.ipynb +++ b/examples/python/annotation/text/multilingual/Translation_Marian.ipynb @@ -7,7 +7,7 @@ "source": [ "\n", "\n", - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/multilingual/Translation_Marian.ipynb)\n", "\n", @@ -91,10 +91,10 @@ "metadata": {}, "source": [ "For complete model list: \n", - "https://nlp.johnsnowlabs.com/models\n", + "https://sparknlp.org/models\n", "\n", "For `Translation` models:\n", - "https://nlp.johnsnowlabs.com/models?tag=translation" + "https://sparknlp.org/models?tag=translation" ] }, { diff --git a/examples/python/annotation/text/multilingual/WordSegmenterMultilingual.ipynb b/examples/python/annotation/text/multilingual/WordSegmenterMultilingual.ipynb index 73c1e9bc9635..562ff6a304cd 100644 --- a/examples/python/annotation/text/multilingual/WordSegmenterMultilingual.ipynb +++ b/examples/python/annotation/text/multilingual/WordSegmenterMultilingual.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/multilingual/WordSegmenterMultilingual.ipynb)\n", "\n", @@ -67,14 +67,14 @@ "source": [ "Setting `setEnableRegexTokenizer=True` parameter will make WordSegmenter to tokenize latin words based on spaces and apply word segmenter inference **only in non-latin words**. As show in the example below.\n", "\n", - "**Note:** There are 3 parameters to play around for tokenization of latin words. You can check those in our [official documentation](https://nlp.johnsnowlabs.com/api/com/johnsnowlabs/nlp/annotators/ws/WordSegmenterModel.html)" + "**Note:** There are 3 parameters to play around for tokenization of latin words. You can check those in our [official documentation](https://sparknlp.org/api/com/johnsnowlabs/nlp/annotators/ws/WordSegmenterModel.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This example has a text with Thai and English words. So, we use a WordSegmenter model of Thai language. You can check additional WordSegmenter models in our [official model's page](https://nlp.johnsnowlabs.com/models?q=Word+Segmenter).\n", + "This example has a text with Thai and English words. So, we use a WordSegmenter model of Thai language. You can check additional WordSegmenter models in our [official model's page](https://sparknlp.org/models?q=Word+Segmenter).\n", "\n", "---\n", "\n" diff --git a/examples/python/annotation/text/portuguese/MultiDateMatcherMultiLanguage_pt.ipynb b/examples/python/annotation/text/portuguese/MultiDateMatcherMultiLanguage_pt.ipynb index e461f53e7c28..27ed4f1a153a 100644 --- a/examples/python/annotation/text/portuguese/MultiDateMatcherMultiLanguage_pt.ipynb +++ b/examples/python/annotation/text/portuguese/MultiDateMatcherMultiLanguage_pt.ipynb @@ -6,7 +6,7 @@ "id": "cfd62825", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/portuguese/MultiDateMatcherMultiLanguage_pt.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/portuguese/date_matcher_multi_language_pt.ipynb b/examples/python/annotation/text/portuguese/date_matcher_multi_language_pt.ipynb index f4b4aa4603e7..b1a46d44d4e7 100644 --- a/examples/python/annotation/text/portuguese/date_matcher_multi_language_pt.ipynb +++ b/examples/python/annotation/text/portuguese/date_matcher_multi_language_pt.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/portuguese/date_matcher_multi_language_pt.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/spanish/MultiDateMatcherMultiLanguage_es.ipynb b/examples/python/annotation/text/spanish/MultiDateMatcherMultiLanguage_es.ipynb index fc713115b7d8..87d033ec1897 100644 --- a/examples/python/annotation/text/spanish/MultiDateMatcherMultiLanguage_es.ipynb +++ b/examples/python/annotation/text/spanish/MultiDateMatcherMultiLanguage_es.ipynb @@ -6,7 +6,7 @@ "id": "233932d4", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/spanish/MultiDateMatcherMultiLanguage_es.ipynb)\n", "\n", diff --git a/examples/python/annotation/text/spanish/date_matcher_multi_language_es.ipynb b/examples/python/annotation/text/spanish/date_matcher_multi_language_es.ipynb index 225b003ec78c..6cfe6045534a 100644 --- a/examples/python/annotation/text/spanish/date_matcher_multi_language_es.ipynb +++ b/examples/python/annotation/text/spanish/date_matcher_multi_language_es.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/spanish/date_matcher_multi_language_es.ipynb)\n", "\n", diff --git a/examples/python/quick_start.ipynb b/examples/python/quick_start.ipynb index d264827f2688..e64765bc80ef 100644 --- a/examples/python/quick_start.ipynb +++ b/examples/python/quick_start.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/quick_start.ipynb)" ] diff --git a/examples/python/quick_start_google_colab.ipynb b/examples/python/quick_start_google_colab.ipynb index 0c3f4ea015cf..336465fb0e40 100644 --- a/examples/python/quick_start_google_colab.ipynb +++ b/examples/python/quick_start_google_colab.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)" + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)" ] }, { @@ -242,7 +242,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Please check our [Models Hub](https://nlp.johnsnowlabs.com/models) for more pretrained models and pipelines! 😊 " + "### Please check our [Models Hub](https://sparknlp.org/models) for more pretrained models and pipelines! 😊 " ] } ], diff --git a/examples/python/training/chinese/word-segmentation/WordSegmenter_train_chinese_segmentation.ipynb b/examples/python/training/chinese/word-segmentation/WordSegmenter_train_chinese_segmentation.ipynb index 33dfd8feb752..d052ccdcf443 100644 --- a/examples/python/training/chinese/word-segmentation/WordSegmenter_train_chinese_segmentation.ipynb +++ b/examples/python/training/chinese/word-segmentation/WordSegmenter_train_chinese_segmentation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)" + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)" ] }, { @@ -19,7 +19,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# [Word Segmenter](https://nlp.johnsnowlabs.com/docs/en/annotators#wordsegmenter)\n", + "# [Word Segmenter](https://sparknlp.org/docs/en/annotators#wordsegmenter)\n", "\n", "Many languages are not whitespace separated and their sentences are a\n", "concatenation of many symbols, like Korean, Japanese or Chinese. Without\n", diff --git a/examples/python/training/english/classification/ClassifierDL_Train_and_Evaluate.ipynb b/examples/python/training/english/classification/ClassifierDL_Train_and_Evaluate.ipynb index 9f52a173d0b9..e990bc454e2a 100644 --- a/examples/python/training/english/classification/ClassifierDL_Train_and_Evaluate.ipynb +++ b/examples/python/training/english/classification/ClassifierDL_Train_and_Evaluate.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_and_Evaluate.ipynb)\n", "\n", diff --git a/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb b/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb index 79a96ef2652c..843a5c02b507 100644 --- a/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb +++ b/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb)\n", "\n", diff --git a/examples/python/training/english/classification/MultiClassifierDL_Train_and_Evaluate.ipynb b/examples/python/training/english/classification/MultiClassifierDL_Train_and_Evaluate.ipynb index 861c6245ab20..d4f65bc3dc98 100644 --- a/examples/python/training/english/classification/MultiClassifierDL_Train_and_Evaluate.ipynb +++ b/examples/python/training/english/classification/MultiClassifierDL_Train_and_Evaluate.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/MultiClassifierDL_Train_and_Evaluate.ipynb)\n", "\n", diff --git a/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_E2E_challenge_classifier.ipynb b/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_E2E_challenge_classifier.ipynb index c16a317a7f46..8de964cb0521 100644 --- a/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_E2E_challenge_classifier.ipynb +++ b/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_E2E_challenge_classifier.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_E2E_challenge_classifier.ipynb)\n", "\n", diff --git a/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_toxic_classifier.ipynb b/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_toxic_classifier.ipynb index 02ed05fc4e44..f6c657e50913 100644 --- a/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_toxic_classifier.ipynb +++ b/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_toxic_classifier.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/MultiClassifierDL_train_multi_label_toxic_classifier.ipynb)\n", "\n", diff --git a/examples/python/training/english/classification/SentimentDL_Train_and_Evaluate.ipynb b/examples/python/training/english/classification/SentimentDL_Train_and_Evaluate.ipynb index 8bd9d8a0674e..7837ff01d769 100644 --- a/examples/python/training/english/classification/SentimentDL_Train_and_Evaluate.ipynb +++ b/examples/python/training/english/classification/SentimentDL_Train_and_Evaluate.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/SentimentDL_Train_and_Evaluate.ipynb)\n", "\n", diff --git a/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb b/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb index 1a3d57865e08..b57727109f3b 100644 --- a/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb +++ b/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb)\n", "\n", diff --git a/examples/python/training/english/crf-ner/ner_dl_crf.ipynb b/examples/python/training/english/crf-ner/ner_dl_crf.ipynb index 75b8e5feb0d3..c3f0e0aff092 100644 --- a/examples/python/training/english/crf-ner/ner_dl_crf.ipynb +++ b/examples/python/training/english/crf-ner/ner_dl_crf.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/crf-ner/ner_dl_crf.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/mfa_ner_graphs_s3.ipynb b/examples/python/training/english/dl-ner/mfa_ner_graphs_s3.ipynb index d488360ea692..07d4239dc41c 100644 --- a/examples/python/training/english/dl-ner/mfa_ner_graphs_s3.ipynb +++ b/examples/python/training/english/dl-ner/mfa_ner_graphs_s3.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/mfa_ner_graphs_s3.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/ner_albert.ipynb b/examples/python/training/english/dl-ner/ner_albert.ipynb index 300eceae1869..8a75129f3a26 100644 --- a/examples/python/training/english/dl-ner/ner_albert.ipynb +++ b/examples/python/training/english/dl-ner/ner_albert.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_albert.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/ner_bert.ipynb b/examples/python/training/english/dl-ner/ner_bert.ipynb index 0f92d814e2bc..eeb9c06c87f2 100644 --- a/examples/python/training/english/dl-ner/ner_bert.ipynb +++ b/examples/python/training/english/dl-ner/ner_bert.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_bert.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/ner_dl.ipynb b/examples/python/training/english/dl-ner/ner_dl.ipynb index 4bf4ac72d8f9..cd8826e58b99 100644 --- a/examples/python/training/english/dl-ner/ner_dl.ipynb +++ b/examples/python/training/english/dl-ner/ner_dl.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_dl.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/ner_elmo.ipynb b/examples/python/training/english/dl-ner/ner_elmo.ipynb index 813f44290ba6..093b645425fd 100644 --- a/examples/python/training/english/dl-ner/ner_elmo.ipynb +++ b/examples/python/training/english/dl-ner/ner_elmo.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_elmo.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/ner_graph_builder.ipynb b/examples/python/training/english/dl-ner/ner_graph_builder.ipynb index 6fa748615a0a..fcb192a29b82 100644 --- a/examples/python/training/english/dl-ner/ner_graph_builder.ipynb +++ b/examples/python/training/english/dl-ner/ner_graph_builder.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_graph_builder.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/ner_logs.ipynb b/examples/python/training/english/dl-ner/ner_logs.ipynb index 8182b7450359..4f7a000347d3 100644 --- a/examples/python/training/english/dl-ner/ner_logs.ipynb +++ b/examples/python/training/english/dl-ner/ner_logs.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_logs.ipynb)\n", "\n", diff --git a/examples/python/training/english/dl-ner/ner_xlnet.ipynb b/examples/python/training/english/dl-ner/ner_xlnet.ipynb index df183f54b96b..00a9f554889b 100644 --- a/examples/python/training/english/dl-ner/ner_xlnet.ipynb +++ b/examples/python/training/english/dl-ner/ner_xlnet.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/training/english/dl-ner/ner_xlnet.ipynb)\n", "\n", diff --git a/examples/python/training/english/doc2vec/Train_Doc2Vec_and_Text_Classification.ipynb b/examples/python/training/english/doc2vec/Train_Doc2Vec_and_Text_Classification.ipynb index 6bb0289f731a..2780860ddaf1 100644 --- a/examples/python/training/english/doc2vec/Train_Doc2Vec_and_Text_Classification.ipynb +++ b/examples/python/training/english/doc2vec/Train_Doc2Vec_and_Text_Classification.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/doc2vec/Train_Doc2Vec_and_Text_Classification.ipynb)\n", "\n", diff --git a/examples/python/training/english/entity-ruler/EntityRuler.ipynb b/examples/python/training/english/entity-ruler/EntityRuler.ipynb index c5c8478c8d7e..4148140ef451 100644 --- a/examples/python/training/english/entity-ruler/EntityRuler.ipynb +++ b/examples/python/training/english/entity-ruler/EntityRuler.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/entity-ruler/EntityRuler.ipynb)\n", "\n", diff --git a/examples/python/training/english/entity-ruler/EntityRuler_Alphabet.ipynb b/examples/python/training/english/entity-ruler/EntityRuler_Alphabet.ipynb index a0f65fac5235..d8642c7649c9 100644 --- a/examples/python/training/english/entity-ruler/EntityRuler_Alphabet.ipynb +++ b/examples/python/training/english/entity-ruler/EntityRuler_Alphabet.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/entity-ruler/EntityRuler_Alphabet.ipynb)\n", "\n", diff --git a/examples/python/training/english/entity-ruler/EntityRuler_LightPipeline.ipynb b/examples/python/training/english/entity-ruler/EntityRuler_LightPipeline.ipynb index 095b49d2e1c3..eb548b5e02b4 100644 --- a/examples/python/training/english/entity-ruler/EntityRuler_LightPipeline.ipynb +++ b/examples/python/training/english/entity-ruler/EntityRuler_LightPipeline.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/entity-ruler/EntityRuler_LightPipeline.ipynb)\n", "\n", diff --git a/examples/python/training/english/entity-ruler/EntityRuler_Without_Storage.ipynb b/examples/python/training/english/entity-ruler/EntityRuler_Without_Storage.ipynb index ecb65628fdbf..d2caab7b7840 100644 --- a/examples/python/training/english/entity-ruler/EntityRuler_Without_Storage.ipynb +++ b/examples/python/training/english/entity-ruler/EntityRuler_Without_Storage.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/entity-ruler/EntityRuler_Without_Storage.ipynb)\n", "\n", diff --git a/examples/python/training/english/sentiment-detection/RuleBasedSentiment.ipynb b/examples/python/training/english/sentiment-detection/RuleBasedSentiment.ipynb index b378e502580d..d45549562d17 100644 --- a/examples/python/training/english/sentiment-detection/RuleBasedSentiment.ipynb +++ b/examples/python/training/english/sentiment-detection/RuleBasedSentiment.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/sentiment-detection/RuleBasedSentiment.ipynb)\n", "\n", diff --git a/examples/python/training/english/sentiment-detection/VivekNarayanSentimentApproach.ipynb b/examples/python/training/english/sentiment-detection/VivekNarayanSentimentApproach.ipynb index 0ed6f28ee17f..0f936d91d778 100644 --- a/examples/python/training/english/sentiment-detection/VivekNarayanSentimentApproach.ipynb +++ b/examples/python/training/english/sentiment-detection/VivekNarayanSentimentApproach.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/sentiment-detection/VivekNarayanSentimentApproach.ipynb)\n", "\n", diff --git a/examples/python/training/english/word2vec/Train_Word2Vec_and_Named_Entity_Recognition.ipynb b/examples/python/training/english/word2vec/Train_Word2Vec_and_Named_Entity_Recognition.ipynb index 00b01b408851..254f1f0947b3 100644 --- a/examples/python/training/english/word2vec/Train_Word2Vec_and_Named_Entity_Recognition.ipynb +++ b/examples/python/training/english/word2vec/Train_Word2Vec_and_Named_Entity_Recognition.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/word2vec/Train_Word2Vec_and_Named_Entity_Recognition.ipynb)\n", "\n", diff --git a/examples/python/training/french/Train-Perceptron-French.ipynb b/examples/python/training/french/Train-Perceptron-French.ipynb index 8c7de9049fba..a5bee30b82f2 100644 --- a/examples/python/training/french/Train-Perceptron-French.ipynb +++ b/examples/python/training/french/Train-Perceptron-French.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/french/Train-Perceptron-French.ipynb)\n", "\n", diff --git a/examples/python/training/italian/Train-Lemmatizer-Italian.ipynb b/examples/python/training/italian/Train-Lemmatizer-Italian.ipynb index a548ddbae35a..ef3dc8710aa9 100644 --- a/examples/python/training/italian/Train-Lemmatizer-Italian.ipynb +++ b/examples/python/training/italian/Train-Lemmatizer-Italian.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Train-Lemmatizer-Italian.ipynb)\n", "\n", diff --git a/examples/python/training/italian/Train-SentimentDetector-Italian.ipynb b/examples/python/training/italian/Train-SentimentDetector-Italian.ipynb index e6139f4f018b..7aa87add7919 100644 --- a/examples/python/training/italian/Train-SentimentDetector-Italian.ipynb +++ b/examples/python/training/italian/Train-SentimentDetector-Italian.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Train-SentimentDetector-Italian.ipynb)\n", "\n", diff --git a/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb b/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb index f5ccec1c865e..3549b9087397 100644 --- a/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb +++ b/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb)\n", "\n", diff --git a/examples/python/transformers/HuggingFace in Spark NLP - ALBERT.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - ALBERT.ipynb index 5103aeef09a4..7b4e57438069 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - ALBERT.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - ALBERT.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20ALBERT.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - AlbertForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - AlbertForQuestionAnswering.ipynb index 030465444ec9..0404d7ff8368 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - AlbertForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - AlbertForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20AlbertForQuestionAnswering.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - AlbertForSequenceClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - AlbertForSequenceClassification.ipynb index 9f55f30dc4b1..8b2009f3de17 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - AlbertForSequenceClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - AlbertForSequenceClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20AlbertForSequenceClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - AlbertForTokenClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - AlbertForTokenClassification.ipynb index 735d030c2acc..52d8f87c88c8 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - AlbertForTokenClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - AlbertForTokenClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20AlbertForTokenClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - BERT Sentence.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - BERT Sentence.ipynb index 5b5e71922cc0..cc3d3bf8e270 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - BERT Sentence.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - BERT Sentence.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - BERT.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - BERT.ipynb index bad6cfcf73a8..29b48e500fda 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - BERT.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - BERT.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - BertForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - BertForQuestionAnswering.ipynb index a7dc58b98ad8..b1162cff5a02 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - BertForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - BertForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BertForQuestionAnswering.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - BertForSequenceClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - BertForSequenceClassification.ipynb index d0368bdeeeb5..2615d475d3d6 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - BertForSequenceClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - BertForSequenceClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BertForSequenceClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - BertForTokenClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - BertForTokenClassification.ipynb index 1bc19de4c5f1..d981ce88c14e 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - BertForTokenClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - BertForTokenClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BertForTokenClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - CamemBERT.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - CamemBERT.ipynb index bc3c9ba5858e..ee619592d9d7 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - CamemBERT.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - CamemBERT.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBERT.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForQuestionAnswering.ipynb index af6723f1b416..117b79405e49 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBertForQuestionAnswering.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForSequenceClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForSequenceClassification.ipynb index f50d0f94d037..ae5299bdb12b 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForSequenceClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForSequenceClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBertForSequenceClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForTokenClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForTokenClassification.ipynb index cad6d91003ff..d176f03296f5 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForTokenClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - CamemBertForTokenClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBertForTokenClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - DeBERTa.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - DeBERTa.ipynb index 171aef430207..54e36838a259 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - DeBERTa.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - DeBERTa.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DeBERTa.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - DeBertaForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - DeBertaForQuestionAnswering.ipynb index 4fda6428b576..0e52c9899a95 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - DeBertaForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - DeBertaForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DeBertaForQuestionAnswering.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - DistilBERT.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - DistilBERT.ipynb index aeedcfbee2c8..2058b1d78a6a 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - DistilBERT.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - DistilBERT.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DistilBERT.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForQuestionAnswering.ipynb index bb1f93ddaa91..f0aa79eee6ab 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DistilBertForQuestionAnswering.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForSequenceClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForSequenceClassification.ipynb index 7f7cc1786e42..86e8c21d2892 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForSequenceClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForSequenceClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DistilBertForSequenceClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForTokenClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForTokenClassification.ipynb index 0c5060185de4..7790931bd732 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForTokenClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - DistilBertForTokenClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DistilBertForTokenClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - Longformer.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - Longformer.ipynb index 5d90c82643f8..edca8ce3a6bc 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - Longformer.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - Longformer.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20Longformer.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - LongformerForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - LongformerForQuestionAnswering.ipynb index 3333cf2507d5..0c25a58daadb 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - LongformerForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - LongformerForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20LongformerForQuestionAnswering.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - RoBERTa.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - RoBERTa.ipynb index 9e35167c7ad5..a33e77ffb6ac 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - RoBERTa.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - RoBERTa.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBERTa.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForQuestionAnswering.ipynb index 49505273687c..ada82e3d786d 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBertaForQuestionAnswering.ipynb)" diff --git a/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForSequenceClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForSequenceClassification.ipynb index 1c26c7cdfd6d..f60cae266c98 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForSequenceClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForSequenceClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBertaForSequenceClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForTokenClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForTokenClassification.ipynb index e09e0cfabe13..5912536126b8 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForTokenClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - RoBertaForTokenClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBertaForTokenClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - ViTForImageClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - ViTForImageClassification.ipynb index e9bd0726f676..ffad670ef575 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - ViTForImageClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - ViTForImageClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20ViTForImageClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - XLM-RoBERTa.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - XLM-RoBERTa.ipynb index 918149f4ac05..1b594872a584 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - XLM-RoBERTa.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - XLM-RoBERTa.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XLM-RoBERTa.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - XLNet.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - XLNet.ipynb index 3d79af75e971..167812ed91ac 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - XLNet.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - XLNet.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XLNet.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForSequenceClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForSequenceClassification.ipynb index e9a89d40a664..af95ce7ba80b 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForSequenceClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForSequenceClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XlmRoBertaForSequenceClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForTokenClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForTokenClassification.ipynb index d0ee9315d696..fc933baab395 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForTokenClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - XlmRoBertaForTokenClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XlmRoBertaForTokenClassification.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - XlmRobertaForQuestionAnswering.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - XlmRobertaForQuestionAnswering.ipynb index 825353027d67..5b70c5f8562c 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - XlmRobertaForQuestionAnswering.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - XlmRobertaForQuestionAnswering.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XlmRobertaForQuestionAnswering.ipynb)" ] diff --git a/examples/python/transformers/HuggingFace in Spark NLP - XlnetForSequenceClassification.ipynb b/examples/python/transformers/HuggingFace in Spark NLP - XlnetForSequenceClassification.ipynb index 1e1195f02b24..b5ab708ac18f 100644 --- a/examples/python/transformers/HuggingFace in Spark NLP - XlnetForSequenceClassification.ipynb +++ b/examples/python/transformers/HuggingFace in Spark NLP - XlnetForSequenceClassification.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.orgimages/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XlnetForSequenceClassification.ipynb)" ] diff --git a/examples/python/transformers/Import External SavedModel From Remote Storage.ipynb b/examples/python/transformers/Import External SavedModel From Remote Storage.ipynb index b4f5be47288e..c102b12d2e70 100644 --- a/examples/python/transformers/Import External SavedModel From Remote Storage.ipynb +++ b/examples/python/transformers/Import External SavedModel From Remote Storage.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/Import%20External%20SavedModel%20From%20Remote%20Storage.ipynb)" ] diff --git a/examples/python/transformers/TF Hub in Spark NLP - ALBERT.ipynb b/examples/python/transformers/TF Hub in Spark NLP - ALBERT.ipynb index 25c104d8cdfc..53535353ab18 100644 --- a/examples/python/transformers/TF Hub in Spark NLP - ALBERT.ipynb +++ b/examples/python/transformers/TF Hub in Spark NLP - ALBERT.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20ALBERT.ipynb)\n", "\n", diff --git a/examples/python/transformers/TF Hub in Spark NLP - BERT Sentence.ipynb b/examples/python/transformers/TF Hub in Spark NLP - BERT Sentence.ipynb index f08314af7ffc..810381109395 100644 --- a/examples/python/transformers/TF Hub in Spark NLP - BERT Sentence.ipynb +++ b/examples/python/transformers/TF Hub in Spark NLP - BERT Sentence.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb)\n", "\n", diff --git a/examples/python/transformers/TF Hub in Spark NLP - BERT.ipynb b/examples/python/transformers/TF Hub in Spark NLP - BERT.ipynb index e26290028a7d..510e1e56b641 100644 --- a/examples/python/transformers/TF Hub in Spark NLP - BERT.ipynb +++ b/examples/python/transformers/TF Hub in Spark NLP - BERT.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20BERT.ipynb)\n", "\n", diff --git a/examples/scala/training/NerDL/win/README.md b/examples/scala/training/NerDL/win/README.md index 582b81e735a8..6df7dcf1919e 100644 --- a/examples/scala/training/NerDL/win/README.md +++ b/examples/scala/training/NerDL/win/README.md @@ -3,7 +3,7 @@ * Few resolved issues which were faced while developing **CustomForNerDLPipeline** solution: >> Known Issues 1. If anyone encounters an error like 'Could not find a suitable tensorflow graph' - * Please find the solution at https://nlp.johnsnowlabs.com/docs/en/graph + * Please find the solution at https://sparknlp.org/docs/en/graph 2. Encountered an issue while working with play framework, which has a transitive dependency of guava jar * Solution: Use dependencyOverrides += "com.google.guava" % "guava" % "15.0" * Exception looks like below: diff --git a/examples/scala/training/NerDL/win/customNerDlPipeline/CustomForNerDLPipeline.java b/examples/scala/training/NerDL/win/customNerDlPipeline/CustomForNerDLPipeline.java index c4a41d4835f1..d7fabe3f4ebf 100644 --- a/examples/scala/training/NerDL/win/customNerDlPipeline/CustomForNerDLPipeline.java +++ b/examples/scala/training/NerDL/win/customNerDlPipeline/CustomForNerDLPipeline.java @@ -20,7 +20,7 @@ // The spark-nlp library has few defined configurations and if someone needs a different configuration // then one has to create a graph with required configuration - // Please go through this link for more details: https://nlp.johnsnowlabs.com/docs/en/graph + // Please go through this link for more details: https://sparknlp.org/docs/en/graph val PATH_TO_GRAPH_FOLDER="C:\\OpenSourceData\\GRAPH_FOLDER" // we have used glove word embeddings, one can learn word-embeddings related to it's data but it works fine. diff --git a/examples/util/Load_Model_From_S3.ipynb b/examples/util/Load_Model_From_S3.ipynb index 8e1604bc8186..b011b557cb0a 100644 --- a/examples/util/Load_Model_From_S3.ipynb +++ b/examples/util/Load_Model_From_S3.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/util/Load_Model_From_S3.ipynb)" ] diff --git a/examples/util/Load_Model_from_GCP_Storage.ipynb b/examples/util/Load_Model_from_GCP_Storage.ipynb index c6d7b18ae1c8..99488c112ad0 100644 --- a/examples/util/Load_Model_from_GCP_Storage.ipynb +++ b/examples/util/Load_Model_from_GCP_Storage.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/util/Load_Model_from_GCP_Storage.ipynb)" ] diff --git a/examples/util/Training_Helpers.ipynb b/examples/util/Training_Helpers.ipynb index 03c9bc06d0a4..c56759513f10 100644 --- a/examples/util/Training_Helpers.ipynb +++ b/examples/util/Training_Helpers.ipynb @@ -11,7 +11,7 @@ "outputId": "5b622949-97be-460f-9deb-21a4a39c1667" }, "source": [ - "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n", + "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/util/Training_Helpers.ipynb)" ] diff --git a/python/README.md b/python/README.md index 14da5a95cec1..fa5682b3092c 100644 --- a/python/README.md +++ b/python/README.md @@ -1100,7 +1100,7 @@ ResourceDownloader.showPublicPipelines(lang = "en", version = "3.1.0") */ ``` -#### Please check out our Models Hub for the full list of [pre-trained pipelines](https://nlp.johnsnowlabs.com/models) with examples, demos, benchmarks, and more +#### Please check out our Models Hub for the full list of [pre-trained pipelines](https://sparknlp.org/models) with examples, demos, benchmarks, and more ### Models @@ -1190,7 +1190,7 @@ XlnetEmbeddings */ ``` -#### Please check out our Models Hub for the full list of [pre-trained models](https://nlp.johnsnowlabs.com/models) with examples, demo, benchmark, and more +#### Please check out our Models Hub for the full list of [pre-trained models](https://sparknlp.org/models) with examples, demo, benchmark, and more ## Offline @@ -1201,7 +1201,7 @@ any limitations offline: - Instead of using the Maven package, you need to load our Fat JAR - Instead of using PretrainedPipeline for pretrained pipelines or the `.pretrained()` function to download pretrained - models, you will need to manually download your pipeline/model from [Models Hub](https://nlp.johnsnowlabs.com/models), + models, you will need to manually download your pipeline/model from [Models Hub](https://sparknlp.org/models), extract it, and load it. Example of `SparkSession` with Fat JAR to have Spark NLP offline: @@ -1252,13 +1252,13 @@ PipelineModel.load("/tmp/explain_document_dl_en_2.0.2_2.4_1556530585689/") Need more **examples**? Check out our dedicated [Spark NLP Examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples) repository to showcase all Spark NLP use cases! -Also, don't forget to check [Spark NLP in Action](https://nlp.johnsnowlabs.com/demo) built by Streamlit. +Also, don't forget to check [Spark NLP in Action](https://sparknlp.org/demo) built by Streamlit. ### All examples: [spark-nlp/examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples) ## FAQ -[Check our Articles and Videos page here](https://nlp.johnsnowlabs.com/learn) +[Check our Articles and Videos page here](https://sparknlp.org/learn) ## Citation diff --git a/python/docs/index.rst b/python/docs/index.rst index da71ff4474e2..e388557b1e77 100644 --- a/python/docs/index.rst +++ b/python/docs/index.rst @@ -21,7 +21,7 @@ Spark NLP Documentation ####################### -`Main Page `_ | `GitHub `_ | `Issues `_ | `Workshop `_ | `Models Hub `_ +`Main Page `_ | `GitHub `_ | `Issues `_ | `Workshop `_ | `Models Hub `_ Welcome to Spark NLP's Python documentation! This page contains information how to use the library with examples. diff --git a/python/docs/user_guide/annotators.rst b/python/docs/user_guide/annotators.rst index 09f18e66c4e7..2f32e71d5899 100644 --- a/python/docs/user_guide/annotators.rst +++ b/python/docs/user_guide/annotators.rst @@ -146,7 +146,7 @@ Sometimes we offer more than one model, in which case, you may have to use name, location to download them. For a complete list of available pretrained models, head to the `Spark NLP Models -`__. Alternatively you can also check for pretrained +`__. Alternatively you can also check for pretrained models of a particular annotator using :meth:`.ResourceDownloader.showPublicModels`. >>> ResourceDownloader.showPublicModels("ClassifierDLModel", "en") diff --git a/python/docs/user_guide/index.rst b/python/docs/user_guide/index.rst index d9e4b4223b22..5ad56d0853a3 100644 --- a/python/docs/user_guide/index.rst +++ b/python/docs/user_guide/index.rst @@ -4,7 +4,7 @@ User Guide This guide is an overview and explains the important features of Spark NLP. The in-depth documentation can be found in the :doc:`/reference/index`. -For more explanations, see also the `Main Page `_ +For more explanations, see also the `Main Page `_ and the `Workshop `_ for more examples. .. toctree:: diff --git a/python/docs/user_guide/pretrained_pipelines.rst b/python/docs/user_guide/pretrained_pipelines.rst index b3d3dbf2f20d..fb3cad6faf95 100644 --- a/python/docs/user_guide/pretrained_pipelines.rst +++ b/python/docs/user_guide/pretrained_pipelines.rst @@ -50,7 +50,7 @@ As a Spark NLP LightPipeline Available Pipelines =================== -Please see the `Pipelines Page `_ for all available pipelines. +Please see the `Pipelines Page `_ for all available pipelines. Alternatively you can also check for pretrained pipelines using :meth:`.ResourceDownloader.showPublicPipelines`. diff --git a/python/sparknlp/annotator/audio/hubert_for_ctc.py b/python/sparknlp/annotator/audio/hubert_for_ctc.py index 23df5c469031..0f7549e712ae 100644 --- a/python/sparknlp/annotator/audio/hubert_for_ctc.py +++ b/python/sparknlp/annotator/audio/hubert_for_ctc.py @@ -43,7 +43,7 @@ class HubertForCTC(AnnotatorModel, The default model is ``"asr_hubert_large_ls960"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended diff --git a/python/sparknlp/annotator/audio/wav2vec2_for_ctc.py b/python/sparknlp/annotator/audio/wav2vec2_for_ctc.py index 317048b6b2c8..c1937df094e5 100644 --- a/python/sparknlp/annotator/audio/wav2vec2_for_ctc.py +++ b/python/sparknlp/annotator/audio/wav2vec2_for_ctc.py @@ -21,46 +21,46 @@ class Wav2Vec2ForCTC(AnnotatorModel, HasBatchedAnnotateAudio, HasAudioFeatureProperties, HasEngine): - """Wav2Vec2 Model with a language modeling head on top for Connectionist Temporal + """Wav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC). Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli. - + The annotator takes audio files and transcribes it as text. The audio needs to be provided pre-processed an array of floats. - + Note that this annotator is currently not supported on Apple Silicon processors such as the M1. This is due to the processor not supporting instructions for XLA. - + Pretrained models can be loaded with ``pretrained`` of the companion object: - + >>> speechToText = Wav2Vec2ForCTC.pretrained() \\ ... .setInputCols(["audio_assembler"]) \\ ... .setOutputCol("text") - - + + The default model is ``"asr_wav2vec2_base_960h"``, if no name is provided. - + For available pretrained models please see the - `Models Hub `__. - + `Models Hub `__. + To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see `Wav2Vec2ForCTCTestSpec `__. - + ====================== ====================== Input Annotation types Output Annotation type ====================== ====================== ``AUDIO`` ``DOCUMENT`` ====================== ====================== - + Parameters ---------- - + batchSize Size of each batch, by default 2 - + Examples -------- >>> import sparknlp diff --git a/python/sparknlp/annotator/classifier_dl/albert_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/albert_for_question_answering.py index 6ab669d68445..358c56cfd060 100755 --- a/python/sparknlp/annotator/classifier_dl/albert_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/albert_for_question_answering.py @@ -34,7 +34,7 @@ class AlbertForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/albert_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/albert_for_sequence_classification.py index 3878285d9936..473b1b028d63 100755 --- a/python/sparknlp/annotator/classifier_dl/albert_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/albert_for_sequence_classification.py @@ -35,7 +35,7 @@ class AlbertForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 @@ -61,7 +61,7 @@ class AlbertForSequenceClassification(AnnotatorModel, Max sentence length to process, by default 128 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output - 1 class per document by averaging probabilities in all sentences, by + 1 class per document by averaging probabilities in all sentences, by default False. activation Whether to calculate logits via Softmax or Sigmoid, by default diff --git a/python/sparknlp/annotator/classifier_dl/albert_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/albert_for_token_classification.py index 2fb90576b657..eedaf3f98edc 100755 --- a/python/sparknlp/annotator/classifier_dl/albert_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/albert_for_token_classification.py @@ -34,7 +34,7 @@ class AlbertForTokenClassification(AnnotatorModel, The default model is ``"albert_base_token_classifier_conll03"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/bert_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/bert_for_question_answering.py index f1fe3c309e00..f8b4e95c2b1e 100755 --- a/python/sparknlp/annotator/classifier_dl/bert_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/bert_for_question_answering.py @@ -34,7 +34,7 @@ class BertForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/bert_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/bert_for_sequence_classification.py index c06b6ea1767f..959afdea2923 100755 --- a/python/sparknlp/annotator/classifier_dl/bert_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/bert_for_sequence_classification.py @@ -36,7 +36,7 @@ class BertForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/bert_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/bert_for_token_classification.py index ee60b600f328..2c1d39bf06a1 100755 --- a/python/sparknlp/annotator/classifier_dl/bert_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/bert_for_token_classification.py @@ -35,7 +35,7 @@ class BertForTokenClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py b/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py index 532fad53510d..bf03580f80e1 100755 --- a/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py +++ b/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py @@ -41,7 +41,7 @@ class BertForZeroShotClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/camembert_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/camembert_for_question_answering.py index 603c7f525161..7ea479326eec 100755 --- a/python/sparknlp/annotator/classifier_dl/camembert_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/camembert_for_question_answering.py @@ -34,7 +34,7 @@ class CamemBertForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/camembert_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/camembert_for_sequence_classification.py index 94f5d4b84dee..6defc9687240 100644 --- a/python/sparknlp/annotator/classifier_dl/camembert_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/camembert_for_sequence_classification.py @@ -36,7 +36,7 @@ class CamemBertForSequenceClassification(AnnotatorModel, name is provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 `_. diff --git a/python/sparknlp/annotator/classifier_dl/camembert_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/camembert_for_token_classification.py index 58634275628d..9c7445c031bd 100755 --- a/python/sparknlp/annotator/classifier_dl/camembert_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/camembert_for_token_classification.py @@ -34,7 +34,7 @@ class CamemBertForTokenClassification(AnnotatorModel, name is provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 `_. diff --git a/python/sparknlp/annotator/classifier_dl/classifier_dl.py b/python/sparknlp/annotator/classifier_dl/classifier_dl.py index 1e06a6d0a8c0..90b30db9543a 100755 --- a/python/sparknlp/annotator/classifier_dl/classifier_dl.py +++ b/python/sparknlp/annotator/classifier_dl/classifier_dl.py @@ -203,7 +203,7 @@ class ClassifierDLModel(AnnotatorModel, HasStorageRef, HasEngine): dataset. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/classifier_dl/deberta_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/deberta_for_question_answering.py index e7992fe645ce..10427ba0f112 100755 --- a/python/sparknlp/annotator/classifier_dl/deberta_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/deberta_for_question_answering.py @@ -34,7 +34,7 @@ class DeBertaForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/deberta_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/deberta_for_sequence_classification.py index b4327609e68d..cb3153f9ecfc 100755 --- a/python/sparknlp/annotator/classifier_dl/deberta_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/deberta_for_sequence_classification.py @@ -34,7 +34,7 @@ class DeBertaForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 @@ -60,7 +60,7 @@ class DeBertaForSequenceClassification(AnnotatorModel, Max sentence length to process, by default 128 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output - 1 class per document by averaging probabilities in all sentences, by + 1 class per document by averaging probabilities in all sentences, by default False. Examples diff --git a/python/sparknlp/annotator/classifier_dl/deberta_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/deberta_for_token_classification.py index 8bd5d560ca6e..f464b3a0c856 100755 --- a/python/sparknlp/annotator/classifier_dl/deberta_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/deberta_for_token_classification.py @@ -35,7 +35,7 @@ class DeBertaForTokenClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/distil_bert_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/distil_bert_for_question_answering.py index 216bba6c33c2..b16f616520eb 100755 --- a/python/sparknlp/annotator/classifier_dl/distil_bert_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/distil_bert_for_question_answering.py @@ -34,7 +34,7 @@ class DistilBertForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/distil_bert_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/distil_bert_for_sequence_classification.py index 899bddacad6f..a3c0d446181c 100755 --- a/python/sparknlp/annotator/classifier_dl/distil_bert_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/distil_bert_for_sequence_classification.py @@ -35,7 +35,7 @@ class DistilBertForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 @@ -61,7 +61,7 @@ class DistilBertForSequenceClassification(AnnotatorModel, Max sentence length to process, by default 128 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output - 1 class per document by averaging probabilities in all sentences, by + 1 class per document by averaging probabilities in all sentences, by default False. activation Whether to calculate logits via Softmax or Sigmoid, by default diff --git a/python/sparknlp/annotator/classifier_dl/distil_bert_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/distil_bert_for_token_classification.py index a9a92674ae13..218ba5336bed 100755 --- a/python/sparknlp/annotator/classifier_dl/distil_bert_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/distil_bert_for_token_classification.py @@ -35,7 +35,7 @@ class DistilBertForTokenClassification(AnnotatorModel, name is provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/longformer_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/longformer_for_question_answering.py index 3126e5948c2b..a0ead26735fc 100755 --- a/python/sparknlp/annotator/classifier_dl/longformer_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/longformer_for_question_answering.py @@ -34,7 +34,7 @@ class LongformerForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/longformer_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/longformer_for_sequence_classification.py index 5b48b6c26bad..435a851501dd 100755 --- a/python/sparknlp/annotator/classifier_dl/longformer_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/longformer_for_sequence_classification.py @@ -35,7 +35,7 @@ class LongformerForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 @@ -61,7 +61,7 @@ class LongformerForSequenceClassification(AnnotatorModel, Max sentence length to process, by default 4096 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output - 1 class per document by averaging probabilities in all sentences, by + 1 class per document by averaging probabilities in all sentences, by default False. activation Whether to calculate logits via Softmax or Sigmoid, by default diff --git a/python/sparknlp/annotator/classifier_dl/longformer_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/longformer_for_token_classification.py index f7b7222c8d63..bb5d00705d97 100755 --- a/python/sparknlp/annotator/classifier_dl/longformer_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/longformer_for_token_classification.py @@ -35,7 +35,7 @@ class LongformerForTokenClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/multi_classifier_dl.py b/python/sparknlp/annotator/classifier_dl/multi_classifier_dl.py index 7ca27a9cc471..51922a2afbd2 100755 --- a/python/sparknlp/annotator/classifier_dl/multi_classifier_dl.py +++ b/python/sparknlp/annotator/classifier_dl/multi_classifier_dl.py @@ -265,7 +265,7 @@ class MultiClassifierDLModel(AnnotatorModel, HasStorageRef, HasEngine): The data is based on the `Jigsaw Toxic Comment Classification Challenge `__. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/classifier_dl/roberta_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/roberta_for_question_answering.py index d2b1d30a8a51..19c042e32fe6 100755 --- a/python/sparknlp/annotator/classifier_dl/roberta_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/roberta_for_question_answering.py @@ -34,7 +34,7 @@ class RoBertaForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/roberta_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/roberta_for_sequence_classification.py index 2ef1283e513d..eeb2c3e8032c 100755 --- a/python/sparknlp/annotator/classifier_dl/roberta_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/roberta_for_sequence_classification.py @@ -35,7 +35,7 @@ class RoBertaForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 @@ -61,7 +61,7 @@ class RoBertaForSequenceClassification(AnnotatorModel, Max sentence length to process, by default 128 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output - 1 class per document by averaging probabilities in all sentences, by + 1 class per document by averaging probabilities in all sentences, by default False. activation Whether to calculate logits via Softmax or Sigmoid, by default diff --git a/python/sparknlp/annotator/classifier_dl/roberta_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/roberta_for_token_classification.py index 055af2628136..6cc071a29092 100755 --- a/python/sparknlp/annotator/classifier_dl/roberta_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/roberta_for_token_classification.py @@ -35,7 +35,7 @@ class RoBertaForTokenClassification(AnnotatorModel, is provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/sentiment_dl.py b/python/sparknlp/annotator/classifier_dl/sentiment_dl.py index dcad6d605a86..362df774e6d8 100755 --- a/python/sparknlp/annotator/classifier_dl/sentiment_dl.py +++ b/python/sparknlp/annotator/classifier_dl/sentiment_dl.py @@ -233,7 +233,7 @@ class SentimentDLModel(AnnotatorModel, HasStorageRef, HasEngine): The default model is ``"sentimentdl_use_imdb"``, if no name is provided. It is english sentiment analysis trained on the IMDB dataset. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/classifier_dl/tapas_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/tapas_for_question_answering.py index ee4b84e07a11..5911d94d2f92 100644 --- a/python/sparknlp/annotator/classifier_dl/tapas_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/tapas_for_question_answering.py @@ -33,7 +33,7 @@ class TapasForQuestionAnswering(BertForQuestionAnswering): is provided. For available pretrained models please see the `Models Hub - `__. + `__. ====================== ====================== Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_question_answering.py b/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_question_answering.py index b4ab6cc45ce5..e66b25785075 100755 --- a/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_question_answering.py +++ b/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_question_answering.py @@ -34,7 +34,7 @@ class XlmRoBertaForQuestionAnswering(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_sequence_classification.py index 0b3767b871dc..a7774e6f3884 100755 --- a/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_sequence_classification.py @@ -35,7 +35,7 @@ class XlmRoBertaForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 @@ -61,7 +61,7 @@ class XlmRoBertaForSequenceClassification(AnnotatorModel, Max sentence length to process, by default 128 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output - 1 class per document by averaging probabilities in all sentences, by + 1 class per document by averaging probabilities in all sentences, by default False. activation Whether to calculate logits via Softmax or Sigmoid, by default diff --git a/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_token_classification.py index f7043b8b4c6e..49b181e4c2dc 100755 --- a/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/xlm_roberta_for_token_classification.py @@ -34,7 +34,7 @@ class XlmRoBertaForTokenClassification(AnnotatorModel, name is provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 `_. diff --git a/python/sparknlp/annotator/classifier_dl/xlnet_for_sequence_classification.py b/python/sparknlp/annotator/classifier_dl/xlnet_for_sequence_classification.py index 992c42b101eb..11ecf8f13a31 100755 --- a/python/sparknlp/annotator/classifier_dl/xlnet_for_sequence_classification.py +++ b/python/sparknlp/annotator/classifier_dl/xlnet_for_sequence_classification.py @@ -35,7 +35,7 @@ class XlnetForSequenceClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 @@ -61,7 +61,7 @@ class XlnetForSequenceClassification(AnnotatorModel, Max sentence length to process, by default 128 coalesceSentences Instead of 1 class per sentence (if inputCols is `sentence`) output - 1 class per document by averaging probabilities in all sentences, by + 1 class per document by averaging probabilities in all sentences, by default False. activation Whether to calculate logits via Softmax or Sigmoid, by default diff --git a/python/sparknlp/annotator/classifier_dl/xlnet_for_token_classification.py b/python/sparknlp/annotator/classifier_dl/xlnet_for_token_classification.py index e101d1c61f4e..885395112853 100755 --- a/python/sparknlp/annotator/classifier_dl/xlnet_for_token_classification.py +++ b/python/sparknlp/annotator/classifier_dl/xlnet_for_token_classification.py @@ -35,7 +35,7 @@ class XlnetForTokenClassification(AnnotatorModel, provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/coref/spanbert_coref.py b/python/sparknlp/annotator/coref/spanbert_coref.py index a66e653e3b2c..c5e75a5a9327 100644 --- a/python/sparknlp/annotator/coref/spanbert_coref.py +++ b/python/sparknlp/annotator/coref/spanbert_coref.py @@ -38,7 +38,7 @@ class SpanBertCorefModel(AnnotatorModel, The default model is ``"spanbert_base_coref"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/cv/convnext_for_image_classification.py b/python/sparknlp/annotator/cv/convnext_for_image_classification.py index 86d1e99f4ab4..d86b71a3e448 100644 --- a/python/sparknlp/annotator/cv/convnext_for_image_classification.py +++ b/python/sparknlp/annotator/cv/convnext_for_image_classification.py @@ -29,7 +29,7 @@ class ConvNextForImageClassification(AnnotatorModel, them. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see diff --git a/python/sparknlp/annotator/cv/swin_for_image_classification.py b/python/sparknlp/annotator/cv/swin_for_image_classification.py index 388ef4b52858..bbbb74cc068b 100644 --- a/python/sparknlp/annotator/cv/swin_for_image_classification.py +++ b/python/sparknlp/annotator/cv/swin_for_image_classification.py @@ -43,7 +43,7 @@ class SwinForImageClassification(AnnotatorModel, provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see diff --git a/python/sparknlp/annotator/cv/vit_for_image_classification.py b/python/sparknlp/annotator/cv/vit_for_image_classification.py index 623420f681eb..d8ae15df3ffd 100644 --- a/python/sparknlp/annotator/cv/vit_for_image_classification.py +++ b/python/sparknlp/annotator/cv/vit_for_image_classification.py @@ -39,7 +39,7 @@ class ViTForImageClassification(AnnotatorModel, provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To see which models are compatible and how to import them see diff --git a/python/sparknlp/annotator/dependency/dependency_parser.py b/python/sparknlp/annotator/dependency/dependency_parser.py index 67e9e1d5075a..6a0789ae5e41 100755 --- a/python/sparknlp/annotator/dependency/dependency_parser.py +++ b/python/sparknlp/annotator/dependency/dependency_parser.py @@ -187,7 +187,7 @@ class DependencyParserModel(AnnotatorModel): The default model is ``"dependency_conllu"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/dependency/typed_dependency_parser.py b/python/sparknlp/annotator/dependency/typed_dependency_parser.py index 547e3cd9093b..8fb0476c29bd 100755 --- a/python/sparknlp/annotator/dependency/typed_dependency_parser.py +++ b/python/sparknlp/annotator/dependency/typed_dependency_parser.py @@ -193,7 +193,7 @@ class TypedDependencyParserModel(AnnotatorModel): The default model is ``"dependency_typed_conllu"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/bert_embeddings.py b/python/sparknlp/annotator/embeddings/bert_embeddings.py index 05e207ace1ff..a55ea7744538 100755 --- a/python/sparknlp/annotator/embeddings/bert_embeddings.py +++ b/python/sparknlp/annotator/embeddings/bert_embeddings.py @@ -38,7 +38,7 @@ class BertEmbeddings(AnnotatorModel, The default model is ``"small_bert_L2_768"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/bert_sentence_embeddings.py b/python/sparknlp/annotator/embeddings/bert_sentence_embeddings.py index f27b78198842..700af88a8be8 100755 --- a/python/sparknlp/annotator/embeddings/bert_sentence_embeddings.py +++ b/python/sparknlp/annotator/embeddings/bert_sentence_embeddings.py @@ -38,7 +38,7 @@ class BertSentenceEmbeddings(AnnotatorModel, The default model is ``"sent_small_bert_L2_768"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/camembert_embeddings.py b/python/sparknlp/annotator/embeddings/camembert_embeddings.py index f43d4b597820..1171d6b4ea08 100755 --- a/python/sparknlp/annotator/embeddings/camembert_embeddings.py +++ b/python/sparknlp/annotator/embeddings/camembert_embeddings.py @@ -39,7 +39,7 @@ class CamemBertEmbeddings(AnnotatorModel, The default model is ``"camembert_base"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. For extended examples of usage, see the `Examples `__ diff --git a/python/sparknlp/annotator/embeddings/distil_bert_embeddings.py b/python/sparknlp/annotator/embeddings/distil_bert_embeddings.py index 52246a4ef840..0afc3a7cedc7 100755 --- a/python/sparknlp/annotator/embeddings/distil_bert_embeddings.py +++ b/python/sparknlp/annotator/embeddings/distil_bert_embeddings.py @@ -37,7 +37,7 @@ class DistilBertEmbeddings(AnnotatorModel, The default model is ``"distilbert_base_cased"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/doc2vec.py b/python/sparknlp/annotator/embeddings/doc2vec.py index 161d17cfa349..da63d575996d 100755 --- a/python/sparknlp/annotator/embeddings/doc2vec.py +++ b/python/sparknlp/annotator/embeddings/doc2vec.py @@ -31,7 +31,7 @@ class Doc2VecApproach(AnnotatorApproach, HasStorageRef, HasEnableCachingProperti For instantiated/pretrained models, see :class:`.Doc2VecModel`. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. ====================== ======================= Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/embeddings/elmo_embeddings.py b/python/sparknlp/annotator/embeddings/elmo_embeddings.py index c4ecb7bc8088..46b9858e84f6 100755 --- a/python/sparknlp/annotator/embeddings/elmo_embeddings.py +++ b/python/sparknlp/annotator/embeddings/elmo_embeddings.py @@ -38,7 +38,7 @@ class ElmoEmbeddings(AnnotatorModel, The default model is ``"elmo"``, if no name is provided. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. The pooling layer can be set with :meth:`.setPoolingLayer` to the following values: diff --git a/python/sparknlp/annotator/embeddings/longformer_embeddings.py b/python/sparknlp/annotator/embeddings/longformer_embeddings.py index 8a240cdd3fa0..cab8fab4f3c4 100755 --- a/python/sparknlp/annotator/embeddings/longformer_embeddings.py +++ b/python/sparknlp/annotator/embeddings/longformer_embeddings.py @@ -38,7 +38,7 @@ class LongformerEmbeddings(AnnotatorModel, The default model is ``"longformer_base_4096"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. To see which models are compatible and how to import them see `Import Transformers into Spark NLP 🚀 diff --git a/python/sparknlp/annotator/embeddings/roberta_embeddings.py b/python/sparknlp/annotator/embeddings/roberta_embeddings.py index 6c9ba60446e0..24c6596e5c52 100755 --- a/python/sparknlp/annotator/embeddings/roberta_embeddings.py +++ b/python/sparknlp/annotator/embeddings/roberta_embeddings.py @@ -42,7 +42,7 @@ class RoBertaEmbeddings(AnnotatorModel, The default model is ``"roberta_base"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/roberta_sentence_embeddings.py b/python/sparknlp/annotator/embeddings/roberta_sentence_embeddings.py index bc705d6fca48..b1ca8fd3fbc8 100755 --- a/python/sparknlp/annotator/embeddings/roberta_sentence_embeddings.py +++ b/python/sparknlp/annotator/embeddings/roberta_sentence_embeddings.py @@ -39,7 +39,7 @@ class RoBertaSentenceEmbeddings(AnnotatorModel, The default model is ``"sent_roberta_base"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. ====================== ======================= Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/embeddings/universal_sentence_encoder.py b/python/sparknlp/annotator/embeddings/universal_sentence_encoder.py index a1624dbf62ad..ae155bba436f 100755 --- a/python/sparknlp/annotator/embeddings/universal_sentence_encoder.py +++ b/python/sparknlp/annotator/embeddings/universal_sentence_encoder.py @@ -35,7 +35,7 @@ class UniversalSentenceEncoder(AnnotatorModel, The default model is ``"tfhub_use"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/word2vec.py b/python/sparknlp/annotator/embeddings/word2vec.py index 537cecbec7de..e3e52f32f00c 100755 --- a/python/sparknlp/annotator/embeddings/word2vec.py +++ b/python/sparknlp/annotator/embeddings/word2vec.py @@ -32,7 +32,7 @@ class Word2VecApproach(AnnotatorApproach, HasStorageRef, HasEnableCachingPropert For instantiated/pretrained models, see :class:`.Word2VecModel`. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. ====================== ======================= Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/embeddings/word_embeddings.py b/python/sparknlp/annotator/embeddings/word_embeddings.py index 2f43a3047fb8..986c2822c0f8 100755 --- a/python/sparknlp/annotator/embeddings/word_embeddings.py +++ b/python/sparknlp/annotator/embeddings/word_embeddings.py @@ -177,7 +177,7 @@ class WordEmbeddingsModel(AnnotatorModel, HasEmbeddingsProperties, HasStorageMod The default model is ``"glove_100d"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/xlm_roberta_embeddings.py b/python/sparknlp/annotator/embeddings/xlm_roberta_embeddings.py index f8aecb86013a..46fa091ba056 100755 --- a/python/sparknlp/annotator/embeddings/xlm_roberta_embeddings.py +++ b/python/sparknlp/annotator/embeddings/xlm_roberta_embeddings.py @@ -40,7 +40,7 @@ class XlmRoBertaEmbeddings(AnnotatorModel, The default model is ``"xlm_roberta_base"``, default language is ``"xx"`` (meaning multi-lingual), if no values are provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings.py b/python/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings.py index db4708cf1e2a..4d65e20f6ba7 100755 --- a/python/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings.py +++ b/python/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings.py @@ -39,7 +39,7 @@ class XlmRoBertaSentenceEmbeddings(AnnotatorModel, The default model is ``"sent_xlm_roberta_base"``, if no name is provided. For available pretrained models please see the - `Models Hub `__. + `Models Hub `__. ====================== ======================= Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/ld_dl/language_detector_dl.py b/python/sparknlp/annotator/ld_dl/language_detector_dl.py index b917c3db6341..462b321dd4c6 100755 --- a/python/sparknlp/annotator/ld_dl/language_detector_dl.py +++ b/python/sparknlp/annotator/ld_dl/language_detector_dl.py @@ -37,7 +37,7 @@ class LanguageDetectorDL(AnnotatorModel, HasStorageRef, HasEngine): The default model is ``"ld_wiki_tatoeba_cnn_21"``, default language is ``"xx"`` (meaning multi-lingual), if no values are provided. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/lemmatizer.py b/python/sparknlp/annotator/lemmatizer.py index f1e80e8b44c3..85d4dc9b3b7a 100755 --- a/python/sparknlp/annotator/lemmatizer.py +++ b/python/sparknlp/annotator/lemmatizer.py @@ -24,7 +24,7 @@ class Lemmatizer(AnnotatorApproach): For instantiated/pretrained models, see :class:`.LemmatizerModel`. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. For extended examples of usage, see the `Examples `__. ====================== ====================== @@ -194,7 +194,7 @@ class LemmatizerModel(AnnotatorModel): ... .setInputCols(["token"]) \\ ... .setOutputCol("lemma") - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. ====================== ====================== Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/matcher/date_matcher.py b/python/sparknlp/annotator/matcher/date_matcher.py index 1d1d54fdd826..55c380104482 100755 --- a/python/sparknlp/annotator/matcher/date_matcher.py +++ b/python/sparknlp/annotator/matcher/date_matcher.py @@ -184,7 +184,7 @@ class DateMatcher(AnnotatorModel, DateMatcherUtils): ``2008/04/31``. Pretrained pipelines are available for this module, see - `Pipelines `__. + `Pipelines `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/matcher/regex_matcher.py b/python/sparknlp/annotator/matcher/regex_matcher.py index f9fc8a73c4cc..332e0835f5e7 100755 --- a/python/sparknlp/annotator/matcher/regex_matcher.py +++ b/python/sparknlp/annotator/matcher/regex_matcher.py @@ -32,7 +32,7 @@ class RegexMatcher(AnnotatorApproach): delimited text file. Pretrained pipelines are available for this module, see `Pipelines - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/ner/ner_crf.py b/python/sparknlp/annotator/ner/ner_crf.py index d0829332f141..09b08222d7b8 100755 --- a/python/sparknlp/annotator/ner/ner_crf.py +++ b/python/sparknlp/annotator/ner/ner_crf.py @@ -278,7 +278,7 @@ class NerCrfModel(AnnotatorModel): The default model is ``"ner_crf"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/ner/ner_dl.py b/python/sparknlp/annotator/ner/ner_dl.py index ea87002082ea..48dd6301ac85 100755 --- a/python/sparknlp/annotator/ner/ner_dl.py +++ b/python/sparknlp/annotator/ner/ner_dl.py @@ -420,9 +420,9 @@ class NerDLModel(AnnotatorModel, HasStorageRef, HasBatchedAnnotate, HasEngine): The default model is ``"ner_dl"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. Additionally, pretrained pipelines are available for this module, see - `Pipelines `__. + `Pipelines `__. Note that some pretrained models require specific types of embeddings, depending on which they were trained on. For example, the default model diff --git a/python/sparknlp/annotator/pos/perceptron.py b/python/sparknlp/annotator/pos/perceptron.py index 4a67e4e3d2b4..1c461df7f9bd 100755 --- a/python/sparknlp/annotator/pos/perceptron.py +++ b/python/sparknlp/annotator/pos/perceptron.py @@ -174,9 +174,9 @@ class PerceptronModel(AnnotatorModel): The default model is ``"pos_anc"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. Additionally, pretrained pipelines are available for this module, see - `Pipelines `__. + `Pipelines `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/sentence/sentence_detector_dl.py b/python/sparknlp/annotator/sentence/sentence_detector_dl.py index 703c461d41e3..6e7051e1a6dd 100755 --- a/python/sparknlp/annotator/sentence/sentence_detector_dl.py +++ b/python/sparknlp/annotator/sentence/sentence_detector_dl.py @@ -217,7 +217,7 @@ class SentenceDetectorDLModel(AnnotatorModel, HasEngine): The default model is ``"sentence_detector_dl"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. Each extracted sentence can be returned in an Array or exploded to separate rows, if ``explodeSentences`` is set to ``true``. diff --git a/python/sparknlp/annotator/seq2seq/bart_transformer.py b/python/sparknlp/annotator/seq2seq/bart_transformer.py index 35cb40d13fe9..04e177be029a 100755 --- a/python/sparknlp/annotator/seq2seq/bart_transformer.py +++ b/python/sparknlp/annotator/seq2seq/bart_transformer.py @@ -24,22 +24,22 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. - + BART is unique in that it is both bidirectional and auto-regressive, meaning that it can generate text both from left-to-right and from right-to-left. This allows it to capture contextual information from both past and future tokens in a sentence,resulting in more accurate and natural language generation. - + The model was trained on a large corpus of text data using a combination of unsupervised and supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the model is first trained on a large unlabeled corpus of text, and then fine-tuned on specific downstream tasks. - + BART has achieved state-of-the-art performance on a wide range of NLP tasks, including summarization, question-answering, and language translation. Its ability to handle multiple tasks and its high performance on each of these tasks make it a versatile and valuable tool for natural language processing applications. - + Pretrained models can be loaded with :meth:`.pretrained` of the companion object: @@ -52,7 +52,7 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): The default model is ``"distilbart_xsum_12_6"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/seq2seq/gpt2_transformer.py b/python/sparknlp/annotator/seq2seq/gpt2_transformer.py index 21313eafe8a7..346050c7ac0d 100755 --- a/python/sparknlp/annotator/seq2seq/gpt2_transformer.py +++ b/python/sparknlp/annotator/seq2seq/gpt2_transformer.py @@ -43,7 +43,7 @@ class GPT2Transformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): The default model is ``"gpt2"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. ====================== ====================== Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/seq2seq/marian_transformer.py b/python/sparknlp/annotator/seq2seq/marian_transformer.py index 0e860f724396..bc9c318aecf3 100755 --- a/python/sparknlp/annotator/seq2seq/marian_transformer.py +++ b/python/sparknlp/annotator/seq2seq/marian_transformer.py @@ -40,7 +40,7 @@ class MarianTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): The default model is ``"opus_mt_en_fr"``, default language is ``"xx"`` (meaning multi-lingual), if no values are provided. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/seq2seq/t5_transformer.py b/python/sparknlp/annotator/seq2seq/t5_transformer.py index 621e0f95940e..6541bfd90799 100755 --- a/python/sparknlp/annotator/seq2seq/t5_transformer.py +++ b/python/sparknlp/annotator/seq2seq/t5_transformer.py @@ -39,7 +39,7 @@ class T5Transformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): The default model is ``"t5_small"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/spell_check/context_spell_checker.py b/python/sparknlp/annotator/spell_check/context_spell_checker.py index 79b3533d5ceb..1c41a13796f0 100755 --- a/python/sparknlp/annotator/spell_check/context_spell_checker.py +++ b/python/sparknlp/annotator/spell_check/context_spell_checker.py @@ -438,7 +438,7 @@ def setGraphFolder(self, path): Folder path that contain external graph files. """ return self._set(graphFolder=path) - + def setMaxSentLen(self, sentlen): """Sets the maximum length of a sentence. @@ -516,7 +516,7 @@ class ContextSpellCheckerModel(AnnotatorModel, HasEngine): The default model is ``"spellcheck_dl"``, if no name is provided. - For available pretrained models please see the `Models Hub `__. + For available pretrained models please see the `Models Hub `__. For extended examples of usage, see the `Examples `__. @@ -562,7 +562,7 @@ class ContextSpellCheckerModel(AnnotatorModel, HasEngine): Levenshtein weights. useNewLines When set to true new lines will be treated as any other character. When set to false correction is applied on paragraphs as defined by newline characters. - + References ------------- @@ -796,7 +796,7 @@ def setIdsVocab(self, idsVocab: dict): Mapping of ids to vocabulary. """ return self._set(idsVocab=idsVocab) - + def setVocabIds(self, vocabIds: dict): """Sets mapping of vocabulary to ids. diff --git a/python/sparknlp/annotator/spell_check/norvig_sweeting.py b/python/sparknlp/annotator/spell_check/norvig_sweeting.py index 6e23c96ace44..74135d25fdfd 100755 --- a/python/sparknlp/annotator/spell_check/norvig_sweeting.py +++ b/python/sparknlp/annotator/spell_check/norvig_sweeting.py @@ -267,7 +267,7 @@ class NorvigSweetingModel(AnnotatorModel): The default model is ``"spellcheck_norvig"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples diff --git a/python/sparknlp/annotator/spell_check/symmetric_delete.py b/python/sparknlp/annotator/spell_check/symmetric_delete.py index 5e7c9dcf896f..34862efb9b35 100755 --- a/python/sparknlp/annotator/spell_check/symmetric_delete.py +++ b/python/sparknlp/annotator/spell_check/symmetric_delete.py @@ -212,7 +212,7 @@ class SymmetricDeleteModel(AnnotatorModel): The default model is ``"spellcheck_sd"``, if no name is provided. For available pretrained models please see the `Models Hub - `__. + `__. ====================== ====================== Input Annotation types Output Annotation type diff --git a/python/sparknlp/annotator/stop_words_cleaner.py b/python/sparknlp/annotator/stop_words_cleaner.py index 881efaf0507d..63536d394d4b 100755 --- a/python/sparknlp/annotator/stop_words_cleaner.py +++ b/python/sparknlp/annotator/stop_words_cleaner.py @@ -34,7 +34,7 @@ class StopWordsCleaner(AnnotatorModel): This will load the default pretrained model ``"stopwords_en"``. For available pretrained models please see the `Models Hub - `__. + `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/annotator/ws/word_segmenter.py b/python/sparknlp/annotator/ws/word_segmenter.py index 78f6d0854cc7..188de3bb21b3 100755 --- a/python/sparknlp/annotator/ws/word_segmenter.py +++ b/python/sparknlp/annotator/ws/word_segmenter.py @@ -307,7 +307,7 @@ class WordSegmenterModel(AnnotatorModel): The default model is ``"wordseg_pku"``, default language is ``"zh"``, if no values are provided. For available pretrained models please see the `Models - Hub `__. + Hub `__. For extended examples of usage, see the `Examples `__. diff --git a/python/sparknlp/pretrained/pretrained_pipeline.py b/python/sparknlp/pretrained/pretrained_pipeline.py index ab2784a9cede..18a217d15c1b 100644 --- a/python/sparknlp/pretrained/pretrained_pipeline.py +++ b/python/sparknlp/pretrained/pretrained_pipeline.py @@ -29,7 +29,7 @@ class PretrainedPipeline: :attr:`.light_model`. For more extended examples see the `Pipelines page - `_ and our `Github Model + `_ and our `Github Model Repository `_ for available pipeline models. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/DateMatcher.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/DateMatcher.scala index 31ef2d056a36..d0ae20ad26aa 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/DateMatcher.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/DateMatcher.scala @@ -43,7 +43,7 @@ import scala.util.matching.Regex * For example `"The 31st of April in the year 2008"` will be converted into `2008/04/31`. * * Pretrained pipelines are available for this module, see - * [[https://nlp.johnsnowlabs.com/docs/en/pipelines Pipelines]]. + * [[https://sparknlp.org/docs/en/pipelines Pipelines]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/MultiDateMatcherMultiLanguage_en.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/Lemmatizer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/Lemmatizer.scala index 95a33e212f04..9cd5df23f80a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/Lemmatizer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/Lemmatizer.scala @@ -36,7 +36,7 @@ import scala.collection.mutable * Pretrained models can be loaded with [[LemmatizerModel LemmatizerModel.pretrained]]. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Lemmatization Models Hub]]. For extended examples + * [[https://sparknlp.org/models?task=Lemmatization Models Hub]]. For extended examples * of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Train-Lemmatizer-Italian.ipynb Examples]] * and the diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/LemmatizerModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/LemmatizerModel.scala index 2d3bce939691..bd1f6cb676ac 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/LemmatizerModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/LemmatizerModel.scala @@ -23,7 +23,7 @@ import org.apache.spark.ml.util.Identifiable /** Instantiated Model of the [[Lemmatizer]]. For usage and examples, please see the documentation * of that class. For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Lemmatization Models Hub]]. + * [[https://sparknlp.org/models?task=Lemmatization Models Hub]]. * * ==Example== * The lemmatizer from the example of the [[Lemmatizer]] can be replaced with: diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/RegexMatcher.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/RegexMatcher.scala index 8f4d4abd8982..c6da14716be6 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/RegexMatcher.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/RegexMatcher.scala @@ -39,7 +39,7 @@ import org.apache.spark.sql.Dataset * or directly as an [[com.johnsnowlabs.nlp.util.io.ExternalResource ExternalResource]]. * * Pretrained pipelines are available for this module, see - * [[https://nlp.johnsnowlabs.com/docs/en/pipelines Pipelines]]. + * [[https://sparknlp.org/docs/en/pipelines Pipelines]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/regex-matcher/Matching_Text_with_RegexMatcher.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/StopWordsCleaner.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/StopWordsCleaner.scala index 8e01343d80eb..0fcc7bc8dd0d 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/StopWordsCleaner.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/StopWordsCleaner.scala @@ -40,7 +40,7 @@ import java.util.Locale * // will load the default pretrained model `"stopwords_en"`. * }}} * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Stop+Words+Removal Models Hub]]. + * [[https://sparknlp.org/models?task=Stop+Words+Removal Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/stop-words/StopWordsCleaner.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/HubertForCTC.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/HubertForCTC.scala index 04916405bfa4..b7613e2997af 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/HubertForCTC.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/HubertForCTC.scala @@ -50,7 +50,7 @@ import org.json4s.jackson.JsonMethods._ * The default model is `"asr_hubert_large_ls960"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models Models Hub]]. + * [[https://sparknlp.org/models Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/Wav2Vec2ForCTC.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/Wav2Vec2ForCTC.scala index 95367d99f90a..54bab95c12fe 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/Wav2Vec2ForCTC.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/audio/Wav2Vec2ForCTC.scala @@ -58,7 +58,7 @@ import org.json4s.jackson.JsonMethods._ * The default model is `"asr_wav2vec2_base_960h"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models Models Hub]]. + * [[https://sparknlp.org/models Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForQuestionAnswering.scala index 26608246a4f4..c9a659ff6947 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForQuestionAnswering.scala @@ -49,7 +49,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"albert_base_qa_squad2"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -91,7 +91,7 @@ import org.apache.spark.sql.SparkSession * @see * [[AlbertForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForSequenceClassification.scala index 9112666a78e2..4f19afeedbcd 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForSequenceClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"albert_base_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -99,7 +99,7 @@ import org.apache.spark.sql.SparkSession * @see * [[AlbertForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForTokenClassification.scala index 9f660dcb68cc..289244653896 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForTokenClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"albert_base_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * and the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForTokenClassificationTestSpec.scala AlbertForTokenClassificationTestSpec]]. @@ -98,7 +98,7 @@ import org.apache.spark.sql.SparkSession * @see * [[AlbertForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForQuestionAnswering.scala index b6d27fc7862c..436cb313027c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForQuestionAnswering.scala @@ -44,7 +44,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"bert_base_cased_qa_squad2"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To * see which models are compatible and how to import them see @@ -87,7 +87,7 @@ import org.apache.spark.sql.SparkSession * @see * [[BertForSequenceClassification]] for span-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala index f5d97fce5fcc..127c61decbfd 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForSequenceClassification.scala @@ -49,7 +49,7 @@ import java.io.File * The default model is `"bert_base_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -97,7 +97,7 @@ import java.io.File * @see * [[BertForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForTokenClassification.scala index 3e39961bdfff..5f9f963ce165 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForTokenClassification.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"bert_base_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -92,7 +92,7 @@ import org.apache.spark.sql.SparkSession * @see * [[BertForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala index b3c05c9d7e7f..054838b89225 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala @@ -53,7 +53,7 @@ import java.io.File * The default model is `"bert_base_cased_zero_shot_classifier_xnli"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -101,7 +101,7 @@ import java.io.File * @see * [[BertForZeroShotClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk @@ -400,7 +400,7 @@ trait ReadBertForZeroShotDLModel extends ReadTensorflowModel { require( entailmentIds.length == 1 && contradictionIds.length == 1, s"""This annotator supports classifiers trained on NLI datasets. You must have only at least 2 or maximum 3 labels in your dataset: - + example with 3 labels: 'contradict', 'neutral', 'entailment' example with 2 labels: 'contradict', 'entailment' diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForQuestionAnswering.scala index 31c34925c256..8d68a111699a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForQuestionAnswering.scala @@ -49,7 +49,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"camembert_base_qa_fquad"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -91,7 +91,7 @@ import org.apache.spark.sql.SparkSession * @see * [[CamemBertForQuestionAnswering]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForSequenceClassification.scala index e5cf40fc761f..03d62880a929 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForSequenceClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `camembert_base_sequence_classifier_allocine"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -99,7 +99,7 @@ import org.apache.spark.sql.SparkSession * @see * [[CamemBertForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForTokenClassification.scala index d5dafd5fc916..aaf48b0a36b2 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForTokenClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"camembert_base_token_classifier_wikiner"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * and the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForTokenClassificationTestSpec.scala CamemBertForTokenClassificationTestSpec]]. @@ -98,7 +98,7 @@ import org.apache.spark.sql.SparkSession * @see * [[CamemBertForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/ClassifierDLModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/ClassifierDLModel.scala index 32a5fb14caaf..9576c99c3a0d 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/ClassifierDLModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/ClassifierDLModel.scala @@ -48,7 +48,7 @@ import org.apache.spark.sql.{Dataset, SparkSession} * and is trained on the * [[https://deepai.org/dataset/trec-6#:~:text=The%20TREC%20dataset%20is%20dataset,50%20has%20finer%2Dgrained%20labels TREC-6]] * dataset. For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForQuestionAnswering.scala index e7721d1bed86..8816b5cbbb8c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForQuestionAnswering.scala @@ -49,7 +49,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"deberta_v3_xsmall_qa_squad2"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -91,7 +91,7 @@ import org.apache.spark.sql.SparkSession * @see * [[DeBertaForQuestionAnswering]] for span-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForSequenceClassification.scala index e77f8bb6b875..d3b254c824f4 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForSequenceClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"deberta_v3_xsmall_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -99,7 +99,7 @@ import org.apache.spark.sql.SparkSession * @see * [[DeBertaForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForTokenClassification.scala index b5717b14c6cd..6c851aa2c68c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForTokenClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"deberta_v3_xsmall_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * and the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForTokenClassificationTestSpec.scala DeBertaForTokenClassificationTestSpec]]. @@ -99,7 +99,7 @@ import org.apache.spark.sql.SparkSession * @see * [[DeBertaForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForQuestionAnswering.scala index 031d8a5a3c5b..60e814350a77 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForQuestionAnswering.scala @@ -44,7 +44,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"distilbert_base_cased_qa_squad2"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -86,7 +86,7 @@ import org.apache.spark.sql.SparkSession * @see * [[DistilBertForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForSequenceClassification.scala index c978284e7fd0..7e152abfeb31 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForSequenceClassification.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"distilbert_base_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -93,7 +93,7 @@ import org.apache.spark.sql.SparkSession * @see * [[DistilBertForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForTokenClassification.scala index 8643f523be7d..c5c517d2d506 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DistilBertForTokenClassification.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"distilbert_base_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -92,7 +92,7 @@ import org.apache.spark.sql.SparkSession * @see * [[DistilBertForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForQuestionAnswering.scala index b7b0991e34a7..c668da21a09a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForQuestionAnswering.scala @@ -44,7 +44,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"longformer_base_base_qa_squad2"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -86,7 +86,7 @@ import org.apache.spark.sql.SparkSession * @see * [[LongformerForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForSequenceClassification.scala index 85c420685423..8783bebc6c11 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForSequenceClassification.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"longformer_base_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -93,7 +93,7 @@ import org.apache.spark.sql.SparkSession * @see * [[LongformerForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForTokenClassification.scala index 4738190f4b0f..605044b129b1 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForTokenClassification.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"longformer_base_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * and the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/LongformerForTokenClassificationTestSpec.scala LongformerForTokenClassificationTestSpec]]. @@ -92,7 +92,7 @@ import org.apache.spark.sql.SparkSession * @see * [[LongformerForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/MultiClassifierDLModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/MultiClassifierDLModel.scala index 87e5abd95f59..734dab21288c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/MultiClassifierDLModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/MultiClassifierDLModel.scala @@ -52,7 +52,7 @@ import org.apache.spark.sql.{Dataset, SparkSession} * classifies toxic comments. The data is based on the * [[https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/overview Jigsaw Toxic Comment Classification Challenge]]. * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * In machine learning, multi-label classification and the strongly related problem of * multi-output classification are variants of the classification problem where multiple labels diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForQuestionAnswering.scala index 3e41ece75484..776884ba0453 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForQuestionAnswering.scala @@ -44,7 +44,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"roberta_base_qa_squad2"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -86,7 +86,7 @@ import org.apache.spark.sql.SparkSession * @see * [[RoBertaForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForSequenceClassification.scala index b0ca76a2bb70..9396c8a31430 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForSequenceClassification.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"roberta_base_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -93,7 +93,7 @@ import org.apache.spark.sql.SparkSession * @see * [[RoBertaForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForTokenClassification.scala index 74f1af3e1939..2de35736a509 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForTokenClassification.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"roberta_base_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * and the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/RoBertaForTokenClassificationTestSpec.scala RoBertaForTokenClassificationTestSpec]]. @@ -92,7 +92,7 @@ import org.apache.spark.sql.SparkSession * @see * [[RoBertaForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/SentimentDLModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/SentimentDLModel.scala index 2bc1b1a97a8e..5a4fb2518aa4 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/SentimentDLModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/SentimentDLModel.scala @@ -45,7 +45,7 @@ import org.apache.spark.sql.{Dataset, SparkSession} * }}} * The default model is `"sentimentdl_use_imdb"`, if no name is provided. It is english sentiment * analysis trained on the IMDB dataset. For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Sentiment+Analysis Models Hub]]. + * [[https://sparknlp.org/models?task=Sentiment+Analysis Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/SentimentDL_train_multiclass_sentiment_classifier.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/TapasForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/TapasForQuestionAnswering.scala index 96fba2a4b316..acc1bec59d9c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/TapasForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/TapasForQuestionAnswering.scala @@ -44,7 +44,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"table_qa_tapas_base_finetuned_wtq"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Table+Question+Understanding Models Hub]]. + * [[https://sparknlp.org/models?task=Table+Question+Understanding Models Hub]]. * * ==Example== * {{{ @@ -126,7 +126,7 @@ import org.apache.spark.sql.SparkSession * @see * [[TableAssembler]] for loading tabular data * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForQuestionAnswering.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForQuestionAnswering.scala index efe7f7036225..7320fc3a95c4 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForQuestionAnswering.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForQuestionAnswering.scala @@ -49,7 +49,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"xlm_roberta_base_qa_squad2"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Question+Answering Models Hub]]. + * [[https://sparknlp.org/models?task=Question+Answering Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -91,7 +91,7 @@ import org.apache.spark.sql.SparkSession * @see * [[XlmRoBertaForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForSequenceClassification.scala index 06c889b9d7f0..3d7c11b8b0e7 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForSequenceClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"xlm_roberta_base_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -99,7 +99,7 @@ import org.apache.spark.sql.SparkSession * @see * [[XlmRoBertaForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForTokenClassification.scala index 8f843dac3031..61f3d5ede997 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForTokenClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"xlm_roberta_base_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * and the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlmRoBertaForTokenClassificationTestSpec.scala XlmRoBertaForTokenClassificationTestSpec]]. @@ -98,7 +98,7 @@ import org.apache.spark.sql.SparkSession * @see * [[XlmRoBertaForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForSequenceClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForSequenceClassification.scala index c90e85cc9b17..f0409c90d5bb 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForSequenceClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForSequenceClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"xlnet_base_sequence_classifier_imdb"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Text+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Text+Classification Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -99,7 +99,7 @@ import org.apache.spark.sql.SparkSession * @see * [[XlnetForSequenceClassification]] for sequence-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForTokenClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForTokenClassification.scala index e0eaa762fbde..0de6ced512c4 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForTokenClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForTokenClassification.scala @@ -51,7 +51,7 @@ import org.apache.spark.sql.SparkSession * The default model is `"xlnet_base_token_classifier_conll03"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * and the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/XlnetForTokenClassificationTestSpec.scala XlnetForTokenClassificationTestSpec]]. @@ -98,7 +98,7 @@ import org.apache.spark.sql.SparkSession * @see * [[XlnetForTokenClassification]] for token-level classification * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based classifiers * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/coref/SpanBertCorefModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/coref/SpanBertCorefModel.scala index af3789b38f85..92767d359dc5 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/coref/SpanBertCorefModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/coref/SpanBertCorefModel.scala @@ -54,7 +54,7 @@ import org.slf4j.{Logger, LoggerFactory} * .setOutputCol("corefs") * }}} * The default model is `"spanbert_base_coref"`, if no name is provided. For available pretrained - * models please see the [[https://nlp.johnsnowlabs.com/models Models Hub]]. + * models please see the [[https://sparknlp.org/models Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/coreference-resolution/Coreference_Resolution_SpanBertCorefModel.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala index bfb5128ad002..b87de63bae8e 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassification.scala @@ -49,7 +49,7 @@ import org.json4s.jackson.JsonMethods._ * The default model is `"image_classifier_convnext_tiny_224_local"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Image+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Image+Classification Models Hub]]. * * Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To * see which models are compatible and how to import them see diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala index 21ce7a0d175f..4341fa23cd4b 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassification.scala @@ -51,7 +51,7 @@ import org.json4s.jackson.JsonMethods._ * provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Image+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Image+Classification Models Hub]]. * * Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To * see which models are compatible and how to import them see diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala index 768cda90ae54..07d979ed0a74 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/ViTForImageClassification.scala @@ -55,7 +55,7 @@ import java.io.File * The default model is `"image_classifier_vit_base_patch16_224"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Image+Classification Models Hub]]. + * [[https://sparknlp.org/models?task=Image+Classification Models Hub]]. * * Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To * see which models are compatible and how to import them see diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ld/dl/LanguageDetectorDL.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ld/dl/LanguageDetectorDL.scala index ba305077b0c5..444dad7b4fec 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ld/dl/LanguageDetectorDL.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ld/dl/LanguageDetectorDL.scala @@ -49,7 +49,7 @@ import scala.collection.immutable.ListMap * }}} * The default model is `"ld_wiki_tatoeba_cnn_21"`, default language is `"xx"` (meaning * multi-lingual), if no values are provided. For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Language+Detection Models Hub]]. + * [[https://sparknlp.org/models?task=Language+Detection Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/language-detection/Language_Detection_and_Indentification.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/crf/NerCrfModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/crf/NerCrfModel.scala index ab76616300fb..bb6ab18b7066 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/crf/NerCrfModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/crf/NerCrfModel.scala @@ -49,7 +49,7 @@ import scala.collection.Map * }}} * The default model is `"ner_crf"`, if no name is provided. For available pretrained models * please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/crf-ner/ner_dl_crf.ipynb Examples]]. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLApproach.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLApproach.scala index f73ce01a5d7e..93b2ca2325a3 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLApproach.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLApproach.scala @@ -627,7 +627,7 @@ trait WithGraphResolver { require( embeddingsFiltered.exists(_.nonEmpty), s"Graph dimensions should be $embeddingsNDims: Could not find a suitable tensorflow graph for embeddings dim: $embeddingsNDims tags: $tags nChars: $nChars. " + - s"Check https://nlp.johnsnowlabs.com/docs/en/graph for instructions to generate the required graph.") + s"Check https://sparknlp.org/docs/en/graph for instructions to generate the required graph.") // 2. Filter by labels and nChars val tagsFiltered = embeddingsFiltered.map { @@ -642,7 +642,7 @@ trait WithGraphResolver { require( tagsFiltered.exists(_.nonEmpty), s"Graph tags size should be $tags: Could not find a suitable tensorflow graph for embeddings dim: $embeddingsNDims tags: $tags nChars: $nChars. " + - s"Check https://nlp.johnsnowlabs.com/docs/en/graph for instructions to generate the required graph.") + s"Check https://sparknlp.org/docs/en/graph for instructions to generate the required graph.") // 3. Filter by labels and nChars val charsFiltered = tagsFiltered.map { @@ -657,7 +657,7 @@ trait WithGraphResolver { require( charsFiltered.exists(_.nonEmpty), s"Graph chars size should be $nChars: Could not find a suitable tensorflow graph for embeddings dim: $embeddingsNDims tags: $tags nChars: $nChars. " + - s"Check https://nlp.johnsnowlabs.com/docs/en/graph for instructions to generate the required graph") + s"Check https://sparknlp.org/docs/en/graph for instructions to generate the required graph") for (i <- files.indices) { if (charsFiltered(i).nonEmpty) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLModel.scala index 68dcb0d6aafd..c9246d80c8b7 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/NerDLModel.scala @@ -47,9 +47,9 @@ import org.apache.spark.sql.{Dataset, SparkSession} * The default model is `"ner_dl"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition Models Hub]]. + * [[https://sparknlp.org/models?task=Named+Entity+Recognition Models Hub]]. * Additionally, pretrained pipelines are available for this module, see - * [[https://nlp.johnsnowlabs.com/docs/en/pipelines Pipelines]]. + * [[https://sparknlp.org/docs/en/pipelines Pipelines]]. * * Note that some pretrained models require specific types of embeddings, depending on which they * were trained on. For example, the default model `"ner_dl"` requires the diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/ZeroShotNerModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/ZeroShotNerModel.scala index 2dd777c72510..fa6de5fe5af9 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/ZeroShotNerModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ner/dl/ZeroShotNerModel.scala @@ -51,7 +51,7 @@ import scala.collection.JavaConverters._ * }}} * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Zero-Shot-NER Models Hub]]. + * [[https://sparknlp.org/models?task=Zero-Shot-NER Models Hub]]. * * ==Example== * {{{ diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/dep/DependencyParserModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/dep/DependencyParserModel.scala index 9abdb9af186b..e74c2c94f575 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/dep/DependencyParserModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/dep/DependencyParserModel.scala @@ -45,7 +45,7 @@ import org.apache.spark.ml.util.Identifiable * .setOutputCol("dependency") * }}} * The default model is `"dependency_conllu"`, if no name is provided. For available pretrained - * models please see the [[https://nlp.johnsnowlabs.com/models Models Hub]]. + * models please see the [[https://sparknlp.org/models Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/typdep/TypedDependencyParserModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/typdep/TypedDependencyParserModel.scala index 597edbb4ead1..1a3866113759 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/typdep/TypedDependencyParserModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/parser/typdep/TypedDependencyParserModel.scala @@ -46,7 +46,7 @@ import org.apache.spark.ml.util.Identifiable * .setOutputCol("dependency_type") * }}} * The default model is `"dependency_typed_conllu"`, if no name is provided. For available - * pretrained models please see the [[https://nlp.johnsnowlabs.com/models Models Hub]]. + * pretrained models please see the [[https://sparknlp.org/models Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/graph-extraction/graph_extraction_intro.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/pos/perceptron/PerceptronModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/pos/perceptron/PerceptronModel.scala index b4f3faf82843..2b5a2d6f60bc 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/pos/perceptron/PerceptronModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/pos/perceptron/PerceptronModel.scala @@ -37,9 +37,9 @@ import org.apache.spark.ml.util.Identifiable * The default model is `"pos_anc"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Part+of+Speech+Tagging Models Hub]]. Additionally, + * [[https://sparknlp.org/models?task=Part+of+Speech+Tagging Models Hub]]. Additionally, * pretrained pipelines are available for this module, see - * [[https://nlp.johnsnowlabs.com/docs/en/pipelines Pipelines]]. + * [[https://sparknlp.org/docs/en/pipelines Pipelines]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/french/Train-Perceptron-French.ipynb Examples]]. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala index 9f87fb730d12..9278a1dd65ad 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala @@ -50,7 +50,7 @@ case class Metrics(accuracy: Double, recall: Double, precision: Double, f1: Doub * }}} * The default model is `"sentence_detector_dl"`, if no name is provided. For available * pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Sentence+Detection Models Hub]]. + * [[https://sparknlp.org/models?task=Sentence+Detection Models Hub]]. * * Each extracted sentence can be returned in an Array or exploded to separate rows, if * `explodeSentences` is set to `true`. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala index b7f878f8e781..a539823efa9f 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala @@ -66,7 +66,7 @@ import org.apache.spark.sql.SparkSession * .setOutputCol("generation") * }}} * The default model is `"distilbart_xsum_12_6"`, if no name is provided. For available - * pretrained models please see the [[https://nlp.johnsnowlabs.com/models?q=bart Models Hub]]. + * pretrained models please see the [[https://sparknlp.org/models?q=bart Models Hub]]. * * For extended examples of usage, see * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala BartTestSpec]]. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/GPT2Transformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/GPT2Transformer.scala index 8dd03277400d..5cafbe4865d5 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/GPT2Transformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/GPT2Transformer.scala @@ -63,7 +63,7 @@ import org.apache.spark.sql.SparkSession * .setOutputCol("generation") * }}} * The default model is `"gpt2"`, if no name is provided. For available pretrained models please - * see the [[https://nlp.johnsnowlabs.com/models?q=gpt2 Models Hub]]. + * see the [[https://sparknlp.org/models?q=gpt2 Models Hub]]. * * For extended examples of usage, see * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/GPT2TestSpec.scala GPT2TestSpec]]. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/MarianTransformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/MarianTransformer.scala index 65de3dfe44b1..381648c91b1c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/MarianTransformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/MarianTransformer.scala @@ -56,7 +56,7 @@ import org.apache.spark.sql.SparkSession * }}} * The default model is `"opus_mt_en_fr"`, default language is `"xx"` (meaning multi-lingual), if * no values are provided. For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Translation Models Hub]]. + * [[https://sparknlp.org/models?task=Translation Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/multilingual/Translation_Marian.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/T5Transformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/T5Transformer.scala index ae9779d25161..2643730c5190 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/T5Transformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/T5Transformer.scala @@ -59,7 +59,7 @@ import org.apache.spark.sql.SparkSession * .setOutputCol("summaries") * }}} * The default model is `"t5_small"`, if no name is provided. For available pretrained models - * please see the [[https://nlp.johnsnowlabs.com/models?q=t5 Models Hub]]. + * please see the [[https://sparknlp.org/models?q=t5 Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/question-answering/Question_Answering_and_Summarization_with_T5.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/context/ContextSpellCheckerModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/context/ContextSpellCheckerModel.scala index ce7fa7c770d9..e4a5de7dee37 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/context/ContextSpellCheckerModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/context/ContextSpellCheckerModel.scala @@ -56,7 +56,7 @@ import scala.collection.mutable * .setOutputCol("checked") * }}} * The default model is `"spellcheck_dl"`, if no name is provided. For available pretrained - * models please see the [[https://nlp.johnsnowlabs.com/models?task=Spell+Check Models Hub]]. + * models please see the [[https://sparknlp.org/models?task=Spell+Check Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/norvig/NorvigSweetingModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/norvig/NorvigSweetingModel.scala index b7a410e0a7b8..37fe61f7acda 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/norvig/NorvigSweetingModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/norvig/NorvigSweetingModel.scala @@ -43,7 +43,7 @@ import scala.collection.immutable.HashSet * .setDoubleVariants(true) * }}} * The default model is `"spellcheck_norvig"`, if no name is provided. For available pretrained - * models please see the [[https://nlp.johnsnowlabs.com/models?task=Spell+Check Models Hub]]. + * models please see the [[https://sparknlp.org/models?task=Spell+Check Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/norvig/NorvigSweetingTestSpec.scala NorvigSweetingTestSpec]]. diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModel.scala index fc32e0608d96..cc6ce666fa8a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModel.scala @@ -42,7 +42,7 @@ import scala.util.control.Breaks._ * .setOutputCol("spell") * }}} * The default model is `"spellcheck_sd"`, if no name is provided. For available pretrained - * models please see the [[https://nlp.johnsnowlabs.com/models?task=Spell+Check Models Hub]]. + * models please see the [[https://sparknlp.org/models?task=Spell+Check Models Hub]]. * * See * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModelTestSpec.scala SymmetricDeleteModelTestSpec]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/ws/WordSegmenterModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/ws/WordSegmenterModel.scala index 550b2c5854a5..67473a7006d4 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/ws/WordSegmenterModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/ws/WordSegmenterModel.scala @@ -63,7 +63,7 @@ import org.apache.spark.ml.util.Identifiable * }}} * The default model is `"wordseg_pku"`, default language is `"zh"`, if no values are provided. * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Word+Segmentation Models Hub]]. + * [[https://sparknlp.org/models?task=Word+Segmentation Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/jupyter/annotation/chinese/word_segmentation/words_segmenter_demo.ipynb Examples]] diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/AlbertEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/AlbertEmbeddings.scala index 88611c91a10b..673dcf3fd53a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/AlbertEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/AlbertEmbeddings.scala @@ -154,7 +154,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.AlbertForTokenClassification AlbertForTokenClassification]] * for AlbertEmbeddings with a token classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddings.scala index c09656d33a90..e48f4739f39b 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddings.scala @@ -48,7 +48,7 @@ import org.slf4j.{Logger, LoggerFactory} * The default model is `"small_bert_L2_768"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_bert.ipynb Examples]] @@ -132,7 +132,7 @@ import org.slf4j.{Logger, LoggerFactory} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.BertForTokenClassification BertForTokenClassification]] * For BertEmbeddings with a token classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertSentenceEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertSentenceEmbeddings.scala index e9eeb993df44..c881719e8e78 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertSentenceEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/BertSentenceEmbeddings.scala @@ -47,7 +47,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * The default model is `"sent_small_bert_L2_768"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb Examples]] @@ -127,7 +127,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.BertForSequenceClassification BertForSequenceClassification]] * for embeddings with a sequence classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/CamemBertEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/CamemBertEmbeddings.scala index bc6251c50d9d..bc81d53ed6c3 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/CamemBertEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/CamemBertEmbeddings.scala @@ -36,7 +36,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * The default model is `"camembert_base"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_bert.ipynb Examples]] @@ -113,7 +113,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/DeBertaEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/DeBertaEmbeddings.scala index bcfe2ae33d42..993e5aae018b 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/DeBertaEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/DeBertaEmbeddings.scala @@ -135,7 +135,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/DistilBertEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/DistilBertEmbeddings.scala index afa7b2f3ff68..ac4113265e41 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/DistilBertEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/DistilBertEmbeddings.scala @@ -46,7 +46,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * The default model is `"distilbert_base_cased"`, if no name is provided. For available * pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DistilBERT.ipynb Examples]] @@ -139,7 +139,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.DistilBertForSequenceClassification DistilBertForSequenceClassification]] * for DistilBertEmbeddings with a sequence classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/Doc2VecModel.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/Doc2VecModel.scala index 88d5aabee448..f1ed150232db 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/Doc2VecModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/Doc2VecModel.scala @@ -47,7 +47,7 @@ import org.apache.spark.sql.DataFrame * The default model is `"doc2vec_gigaword_300"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models Models Hub]]. + * [[https://sparknlp.org/models Models Hub]]. * * '''Sources''' : * diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/ElmoEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/ElmoEmbeddings.scala index 8ced250f5dcf..5e1d2a1a323d 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/ElmoEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/ElmoEmbeddings.scala @@ -43,7 +43,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * The default model is `"elmo"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * The pooling layer can be set with `setPoolingLayer` to the following values: * - `"word_emb"`: the character-based word representations with shape `[batch_size, @@ -129,7 +129,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of other + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of other * transformer based embeddings * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/LongformerEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/LongformerEmbeddings.scala index 3ea3c2a6f336..f34cda45c5f0 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/LongformerEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/LongformerEmbeddings.scala @@ -48,7 +48,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * The default model is `"longformer_base_4096"`, if no name is provided. For available * pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For some examples of usage, see * [[https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/LongformerEmbeddingsTestSpec.scala LongformerEmbeddingsTestSpec]]. @@ -127,7 +127,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.LongformerForTokenClassification LongformerForTokenClassification]] * for Longformer embeddings with a token classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaEmbeddings.scala index 27eeda15c8cd..d8e21c289fa6 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaEmbeddings.scala @@ -50,7 +50,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * .setOutputCol("embeddings") * }}} * The default model is `"roberta_base"`, if no name is provided. For available pretrained models - * please see the [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * please see the [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20RoBERTa.ipynb Examples]] @@ -139,7 +139,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.RoBertaForTokenClassification RoBertaForTokenClassification]] * For RoBerta embeddings with a token classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaSentenceEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaSentenceEmbeddings.scala index e71a26005c90..4bf688b02932 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaSentenceEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/RoBertaSentenceEmbeddings.scala @@ -50,7 +50,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * .setOutputCol("sentence_embeddings") * }}} * The default model is `"sent_roberta_base"`, if no name is provided. For available pretrained - * models please see the [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * models please see the [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -136,7 +136,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * @see * [[RoBertaEmbeddings]] for token-level embeddings * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/UniversalSentenceEncoder.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/UniversalSentenceEncoder.scala index 457dba10a22b..2fb571c9f1cf 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/UniversalSentenceEncoder.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/UniversalSentenceEncoder.scala @@ -43,7 +43,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * .setOutputCol("sentence_embeddings") * }}} * The default model is `"tfhub_use"`, if no name is provided. For available pretrained models - * please see the [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * please see the [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/classification/ClassifierDL_Train_multi_class_news_category_classifier.ipynb Examples]] @@ -119,7 +119,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/Word2VecModel.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/Word2VecModel.scala index 751a8c44da89..bc7692c7a26a 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/Word2VecModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/Word2VecModel.scala @@ -48,7 +48,7 @@ import org.apache.spark.sql.DataFrame * The default model is `"word2vec_gigaword_300"`, if no name is provided. * * For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models Models Hub]]. + * [[https://sparknlp.org/models Models Hub]]. * * '''Sources''' : * diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddings.scala index debe836455ed..6f30aaa59eaf 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddings.scala @@ -111,7 +111,7 @@ import org.apache.spark.sql.Dataset * @see * [[SentenceEmbeddings]] to combine embeddings into a sentence-level representation * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @param uid * required uid for storing annotator to disk diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala index f32c7dace570..fbd5e813f179 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsModel.scala @@ -43,7 +43,7 @@ import org.apache.spark.sql.{DataFrame, Dataset, Row} * .setOutputCol("embeddings") * }}} * The default model is `"glove_100d"`, if no name is provided. For available pretrained models - * please see the [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * please see the [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * There are also two convenient functions to retrieve the embeddings coverage with respect to * the transformed dataset: @@ -125,7 +125,7 @@ import org.apache.spark.sql.{DataFrame, Dataset, Row} * @see * [[SentenceEmbeddings]] to combine embeddings into a sentence-level representation * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaEmbeddings.scala index 75ba2f2f3391..386a21f5069b 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaEmbeddings.scala @@ -53,7 +53,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * The default model is `"xlm_roberta_base"`, default language is `"xx"` (meaning multi-lingual), * if no values are provided. For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * For extended examples of usage, see the * [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XLM-RoBERTa.ipynb Examples]] @@ -143,7 +143,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.XlmRoBertaForTokenClassification XlmRoBertaForTokenClassification]] * For XlmRoBerta embeddings with a token classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaSentenceEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaSentenceEmbeddings.scala index a4172ce5843d..9f716778c57f 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaSentenceEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlmRoBertaSentenceEmbeddings.scala @@ -53,7 +53,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * }}} * The default model is `"sent_xlm_roberta_base"`, default language is `"xx"` (meaning * multi-lingual), if no values are provided. For available pretrained models please see the - * [[https://nlp.johnsnowlabs.com/models?task=Embeddings Models Hub]]. + * [[https://sparknlp.org/models?task=Embeddings Models Hub]]. * * To see which models are compatible and how to import them see * [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]] and to see more extended @@ -140,7 +140,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * @see * [[XlmRoBertaEmbeddings]] for token-level embeddings * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @groupname anno Annotator types * @groupdesc anno diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlnetEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlnetEmbeddings.scala index 9f4ffecdafdf..f85f4a5854b0 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlnetEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/XlnetEmbeddings.scala @@ -150,7 +150,7 @@ import org.apache.spark.sql.{DataFrame, SparkSession} * [[com.johnsnowlabs.nlp.annotators.classifier.dl.XlnetForTokenClassification XlnetForTokenClassification]] * For Xlnet embeddings with a token classification layer on top * @see - * [[https://nlp.johnsnowlabs.com/docs/en/annotators Annotators Main Page]] for a list of + * [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of * transformer based embeddings * @param uid * required internal uid for saving annotator diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/PretrainedPipeline.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/PretrainedPipeline.scala index 664508a72aa6..f4cbd25c52b0 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/PretrainedPipeline.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/PretrainedPipeline.scala @@ -25,7 +25,7 @@ import org.apache.spark.sql.DataFrame * model can be retrieved with member `lightModel`. * * For more extended examples see the - * [[https://nlp.johnsnowlabs.com/docs/en/pipelines Pipelines page]] and our + * [[https://sparknlp.org/docs/en/pipelines Pipelines page]] and our * [[https://github.com/JohnSnowLabs/spark-nlp-models Github Model Repository]] for available * pipeline models. * From 2e78960986723f1b867705fd17f1810e4f874ce4 Mon Sep 17 00:00:00 2001 From: Prabod Rathnayaka Date: Mon, 10 Apr 2023 00:12:44 +1000 Subject: [PATCH 17/26] Update INT64 to INT32 (#13748) --- .../scala/com/johnsnowlabs/ml/ai/Bart.scala | 106 +++++++++--------- .../ml/ai/util/Generation/Generate.scala | 36 +++--- .../ml/ai/util/Generation/Logit/Logit.scala | 2 +- .../MinLengthLogitProcessor.scala | 4 +- .../NoRepeatNgramsLogitProcessor.scala | 20 ++-- .../RepetitionPenaltyLogitProcessor.scala | 2 +- .../Generation/Logit/LogitProcessorList.scala | 4 +- .../LogitWarper/TemperatureLogitWarper.scala | 2 +- .../Logit/LogitWarper/TopKLogitWarper.scala | 2 +- .../Logit/LogitWarper/TopPLogitWarper.scala | 2 +- .../Generation/Search/BeamHypotheses.scala | 8 +- .../util/Generation/Search/BeamScorer.scala | 28 ++--- .../Generation/Search/BeamSearchScorer.scala | 54 ++++----- 13 files changed, 135 insertions(+), 135 deletions(-) diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala b/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala index 9ba7c6b8ffe5..57f51fd68abc 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala @@ -66,12 +66,12 @@ private[johnsnowlabs] class Bart( .forModel("bart", merges = merges, vocab = vocabulary, padWithSentenceTokens = false) .asInstanceOf[BartTokenizer] - private val paddingTokenId = 1L - private val eosTokenId = 2L + private val paddingTokenId = 1 + private val eosTokenId = 2 private val vocab_size = 50264 private def sessionWarmup(): Unit = { - val dummyInput = Array.fill(1)(0L) ++ Array(eosTokenId) + val dummyInput = Array.fill(1)(0) ++ Array(eosTokenId) tag( Seq(dummyInput), minOutputLength = 0, @@ -82,15 +82,15 @@ private[johnsnowlabs] class Bart( topP = 0f, repetitionPenalty = 0f, noRepeatNgramSize = 0, - randomSeed = Option(0L), + randomSeed = Option(0), ignoreTokenIds = Array(0), beamSize = 1) } - sessionWarmup() +// sessionWarmup() def tag( - batch: Seq[Array[Long]], + batch: Seq[Array[Int]], minOutputLength: Int, maxOutputLength: Int, doSample: Boolean, @@ -101,8 +101,8 @@ private[johnsnowlabs] class Bart( noRepeatNgramSize: Int, randomSeed: Option[Long], ignoreTokenIds: Array[Int] = Array(), - beamSize: Int): Array[Array[Long]] = { - val ignoreTokenIdsInt = ignoreTokenIds.map(_.toLong) + beamSize: Int): Array[Array[Int]] = { + val ignoreTokenIdsInt = ignoreTokenIds val expandedEncoderInputIdsVals = batch.flatMap(x => List.fill(beamSize)(x)) // val expandedEncoderInputIdsVals = batch val sequencesLength = expandedEncoderInputIdsVals.map(x => x.length).toArray @@ -127,8 +127,8 @@ private[johnsnowlabs] class Bart( val tensorEncoder = new TensorResources() val inputDim = expandedEncoderInputIdsVals.length * maxSentenceLength - val encoderInputBuffers = tensorEncoder.createLongBuffer(inputDim) - val encoderAttentionMaskBuffers = tensorEncoder.createLongBuffer(inputDim) + val encoderInputBuffers = tensorEncoder.createIntBuffer(inputDim) + val encoderAttentionMaskBuffers = tensorEncoder.createIntBuffer(inputDim) val shape = Array(expandedEncoderInputIdsVals.length.toLong, maxSentenceLength) @@ -136,9 +136,9 @@ private[johnsnowlabs] class Bart( val offset = idx * maxSentenceLength val diff = maxSentenceLength - tokenIds.length - val s = tokenIds.take(maxSentenceLength) ++ Array.fill[Long](diff)(this.paddingTokenId) + val s = tokenIds.take(maxSentenceLength) ++ Array.fill[Int](diff)(this.paddingTokenId) encoderInputBuffers.offset(offset).write(s) - val mask = s.map(x => if (x != this.paddingTokenId) 1L else 0L) + val mask = s.map(x => if (x != this.paddingTokenId) 1 else 0) encoderAttentionMaskBuffers.offset(offset).write(mask) } @@ -147,9 +147,9 @@ private[johnsnowlabs] class Bart( initAllTables = false, savedSignatures = signatures) - val encoderInputTensors = tensorEncoder.createLongBufferTensor(shape, encoderInputBuffers) + val encoderInputTensors = tensorEncoder.createIntBufferTensor(shape, encoderInputBuffers) val encoderAttentionMaskTensors = - tensorEncoder.createLongBufferTensor(shape, encoderAttentionMaskBuffers) + tensorEncoder.createIntBufferTensor(shape, encoderAttentionMaskBuffers) val runner = session.runner runner @@ -188,7 +188,7 @@ private[johnsnowlabs] class Bart( } val decoderEncoderStateTensors = tensorEncoder.createFloatBufferTensor( - Array(expandedEncoderInputIdsVals.length.toLong, maxSentenceLength, dim), + Array(expandedEncoderInputIdsVals.length, maxSentenceLength, dim), decoderEncoderStateBuffers) // val modelOutputs = generateNoBeamSearch( @@ -232,7 +232,7 @@ private[johnsnowlabs] class Bart( } def generateBeamSearch( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], decoderEncoderStateTensors: Tensor, encoderAttentionMaskTensors: Tensor, maxOutputLength: Int, @@ -246,8 +246,8 @@ private[johnsnowlabs] class Bart( repetitionPenalty: Double, noRepeatNgramSize: Int, randomSeed: Option[Long], - ignoreTokenIds: Array[Long] = Array(), - session: Session): Array[Array[Long]] = { + ignoreTokenIds: Array[Int] = Array(), + session: Session): Array[Array[Int]] = { var decoderInputs = inputIds.map(_ => Array(this.eosTokenId)).toArray @@ -293,7 +293,7 @@ private[johnsnowlabs] class Bart( } def generateNoBeamSearch( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], decoderEncoderStateTensors: Tensor, encoderAttentionMaskTensors: Tensor, maxOutputLength: Int, @@ -306,7 +306,7 @@ private[johnsnowlabs] class Bart( noRepeatNgramSize: Int, randomSeed: Option[Long], ignoreTokenIds: Array[Int] = Array(), - session: Session): Array[Array[Long]] = { + session: Session): Array[Array[Int]] = { /** Generate sequences for each example without beam search (numBeams == 1). All returned * sequence are generated independently. @@ -346,22 +346,22 @@ private[johnsnowlabs] class Bart( val tensorDecoder = new TensorResources() val decoderInputBuffers = - tensorDecoder.createLongBuffer(decoderInputs.length * decoderInputLength) + tensorDecoder.createIntBuffer(decoderInputs.length * decoderInputLength) val decoderAttentionBuffers = - tensorDecoder.createLongBuffer(decoderInputs.length * decoderInputLength) + tensorDecoder.createIntBuffer(decoderInputs.length * decoderInputLength) decoderInputs.zipWithIndex.foreach { case (pieceIds, idx) => val offset = idx * decoderInputLength decoderInputBuffers.offset(offset).write(pieceIds) - val paddingMasks = pieceIds.map(_ => 1L) + val paddingMasks = pieceIds.map(_ => 1) decoderAttentionBuffers.offset(offset).write(paddingMasks) } - val decoderInputTensors = tensorDecoder.createLongBufferTensor( - Array(decoderInputs.length.toLong, decoderInputLength), + val decoderInputTensors = tensorDecoder.createIntBufferTensor( + Array(decoderInputs.length, decoderInputLength), decoderInputBuffers) - val decoderAttentionMaskTensors = tensorDecoder.createLongBufferTensor( - Array(decoderInputs.length.toLong, decoderInputLength), + val decoderAttentionMaskTensors = tensorDecoder.createIntBufferTensor( + Array(decoderInputs.length, decoderInputLength), decoderAttentionBuffers) val runner = session.runner @@ -454,7 +454,7 @@ private[johnsnowlabs] class Bart( Float.NegativeInfinity) } - var nextToken = Array.ofDim[Long](decoderInputs.length) + var nextToken = Array.ofDim[Int](decoderInputs.length) if (doSample) { // Temperature (higher temperature => more likely to sample low probability tokens) @@ -468,9 +468,9 @@ private[johnsnowlabs] class Bart( nextToken = nextTokenLogits.map(input => categoricalSample(input, randomSeed)) } else { // Greedy decoding - nextToken = nextTokenLogits.map(input => input.indexOf(input.max).toLong) + nextToken = nextTokenLogits.map(input => input.indexOf(input.max)) } - var tokensToAdd = Array.ofDim[Long](decoderInputs.length) + var tokensToAdd = Array.ofDim[Int](decoderInputs.length) // update generations and finished sentences if (!eosTokenId.isNaN) @@ -521,7 +521,7 @@ private[johnsnowlabs] class Bart( } def createNextTokenLogitsPenalties( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], logits: Array[Array[Float]], repetitionPenalty: Double): Array[Array[Float]] = { // create logit penalties for already seen inputIds @@ -547,16 +547,16 @@ private[johnsnowlabs] class Bart( } private def calcBannedNgramTokens( - prevInputIds: Seq[Array[Long]], + prevInputIds: Seq[Array[Int]], numHypos: Int, noRepeatNgramSize: Int, - curLen: Int): Array[Array[Long]] = { + curLen: Int): Array[Array[Int]] = { // based on fairseq for noRepeatNgram in beam_search if (curLen + 1 < noRepeatNgramSize) // return no banned tokens if we haven't generated noRepeatNgram_size tokens yet - return Array.ofDim[Long](numHypos, 0) + return Array.ofDim[Int](numHypos, 0) val generatedNgrams = - Array.tabulate(numHypos)(_ => mutable.Map.empty[IndexedSeq[Long], List[Long]]) + Array.tabulate(numHypos)(_ => mutable.Map.empty[IndexedSeq[Int], List[Int]]) for (idx <- 0 until numHypos) { val genTokens = prevInputIds(idx) val generatedNgram = generatedNgrams(idx) @@ -565,7 +565,7 @@ private[johnsnowlabs] class Bart( val ngram = for (e <- ngramArrays) yield e(ngramInd) val prevNgramTuple = ngram.dropRight(1) generatedNgram(prevNgramTuple) = - generatedNgram.getOrElse(prevNgramTuple, List.empty[Long]) :+ ngram.last + generatedNgram.getOrElse(prevNgramTuple, List.empty[Int]) :+ ngram.last } } (for (hypoIdx <- 0 until numHypos) @@ -578,15 +578,15 @@ private[johnsnowlabs] class Bart( } def getGeneratedNgrams( - prevInputIds: Seq[Array[Long]], - generatedNgrams: Array[mutable.Map[IndexedSeq[Long], List[Long]]], + prevInputIds: Seq[Array[Int]], + generatedNgrams: Array[mutable.Map[IndexedSeq[Int], List[Int]]], hypoIdx: Int, curLen: Int, - noRepeatNgramSize: Int): Array[Long] = { + noRepeatNgramSize: Int): Array[Int] = { // Before decoding the next token, prevent decoding of ngrams that have already appeared val startIdx = curLen + 1 - noRepeatNgramSize val ngramIdx = prevInputIds(hypoIdx).slice(startIdx, curLen) - generatedNgrams(hypoIdx).getOrElse(ngramIdx, List.empty[Long]).toArray + generatedNgrams(hypoIdx).getOrElse(ngramIdx, List.empty[Int]).toArray } private def topKTopPFiltering( @@ -677,7 +677,7 @@ private[johnsnowlabs] class Bart( for ((inputId, index) <- prevInputIds.zip(indices)) yield if (index) value else inputId } - private def categoricalSample(dist: Array[Float], randomSeed: Option[Long]): Long = { + private def categoricalSample(dist: Array[Float], randomSeed: Option[Long]): Int = { val (distFiltered, indices) = dist.zipWithIndex.filter { case (elem, index) => !elem.isInfinite }.sorted.unzip @@ -702,11 +702,11 @@ private[johnsnowlabs] class Bart( indices(0) } - def decode(sentences: Array[Array[Long]]): Seq[String] = { + def decode(sentences: Array[Array[Int]]): Seq[String] = { sentences.map(s => bpeTokenizer.decodeTokens(s.map(_.toInt))) } - def encode(sentences: Seq[Annotation], task: String): Seq[Array[Long]] = { + def encode(sentences: Seq[Annotation], task: String): Seq[Array[Int]] = { SentenceSplit .unpack(sentences) .map(s => { @@ -722,7 +722,7 @@ private[johnsnowlabs] class Bart( bpeTokenizer .tokenize(sentWithTask) .map(bpeTokenizer.encode) - .flatMap(_.map(_.pieceId.toLong)) + .flatMap(_.map(_.pieceId)) }) } @@ -777,8 +777,8 @@ private[johnsnowlabs] class Bart( } override def getModelOutput( - encoderInputIds: Seq[Array[Long]], - decoderInputIds: Seq[Array[Long]], + encoderInputIds: Seq[Array[Int]], + decoderInputIds: Seq[Array[Int]], decoderEncoderStateTensors: Tensor, encoderAttentionMaskTensors: Tensor, maxLength: Int, @@ -792,22 +792,22 @@ private[johnsnowlabs] class Bart( val tensorDecoder = new TensorResources() val decoderInputBuffers = - tensorDecoder.createLongBuffer(decoderInputIds.length * decoderInputLength) + tensorDecoder.createIntBuffer(decoderInputIds.length * decoderInputLength) val decoderAttentionBuffers = - tensorDecoder.createLongBuffer(decoderInputIds.length * decoderInputLength) + tensorDecoder.createIntBuffer(decoderInputIds.length * decoderInputLength) decoderInputIds.zipWithIndex.foreach { case (pieceIds, idx) => val offset = idx * decoderInputLength decoderInputBuffers.offset(offset).write(pieceIds) - val paddingMasks = pieceIds.map(_ => 1L) + val paddingMasks = pieceIds.map(_ => 1) decoderAttentionBuffers.offset(offset).write(paddingMasks) } - val decoderInputTensors = tensorDecoder.createLongBufferTensor( - Array(decoderInputIds.length.toLong, decoderInputLength), + val decoderInputTensors = tensorDecoder.createIntBufferTensor( + Array(decoderInputIds.length, decoderInputLength), decoderInputBuffers) - val decoderAttentionMaskTensors = tensorDecoder.createLongBufferTensor( - Array(decoderInputIds.length.toLong, decoderInputLength), + val decoderAttentionMaskTensors = tensorDecoder.createIntBufferTensor( + Array(decoderInputIds.length, decoderInputLength), decoderAttentionBuffers) val runner = session.runner diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala index 6432043b0231..892cbe708c1e 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Generate.scala @@ -24,18 +24,18 @@ import org.tensorflow.{Session, Tensor} trait Generate { def beamSearch( - encoderInputIdsVals: Seq[Array[Long]], - inputIdsVal: Seq[Array[Long]], + encoderInputIdsVals: Seq[Array[Int]], + inputIdsVal: Seq[Array[Int]], decoderEncoderStateTensors: Tensor, encoderAttentionMaskTensors: Tensor, beamScorer: BeamScorer, logitProcessor: LogitProcessorList, maxLength: Int, - padTokenId: Long, - eosTokenId: Long, + padTokenId: Int, + eosTokenId: Int, doSample: Boolean, randomSeed: Option[Long], - session: Session): Array[Array[Long]] = { + session: Session): Array[Array[Int]] = { var inputIds = inputIdsVal val batchSize = beamScorer.getBeamHypothesesSeq.length val numBeams = beamScorer.getNumBeams @@ -46,9 +46,9 @@ trait Generate { beamScores = beamScores.zipWithIndex.map { case (_, ind) => if (ind % numBeams == 0) 0 else (-1e-9).toFloat } - var beamIndices = Seq.fill(batchBeamSize)(Array[Long]()) - var nextIndices = Array[Array[Long]]() - var nextTokens = Array[Array[Long]]() + var beamIndices = Seq.fill(batchBeamSize)(Array[Int]()) + var nextIndices = Array[Array[Int]]() + var nextTokens = Array[Array[Int]]() var expandedInputs = inputIds.flatMap(x => List.fill(numBeams)(x)) val expandedEncoderInputIdsVals = encoderInputIdsVals.flatMap(x => List.fill(numBeams)(x)) breakable { @@ -90,7 +90,7 @@ trait Generate { nextTokenScores = reshapedNextTokenScores var nextKTopTokenScores: Array[Array[Float]] = Array[Array[Float]]() - var nextKTopTokens: Array[Array[Long]] = Array[Array[Long]]() + var nextKTopTokens: Array[Array[Int]] = Array[Array[Int]]() if (doSample) { val nextKIndices = nextTokenScores.map(x => { @@ -106,7 +106,7 @@ trait Generate { nextKTopTokenScores.map(x => x.zipWithIndex.sortWith(_._1 > _._1).map(_._1)) val tempNextKInd = nextKTopTokenScores.map(x => x.zipWithIndex.sortWith(_._1 > _._1).map(_._2)) - nextKTopTokens = Array.ofDim[Long](nextKIndices.length, nextKIndices.head.length) + nextKTopTokens = Array.ofDim[Int](nextKIndices.length, nextKIndices.head.length) for (i <- tempNextKInd.indices) { for (j <- tempNextKInd(i).indices) { @@ -117,7 +117,7 @@ trait Generate { nextKTopTokenScores = nextTokenScores.map(x => x.zipWithIndex.sortWith(_._1 > _._1).take(2 * numBeams).map(_._1)) nextKTopTokens = nextTokenScores.map(x => - x.zipWithIndex.sortWith(_._1 > _._1).take(2 * numBeams).map(_._2.toLong)) + x.zipWithIndex.sortWith(_._1 > _._1).take(2 * numBeams).map(_._2)) } nextIndices = nextKTopTokens.map(y => y.map(x => x / vocabSize)) nextTokens = nextKTopTokens.map(y => y.map(x => x % vocabSize)) @@ -134,7 +134,7 @@ trait Generate { val newBeamScores = beamOutputs._1.flatMap(_.toList) val beamNextTokens = beamOutputs._2.flatMap(_.toList) val beamIdx = beamOutputs._3.flatMap(_.toList) - var newInputIds = Seq[Array[Long]]() + var newInputIds = Seq[Array[Int]]() for ((i, ind) <- beamIdx.zipWithIndex) { val tempInput = expandedInputs(i.toInt) :+ beamNextTokens(ind) @@ -166,8 +166,8 @@ trait Generate { } def getModelOutput( - encoderInputIds: Seq[Array[Long]], - decoderInputIds: Seq[Array[Long]], + encoderInputIds: Seq[Array[Int]], + decoderInputIds: Seq[Array[Int]], decoderEncoderStateTensors: Tensor, encoderAttentionMaskTensors: Tensor, maxLength: Int, @@ -202,7 +202,7 @@ trait Generate { outputArray // Return the reshaped array } - def sample(logits: Seq[Float], k: Int, seed: Long = 42): Array[Long] = { + def sample(logits: Seq[Float], k: Int, seed: Long = 42): Array[Int] = { val maxLogit = logits.max val logitsExp = logits.map(logit => math.exp(logit - maxLogit)) val sumExp = logitsExp.sum @@ -219,10 +219,10 @@ trait Generate { } results :+= index } - results.map(_.toLong).toArray + results.toArray } - def multinomialSampling(logitValues: Array[Float], k: Int, seed: Option[Long]): Array[Long] = { + def multinomialSampling(logitValues: Array[Float], k: Int, seed: Option[Long]): Array[Int] = { val (distFiltered, indices) = logitValues.zipWithIndex.filter { case (elem, index) => !elem.isInfinite }.sorted.unzip @@ -232,7 +232,7 @@ trait Generate { val probabilities = expLogitValues.map(_ / sumExpLogitValues) // val indices = Array.range(0, logitValues.length) - val selectedIndices = new Array[Long](k) + val selectedIndices = new Array[Int](k) var seededRandom = new scala.util.Random() if (seed.isDefined) { seededRandom = new scala.util.Random(seed.get) diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala index 832ba60bbe4f..1ce50ea9577a 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.scala @@ -18,7 +18,7 @@ package com.johnsnowlabs.ml.ai.util.Generation.Logit abstract class Logit { def call( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala index 614ddc0f17ea..930ac5196936 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.scala @@ -16,10 +16,10 @@ package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess -class MinLengthLogitProcessor(val eosTokenId: Long, val minLength: Int, val vocabSize: Int) +class MinLengthLogitProcessor(val eosTokenId: Int, val minLength: Int, val vocabSize: Int) extends LogitProcessor { override def call( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { if (!eosTokenId.isNaN && currentLength < this.minLength) { diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala index e83c7107376f..3f022718da08 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/NoRepeatNgramsLogitProcessor.scala @@ -20,7 +20,7 @@ import scala.collection.mutable class NoRepeatNgramsLogitProcessor(val noRepeatNgramSize: Int, val vocabSize: Int) extends LogitProcessor { override def call( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { if (noRepeatNgramSize > 0) { @@ -51,16 +51,16 @@ class NoRepeatNgramsLogitProcessor(val noRepeatNgramSize: Int, val vocabSize: In } private def calcBannedNgramTokens( - prevInputIds: Seq[Array[Long]], + prevInputIds: Seq[Array[Int]], numHypos: Int, noRepeatNgramSize: Int, - curLen: Int): Array[Array[Long]] = { + curLen: Int): Array[Array[Int]] = { // based on fairseq for noRepeatNgram in beam_search if (curLen + 1 < noRepeatNgramSize) // return no banned tokens if we haven't generated noRepeatNgram_size tokens yet - return Array.ofDim[Long](numHypos, 0) + return Array.ofDim[Int](numHypos, 0) val generatedNgrams = - Array.tabulate(numHypos)(_ => mutable.Map.empty[IndexedSeq[Long], List[Long]]) + Array.tabulate(numHypos)(_ => mutable.Map.empty[IndexedSeq[Int], List[Int]]) for (idx <- 0 until numHypos) { val genTokens = prevInputIds(idx) val generatedNgram = generatedNgrams(idx) @@ -69,7 +69,7 @@ class NoRepeatNgramsLogitProcessor(val noRepeatNgramSize: Int, val vocabSize: In val ngram = for (e <- ngramArrays) yield e(ngramInd) val prevNgramTuple = ngram.dropRight(1) generatedNgram(prevNgramTuple) = - generatedNgram.getOrElse(prevNgramTuple, List.empty[Long]) :+ ngram.last + generatedNgram.getOrElse(prevNgramTuple, List.empty[Int]) :+ ngram.last } } (for (hypoIdx <- 0 until numHypos) @@ -82,14 +82,14 @@ class NoRepeatNgramsLogitProcessor(val noRepeatNgramSize: Int, val vocabSize: In } private def getGeneratedNgrams( - prevInputIds: Seq[Array[Long]], - generatedNgrams: Array[mutable.Map[IndexedSeq[Long], List[Long]]], + prevInputIds: Seq[Array[Int]], + generatedNgrams: Array[mutable.Map[IndexedSeq[Int], List[Int]]], hypoIdx: Int, curLen: Int, - noRepeatNgramSize: Int): Array[Long] = { + noRepeatNgramSize: Int): Array[Int] = { // Before decoding the next token, prevent decoding of ngrams that have already appeared val startIdx = curLen + 1 - noRepeatNgramSize val ngramIdx = prevInputIds(hypoIdx).slice(startIdx, curLen) - generatedNgrams(hypoIdx).getOrElse(ngramIdx, List.empty[Long]).toArray + generatedNgrams(hypoIdx).getOrElse(ngramIdx, List.empty[Int]).toArray } } diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala index 1d2222967b75..877e8fdef8c6 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/RepetitionPenaltyLogitProcessor.scala @@ -18,7 +18,7 @@ package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitProcess class RepetitionPenaltyLogitProcessor(val penalty: Double) extends LogitProcessor { override def call( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { if (penalty != 1.0) { diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala index fe8be1edfce8..dcb77f477b04 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcessorList.scala @@ -25,7 +25,7 @@ class LogitProcessorList { } def process( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { var tempScores = scores @@ -38,7 +38,7 @@ class LogitProcessorList { } def warp( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { var tempScores = scores diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala index 611ceb699aa6..20f17b081dc0 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TemperatureLogitWarper.scala @@ -18,7 +18,7 @@ package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper class TemperatureLogitWarper(val temperature: Double) extends LogitWarper { override def call( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { if (temperature > 0 && temperature <= 1) { diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala index 937033d368aa..6ec064de2b73 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopKLogitWarper.scala @@ -23,7 +23,7 @@ class TopKLogitWarper( val minTokensToKeep: Int = 1) extends LogitWarper { override def call( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { var logitsUpd = scores diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala index 2b74e3284b6d..f96c87c11eef 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitWarper/TopPLogitWarper.scala @@ -18,7 +18,7 @@ package com.johnsnowlabs.ml.ai.util.Generation.Logit.LogitWarper class TopPLogitWarper(val p: Double, val minTokensToKeep: Int = 1) extends LogitWarper { override def call( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], scores: Array[Array[Float]], currentLength: Int): Array[Array[Float]] = { var scoresUpd = scores diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala index 04c1b7c1c037..7ada974f852a 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamHypotheses.scala @@ -21,14 +21,14 @@ class BeamHypotheses( var numBeams: Int, var earlyStopping: Boolean = false, var maxLength: Int) { - private var beams: Seq[(Double, Array[Long], Array[Long])] = Seq() + private var beams: Seq[(Double, Array[Int], Array[Int])] = Seq() private var worstScore: Double = 1e9 def length(): Int = { beams.length } - def getBeams: Seq[(Double, Array[Long], Array[Long])] = { + def getBeams: Seq[(Double, Array[Int], Array[Int])] = { this.beams } @@ -40,7 +40,7 @@ class BeamHypotheses( * @param beamIndices * Beam Indices */ - def add(hypotheses: Array[Long], sumLogProbs: Double, beamIndices: Array[Long]): Unit = { + def add(hypotheses: Array[Int], sumLogProbs: Double, beamIndices: Array[Int]): Unit = { val score = sumLogProbs / Math.pow(hypotheses.length, this.lengthPenalty) if (this.beams.length < this.numBeams || score > this.worstScore) { this.beams = beams :+ (score, hypotheses, beamIndices) @@ -65,7 +65,7 @@ class BeamHypotheses( * @return * Status of the sentence */ - def isDone(bestSumLogProbs: Double, currentLength: Long): Boolean = { + def isDone(bestSumLogProbs: Double, currentLength: Int): Boolean = { if (this.beams.length < this.numBeams) { false } else if (this.earlyStopping) { diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala index 43471fd28e45..2fcbcada9533 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamScorer.scala @@ -19,24 +19,24 @@ package com.johnsnowlabs.ml.ai.util.Generation.Search abstract class BeamScorer() { def process( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], nextScores: Seq[Array[Float]], - nextTokens: Seq[Array[Long]], - nextIndices: Seq[Array[Long]], - padTokenId: Long, - eosTokenId: Long, - beamIndices: Seq[Array[Long]], - currentLength: Long): (Array[Array[Float]], Array[Array[Long]], Array[Array[Long]]) + nextTokens: Seq[Array[Int]], + nextIndices: Seq[Array[Int]], + padTokenId: Int, + eosTokenId: Int, + beamIndices: Seq[Array[Int]], + currentLength: Int): (Array[Array[Float]], Array[Array[Int]], Array[Array[Int]]) def finalize( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], finalBeamScores: Array[Float], - finalBeamTokens: Array[Long], - finalBeamIndices: Array[Long], - maxLength: Long, - padTokenId: Long, - eosTokenId: Long, - beamIndices: Seq[Array[Long]]): (Array[Array[Long]], Array[Float], Array[Array[Long]]) + finalBeamTokens: Array[Int], + finalBeamIndices: Array[Int], + maxLength: Int, + padTokenId: Int, + eosTokenId: Int, + beamIndices: Seq[Array[Int]]): (Array[Array[Int]], Array[Float], Array[Array[Int]]) def getBeamHypothesesSeq: Seq[BeamHypotheses] def getNumBeams: Int def isDone: Boolean diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala index afce9389f850..2bbe617140bf 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/util/Generation/Search/BeamSearchScorer.scala @@ -44,26 +44,26 @@ class BeamSearchScorer( private val done: Array[Boolean] = Array.fill(batchSize)(false) override def process( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], nextScores: Seq[Array[Float]], - nextTokens: Seq[Array[Long]], - nextIndices: Seq[Array[Long]], - padTokenId: Long, - eosTokenId: Long, - beamIndices: Seq[Array[Long]], - currentLength: Long): (Array[Array[Float]], Array[Array[Long]], Array[Array[Long]]) = { + nextTokens: Seq[Array[Int]], + nextIndices: Seq[Array[Int]], + padTokenId: Int, + eosTokenId: Int, + beamIndices: Seq[Array[Int]], + currentLength: Int): (Array[Array[Float]], Array[Array[Int]], Array[Array[Int]]) = { // val currentLength = inputIds.length val batchSize = this.beamHypothesesSeq.length val nextBeamScores = Array.ofDim[Float](batchSize, this.beamSize) - val nextBeamTokens = Array.ofDim[Long](batchSize, this.beamSize) - val nextBeamIndices = Array.ofDim[Long](batchSize, this.beamSize) + val nextBeamTokens = Array.ofDim[Int](batchSize, this.beamSize) + val nextBeamIndices = Array.ofDim[Int](batchSize, this.beamSize) this.beamHypothesesSeq.zipWithIndex.foreach { case (hypotheses, batchIdx) => breakable { if (this.done(batchIdx)) { nextBeamScores(batchIdx) = nextBeamScores(batchIdx).map(_ => 0.0f) nextBeamTokens(batchIdx) = nextBeamTokens(batchIdx).map(_ => padTokenId) - nextBeamIndices(batchIdx) = nextBeamIndices(batchIdx).map(_ => 0L) + nextBeamIndices(batchIdx) = nextBeamIndices(batchIdx).map(_ => 0) break } var beamIdx = 0 @@ -79,13 +79,13 @@ class BeamSearchScorer( if (beamTokenRank >= this.beamSize) { break } - var beamIndex = Array[Long]() + var beamIndex = Array[Int]() if (beamIndices.nonEmpty) { - beamIndex = beamIndices(batchBeamIdx.toInt) + beamIndex = beamIndices(batchBeamIdx) beamIndex = beamIndex.map(i => i + batchBeamIdx) } - hypotheses.add(inputIds(batchBeamIdx.toInt), nextScore, beamIndex) + hypotheses.add(inputIds(batchBeamIdx), nextScore, beamIndex) } else { nextBeamScores(batchIdx)(beamIdx) = nextScore nextBeamTokens(batchIdx)(beamIdx) = nextToken @@ -102,14 +102,14 @@ class BeamSearchScorer( } override def finalize( - inputIds: Seq[Array[Long]], + inputIds: Seq[Array[Int]], finalBeamScores: Array[Float], - finalBeamTokens: Array[Long], - finalBeamIndices: Array[Long], - maxLength: Long, - padTokenId: Long, - eosTokenId: Long, - beamIndices: Seq[Array[Long]]): (Array[Array[Long]], Array[Float], Array[Array[Long]]) = { + finalBeamTokens: Array[Int], + finalBeamIndices: Array[Int], + maxLength: Int, + padTokenId: Int, + eosTokenId: Int, + beamIndices: Seq[Array[Int]]): (Array[Array[Int]], Array[Float], Array[Array[Int]]) = { val batchSize = this.beamHypothesesSeq.length this.beamHypothesesSeq.zipWithIndex.foreach { case (hypotheses, batchIdx) => breakable { @@ -120,7 +120,7 @@ class BeamSearchScorer( var batchBeamIdx = batchIdx * this.beamSize + beamId var finalScore = finalBeamScores(batchBeamIdx) var finalTokens = inputIds(batchBeamIdx) - var beamIndex = Array[Long]() + var beamIndex = Array[Int]() if (beamIndices.nonEmpty) { beamIndex = beamIndices(batchBeamIdx) } @@ -128,9 +128,9 @@ class BeamSearchScorer( } } } - val sentLengths = Array.ofDim[Long](batchSize * this.numBeamHypothesisToKeep) - var best = Seq[Array[Long]]() - var bestIndices = Seq[Array[Long]]() + val sentLengths = Array.ofDim[Int](batchSize * this.numBeamHypothesisToKeep) + var best = Seq[Array[Int]]() + var bestIndices = Seq[Array[Int]]() val bestScores = Array.ofDim[Float](batchSize * this.numBeamHypothesisToKeep) this.beamHypothesesSeq.zipWithIndex.foreach { case (hypotheses, i) => breakable { @@ -150,13 +150,13 @@ class BeamSearchScorer( } val sentLengthMax = sentLengths.max + 1 val sentMaxLength = Math.min(sentLengthMax, maxLength) + 1 - var decoded = Array.ofDim[Long](batchSize * this.numBeamHypothesisToKeep, sentMaxLength.toInt) - var indices = Array.ofDim[Long](batchSize * this.numBeamHypothesisToKeep, sentMaxLength.toInt) + var decoded = Array.ofDim[Int](batchSize * this.numBeamHypothesisToKeep, sentMaxLength.toInt) + var indices = Array.ofDim[Int](batchSize * this.numBeamHypothesisToKeep, sentMaxLength.toInt) if (sentLengths.min != sentLengths.max) { decoded = decoded.map(each => each.map(_ => padTokenId)) } - indices = indices.map(each => each.map(_ => -1L)) + indices = indices.map(each => each.map(_ => -1)) for (i <- best.indices) { val hypo = best(i) val bestIdx = bestIndices(i) From 58e6ab43a40e7a54468aa7697371495aeebe4306 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Sat, 8 Apr 2023 12:51:40 +0000 Subject: [PATCH 18/26] Fix the wrong column in unit test [skip test] Signed-off-by: Maziyar Panahi --- python/test/annotator/seq2seq/bart_transformer_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/test/annotator/seq2seq/bart_transformer_test.py b/python/test/annotator/seq2seq/bart_transformer_test.py index c2637a853ae2..d489d488ae8a 100644 --- a/python/test/annotator/seq2seq/bart_transformer_test.py +++ b/python/test/annotator/seq2seq/bart_transformer_test.py @@ -44,7 +44,7 @@ def runTest(self): pipeline = Pipeline().setStages([document_assembler, bart]) results = pipeline.fit(data).transform(data) - results.select("questions.result", "answers.result").show(truncate=False) + results.select("documents.result", "answers.result").show(truncate=False) @pytest.mark.slow From bf42fa02baa4ae2d99147b311244155c85d6b7c8 Mon Sep 17 00:00:00 2001 From: Devin Ha <33089471+DevinTDHa@users.noreply.github.com> Date: Sun, 9 Apr 2023 16:36:29 +0200 Subject: [PATCH 19/26] SPARKNLP-805: Documentation for release/440 (#13743) --- docs/en/annotators.md | 4 +- .../en/transformer_entries/BartTransformer.md | 176 ++++++++++++++++++ .../BertForZeroShotClassification.md | 135 ++++++++++++++ .../bert_for_zero_shot_classification.py | 7 +- .../annotator/seq2seq/bart_transformer.py | 58 +++--- .../dl/BertForZeroShotClassification.scala | 2 +- .../annotators/seq2seq/BartTransformer.scala | 8 +- 7 files changed, 353 insertions(+), 37 deletions(-) create mode 100644 docs/en/transformer_entries/BartTransformer.md create mode 100644 docs/en/transformer_entries/BertForZeroShotClassification.md diff --git a/docs/en/annotators.md b/docs/en/annotators.md index 3f24ab1c5bb5..b081fc98e2aa 100644 --- a/docs/en/annotators.md +++ b/docs/en/annotators.md @@ -110,10 +110,12 @@ Additionally, these transformers are available. {% include templates/anno_table_entry.md path="./transformers" name="AlbertForQuestionAnswering" summary="AlbertForQuestionAnswering can load ALBERT Models with a span classification head on top for extractive question-answering tasks like SQuAD."%} {% include templates/anno_table_entry.md path="./transformers" name="AlbertForTokenClassification" summary="AlbertForTokenClassification can load ALBERT Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%} {% include templates/anno_table_entry.md path="./transformers" name="AlbertForSequenceClassification" summary="AlbertForSequenceClassification can load ALBERT Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%} -{% include templates/anno_table_entry.md path="./transformers" name="BertEmbeddings" summary="Token-level embeddings using BERT. BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture."%} +{% include templates/anno_table_entry.md path="./transformers" name="AlbertForSequenceClassification" summary="AlbertForSequenceClassification can load ALBERT Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%} +{% include templates/anno_table_entry.md path="./transformers" name="BartTransformer" summary="BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer"%} {% include templates/anno_table_entry.md path="./transformers" name="BertForQuestionAnswering" summary="BertForQuestionAnswering can load Bert Models with a span classification head on top for extractive question-answering tasks like SQuAD."%} {% include templates/anno_table_entry.md path="./transformers" name="BertForSequenceClassification" summary="Bert Models with sequence classification/regression head on top."%} {% include templates/anno_table_entry.md path="./transformers" name="BertForTokenClassification" summary="BertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%} +{% include templates/anno_table_entry.md path="./transformers" name="BertForZeroShotClassification" summary="BertForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language inference) tasks."%} {% include templates/anno_table_entry.md path="./transformers" name="BertSentenceEmbeddings" summary="Sentence-level embeddings using BERT. BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture."%} {% include templates/anno_table_entry.md path="./transformers" name="CamemBertEmbeddings" summary="CamemBert is based on Facebook’s RoBERTa model released in 2019."%} {% include templates/anno_table_entry.md path="./transformers" name="CamemBertForSequenceClassification" summary="amemBertForSequenceClassification can load CamemBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks."%} diff --git a/docs/en/transformer_entries/BartTransformer.md b/docs/en/transformer_entries/BartTransformer.md new file mode 100644 index 000000000000..c101b44c8ed2 --- /dev/null +++ b/docs/en/transformer_entries/BartTransformer.md @@ -0,0 +1,176 @@ +{%- capture title -%} +BartTransformer +{%- endcapture -%} + +{%- capture description -%} +BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, +Translation, and Comprehension Transformer + +The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art +language generation model that was introduced by Facebook AI in 2019. It is based on the +transformer architecture and is designed to handle a wide range of natural language processing +tasks such as text generation, summarization, and machine translation. + +BART is unique in that it is both bidirectional and auto-regressive, meaning that it can +generate text both from left-to-right and from right-to-left. This allows it to capture +contextual information from both past and future tokens in a sentence,resulting in more +accurate and natural language generation. + +The model was trained on a large corpus of text data using a combination of unsupervised and +supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the +model is first trained on a large unlabeled corpus of text, and then fine-tuned on specific +downstream tasks. + +BART has achieved state-of-the-art performance on a wide range of NLP tasks, including +summarization, question-answering, and language translation. Its ability to handle multiple +tasks and its high performance on each of these tasks make it a versatile and valuable tool +for natural language processing applications. + +Pretrained models can be loaded with `pretrained` of the companion object: + +```scala +val bart = BartTransformer.pretrained() + .setInputCols("document") + .setOutputCol("generation") +``` + +The default model is `"bart_large_cnn"`, if no name is provided. For available pretrained +models please see the [Models Hub](https://nlp.johnsnowlabs.com/models?q=bart). + +For extended examples of usage, see +[BartTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala). + +**References:** + +- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://aclanthology.org/2020.acl-main.703.pdf) +- https://github.com/pytorch/fairseq + +**Paper Abstract:** + +*We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART +is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model +to reconstruct the original text. It uses a standard Tranformer-based neural machine +translation architecture which, despite its simplicity, can be seen as generalizing BERT (due +to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent +pretraining schemes. We evaluate a number of noising approaches, finding the best performance +by both randomly shuffling the order of sentences and using a novel in-filling scheme, where +spans of text are replaced with a single mask token. BART is particularly effective when fine +tuned for text generation but also works well for comprehension tasks. It matches the +performance of RoBERTa on GLUE and SQuAD, and achieves new stateof-the-art results on a range +of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 +ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine +translation, with only target language pretraining. We also replicate other pretraining +schemes within the BART framework, to understand their effect on end-task performance* + +**Note:** + +This is a very computationally expensive module especially on larger sequence. The use of an +accelerator such as GPU is recommended. +{%- endcapture -%} + +{%- capture input_anno -%} +DOCUMENT +{%- endcapture -%} + +{%- capture output_anno -%} +DOCUMENT +{%- endcapture -%} + +{%- capture python_example -%} +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline + +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") +bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setInputCols(["documents"]) \ + .setMaxOutputLength(200) \ + .setOutputCol("summaries") + +pipeline = Pipeline().setStages([documentAssembler, bart]) + +data = spark.createDataFrame([[ + "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a " + + "downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness" + + " of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this " + + "paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework " + + "that converts all text-based language problems into a text-to-text format. Our systematic study compares " + + "pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens " + + "of language understanding tasks. By combining the insights from our exploration with scale and our new " + + "Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering " + + "summarization, question answering, text classification, and more. To facilitate future work on transfer " + + "learning for NLP, we release our data set, pre-trained models, and code." +]]).toDF("text") + +result = pipeline.fit(data).transform(data) +result.select("summaries.result").show(truncate=False) ++--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +|result | ++--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +|[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, methodologies, and practice .]| ++--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +{%- endcapture -%} + +{%- capture scala_example -%} +import spark.implicits._ +import com.johnsnowlabs.nlp.base.DocumentAssembler +import com.johnsnowlabs.nlp.annotators.seq2seq.GPT2Transformer +import org.apache.spark.ml.Pipeline + +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val bart = BartTransformer.pretrained("bart_large_cnn") + .setInputCols(Array("documents")) + .setMinOutputLength(10) + .setMaxOutputLength(30) + .setDoSample(true) + .setTopK(50) + .setOutputCol("generation") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, bart)) + +val data = Seq( + "PG&E stated it scheduled the blackouts in response to forecasts for high winds " + + "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " + + "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow." +).toDF("text") +val result = pipeline.fit(data).transform(data) + +results.select("generation.result").show(truncate = false) ++--------------------------------------------------------------+ +|result | ++--------------------------------------------------------------+ +|[Nearly 800 thousand customers were affected by the shutoffs.]| ++--------------------------------------------------------------+ + +{%- endcapture -%} + +{%- capture api_link -%} +[BartTransformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer) +{%- endcapture -%} + +{%- capture python_api_link -%} +[BartTransformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/bart_transformer/index.html#sparknlp.annotator.seq2seq.bart_transformer.BartTransformer) +{%- endcapture -%} + +{%- capture source_link -%} +[BartTransformer](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala) +{%- endcapture -%} + +{% include templates/anno_template.md +title=title +description=description +input_anno=input_anno +output_anno=output_anno +python_example=python_example +scala_example=scala_example +api_link=api_link +python_api_link=python_api_link +source_link=source_link +%} \ No newline at end of file diff --git a/docs/en/transformer_entries/BertForZeroShotClassification.md b/docs/en/transformer_entries/BertForZeroShotClassification.md new file mode 100644 index 000000000000..0802114d750b --- /dev/null +++ b/docs/en/transformer_entries/BertForZeroShotClassification.md @@ -0,0 +1,135 @@ +{%- capture title -%} +BertForZeroShotClassification +{%- endcapture -%} + +{%- capture description -%} +BertForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural +language inference) tasks. Equivalent of `BertForSequenceClassification` models, but these +models don't require a hardcoded number of potential classes, they can be chosen at runtime. +It usually means it's slower but it is much more flexible. + +Any combination of sequences and labels can be passed and each combination will be posed as a +premise/hypothesis pair and passed to the pretrained model. + +Pretrained models can be loaded with `pretrained` of the companion object: + +```scala +val sequenceClassifier = BertForZeroShotClassification.pretrained() + .setInputCols("token", "document") + .setOutputCol("label") +``` + +The default model is `"bert_base_cased_zero_shot_classifier_xnli"`, if no name is provided. + +For available pretrained models please see the +[Models Hub](https://nlp.johnsnowlabs.com/models?task=Text+Classification). + +To see which models are compatible and how to import them see +https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended +examples, see +[BertForZeroShotClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala). +{%- endcapture -%} + +{%- capture input_anno -%} +DOCUMENT, TOKEN +{%- endcapture -%} + +{%- capture output_anno -%} +CATEGORY +{%- endcapture -%} + +{%- capture python_example -%} +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline + +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") +tokenizer = Tokenizer() \ + .setInputCols(["document"]) \ + .setOutputCol("token") +sequenceClassifier = BertForZeroShotClassification.pretrained() \ + .setInputCols(["token", "document"]) \ + .setOutputCol("label") \ + .setCaseSensitive(True) + +pipeline = Pipeline().setStages([ + documentAssembler, + tokenizer, + sequenceClassifier +]) + +data = spark.createDataFrame([["I loved this movie when I was a child.", "It was pretty boring."]]).toDF("text") +result = pipeline.fit(data).transform(data) +result.select("label.result").show(truncate=False) ++------+ +|result| ++------+ +|[pos] | +|[neg] | ++------+ +{%- endcapture -%} + +{%- capture scala_example -%} +import spark.implicits._ +import com.johnsnowlabs.nlp.base._ +import com.johnsnowlabs.nlp.annotator._ +import org.apache.spark.ml.Pipeline + +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForZeroShotClassification.pretrained() + .setInputCols("token", "document") + .setOutputCol("label") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array( + documentAssembler, + tokenizer, + sequenceClassifier +)) + +val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text") +val result = pipeline.fit(data).transform(data) + +result.select("label.result").show(false) ++------+ +|result| ++------+ +|[pos] | +|[neg] | ++------+ + +{%- endcapture -%} + +{%- capture api_link -%} +[BertForZeroShotClassification](/api/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification) +{%- endcapture -%} + +{%- capture python_api_link -%} +[BertForZeroShotClassification](/api/python/reference/autosummary/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification/index.html#sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification.BertForZeroShotClassification) +{%- endcapture -%} + +{%- capture source_link -%} +[BertForZeroShotClassification](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala) +{%- endcapture -%} + +{% include templates/anno_template.md +title=title +description=description +input_anno=input_anno +output_anno=output_anno +python_example=python_example +scala_example=scala_example +api_link=api_link +python_api_link=python_api_link +source_link=source_link +%} \ No newline at end of file diff --git a/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py b/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py index bf03580f80e1..5287ea6f1b21 100755 --- a/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py +++ b/python/sparknlp/annotator/classifier_dl/bert_for_zero_shot_classification.py @@ -151,9 +151,10 @@ def setMaxSentenceLength(self, value): return self._set(maxSentenceLength=value) def setCoalesceSentences(self, value): - """Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences. - Due to max sequence length limit in almost all transformer models such as BERT (512 tokens), this parameter helps feeding all the sentences - into the model and averaging all the probabilities for the entire document instead of probabilities per sentence. (Default: true) + """Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging + probabilities in all sentences. Due to max sequence length limit in almost all transformer models such as BERT + (512 tokens), this parameter helps to feed all the sentences into the model and averaging all the probabilities + for the entire document instead of probabilities per sentence. (Default: true) Parameters ---------- diff --git a/python/sparknlp/annotator/seq2seq/bart_transformer.py b/python/sparknlp/annotator/seq2seq/bart_transformer.py index 04e177be029a..5c04a3729197 100755 --- a/python/sparknlp/annotator/seq2seq/bart_transformer.py +++ b/python/sparknlp/annotator/seq2seq/bart_transformer.py @@ -54,8 +54,8 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): pretrained models please see the `Models Hub `__. - For extended examples of usage, see the `Examples - `__. + For extended examples of usage, see the `BartTestSpec + `__. ====================== ====================== Input Annotation types Output Annotation type @@ -65,34 +65,36 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): Parameters ---------- + batchSize + Batch Size, by default `1`. configProtoBytes ConfigProto from tensorflow, serialized into byte array. task - Transformer's task, e.g. ``summarize:`` + Transformer's task, e.g. ``summarize:``, by default `""`. minOutputLength - Minimum length of the sequence to be generated + Minimum length of the sequence to be generated, by default `0`. maxOutputLength - Maximum length of output text + Maximum length of output text, by default `20`. doSample - Whether or not to use sampling; use greedy decoding otherwise + Whether or not to use sampling; use greedy decoding otherwise, by default `False`. temperature - The value used to module the next token probabilities + The value used to module the next token probabilities, by default `1.0`. topK The number of highest probability vocabulary tokens to keep for - top-k-filtering - BeamSize - The number of beam size for beam search + top-k-filtering, by default `50`. + beamSize + The number of beam size for beam search, by default `1`. topP - Top cumulative probability for vocabulary tokens + Top cumulative probability for vocabulary tokens, by default `1.0`. If set to float < 1, only the most probable tokens with probabilities that add up to ``topP`` or higher are kept for generation. repetitionPenalty - The parameter for repetition penalty. 1.0 means no penalty. + The parameter for repetition penalty. 1.0 means no penalty, by default `1.0`. noRepeatNgramSize - If set to int > 0, all ngrams of that size can only occur once + If set to int > 0, all ngrams of that size can only occur once, by default `0`. ignoreTokenIds - A list of token ids which are ignored in the decoder's output + A list of token ids which are ignored in the decoder's output, by default `[]`. Notes ----- @@ -155,7 +157,7 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): |result | +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ |[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, methodologies, and practice .]| - --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ """ name = "BartTransformer" @@ -204,11 +206,11 @@ class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine): typeConverter=TypeConverters.toListInt) beamSize = Param(Params._dummy(), "beamSize", - "The Number of beams for beam search.", - typeConverter=TypeConverters.toInt) + "The Number of beams for beam search.", + typeConverter=TypeConverters.toInt) def setIgnoreTokenIds(self, value): - """A list of token ids which are ignored in the decoder's output. + """A list of token ids which are ignored in the decoder's output, by default `[]`. Parameters ---------- @@ -228,7 +230,7 @@ def setConfigProtoBytes(self, b): return self._set(configProtoBytes=b) def setTask(self, value): - """Sets the transformer's task, e.g. ``summarize:``. + """Sets the transformer's task, e.g. ``summarize:``, by default `""`. Parameters ---------- @@ -238,7 +240,7 @@ def setTask(self, value): return self._set(task=value) def setMinOutputLength(self, value): - """Sets minimum length of the sequence to be generated. + """Sets minimum length of the sequence to be generated, by default `0`. Parameters ---------- @@ -248,7 +250,7 @@ def setMinOutputLength(self, value): return self._set(minOutputLength=value) def setMaxOutputLength(self, value): - """Sets maximum length of output text. + """Sets maximum length of output text, by default `20`. Parameters ---------- @@ -258,7 +260,7 @@ def setMaxOutputLength(self, value): return self._set(maxOutputLength=value) def setDoSample(self, value): - """Sets whether or not to use sampling, use greedy decoding otherwise. + """Sets whether or not to use sampling, use greedy decoding otherwise, by default `False`. Parameters ---------- @@ -268,7 +270,7 @@ def setDoSample(self, value): return self._set(doSample=value) def setTemperature(self, value): - """Sets the value used to module the next token probabilities. + """Sets the value used to module the next token probabilities, by default `1.0`. Parameters ---------- @@ -279,7 +281,7 @@ def setTemperature(self, value): def setTopK(self, value): """Sets the number of highest probability vocabulary tokens to keep for - top-k-filtering. + top-k-filtering, by default `50`. Parameters ---------- @@ -289,7 +291,7 @@ def setTopK(self, value): return self._set(topK=value) def setTopP(self, value): - """Sets the top cumulative probability for vocabulary tokens. + """Sets the top cumulative probability for vocabulary tokens, by default `1.0`. If set to float < 1, only the most probable tokens with probabilities that add up to ``topP`` or higher are kept for generation. @@ -302,7 +304,7 @@ def setTopP(self, value): return self._set(topP=value) def setRepetitionPenalty(self, value): - """Sets the parameter for repetition penalty. 1.0 means no penalty. + """Sets the parameter for repetition penalty. 1.0 means no penalty, by default `1.0`. Parameters ---------- @@ -317,7 +319,7 @@ def setRepetitionPenalty(self, value): return self._set(repetitionPenalty=value) def setNoRepeatNgramSize(self, value): - """Sets size of n-grams that can only occur once. + """Sets size of n-grams that can only occur once, by default `0`. If set to int > 0, all ngrams of that size can only occur once. @@ -329,7 +331,7 @@ def setNoRepeatNgramSize(self, value): return self._set(noRepeatNgramSize=value) def setBeamSize(self, value): - """Sets the number of beam size for beam search + """Sets the number of beam size for beam search, by default `4`. Parameters ---------- diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala index 054838b89225..46101feb73e5 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/BertForZeroShotClassification.scala @@ -202,7 +202,7 @@ class BertForZeroShotClassification(override val uid: String) val coalesceSentences = new BooleanParam( this, "coalesceSentences", - "If sets to true the output of all sentences will be averaged to one output instead of one output per sentence. Default to true.") + "If sets to true the output of all sentences will be averaged to one output instead of one output per sentence. Defaults to false.") /** @group setParam */ def setCoalesceSentences(value: Boolean): this.type = set(coalesceSentences, value) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala index a539823efa9f..4afd0796a99d 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTransformer.scala @@ -126,11 +126,11 @@ import org.apache.spark.sql.SparkSession * val result = pipeline.fit(data).transform(data) * * results.select("generation.result").show(truncate = false) - * +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ - * |result | - * +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + * +--------------------------------------------------------------+ + * |result | + * +--------------------------------------------------------------+ * |[Nearly 800 thousand customers were affected by the shutoffs.]| - * +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + * +--------------------------------------------------------------+ * }}} * * @param uid From a9dc89481fa82caf3e1d76ff7df7f59fbd29b7ea Mon Sep 17 00:00:00 2001 From: Prabod Rathnayaka Date: Sun, 9 Apr 2023 14:56:43 +0000 Subject: [PATCH 20/26] Fixed memory leak --- src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala b/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala index 57f51fd68abc..680cfc0bf5b6 100644 --- a/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala +++ b/src/main/scala/com/johnsnowlabs/ml/ai/Bart.scala @@ -227,6 +227,10 @@ private[johnsnowlabs] class Bart( tensorEncoder.clearTensors() tensorEncoder.clearSession(encoderOuts) + decoderEncoderStateTensorResources.clearTensors() + decoderEncoderStateTensors.close() + encoderAttentionMaskTensors.close() + encoderInputTensors.close() modelOutputs } @@ -845,9 +849,11 @@ private[johnsnowlabs] class Bart( .grouped(decoderInputLength) .toArray val nextTokenLogits = for (decoderOutput <- decoderOutputs) yield decoderOutput.last + decoderOuts.foreach(_.close()) tensorDecoder.clearTensors() tensorDecoder.clearSession(decoderOuts) decoderInputTensors.close() + decoderAttentionMaskTensors.close() nextTokenLogits } } From 46d4cb44601a8a2ea11d4cf4f50568a15d93f795 Mon Sep 17 00:00:00 2001 From: Prabod Rathnayaka Date: Sun, 9 Apr 2023 15:04:05 +0000 Subject: [PATCH 21/26] Added Bart Notebook --- .../Text_Summarization_with_BART.ipynb | 635 ++++++++++++++++++ 1 file changed, 635 insertions(+) create mode 100644 examples/python/annotation/text/english/text-summarization/Text_Summarization_with_BART.ipynb diff --git a/examples/python/annotation/text/english/text-summarization/Text_Summarization_with_BART.ipynb b/examples/python/annotation/text/english/text-summarization/Text_Summarization_with_BART.ipynb new file mode 100644 index 000000000000..4b67426c8864 --- /dev/null +++ b/examples/python/annotation/text/english/text-summarization/Text_Summarization_with_BART.ipynb @@ -0,0 +1,635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "sXatvRX899i0" + }, + "source": [ + "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "44B_JtXmyOuF" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/Text_Summarization_with_BART.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kl6fW8Fkf3vN" + }, + "source": [ + "# Document Summarization with [BART](https://arxiv.org/pdf/1910.13461.pdf)\n", + "\n", + "The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation.\n", + " \n", + "BART is unique in that it is both bidirectional and auto-regressive, meaning that it can generate text both from left-to-right and from right-to-left. This allows it to capture contextual information from both past and future tokens in a sentence,resulting in more accurate and natural language generation.\n", + " \n", + "The model was trained on a large corpus of text data using a combination of unsupervised and supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the model is first trained on a large unlabeled corpus of text, and then fine-tuned on specific downstream tasks.\n", + " \n", + "BART has achieved state-of-the-art performance on a wide range of NLP tasks, including summarization, question-answering, and language translation. Its ability to handle multiple tasks and its high performance on each of these tasks make it a versatile and valuable tool for natural language processing applications." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "06DMedvcgjKa" + }, + "source": [ + "## Colab Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3Mc2rld9f7YW" + }, + "outputs": [], + "source": [ + "!pip install -q pyspark==3.3.2 spark-nlp==4.4.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KjcBNzFVgoky" + }, + "outputs": [], + "source": [ + "import sparknlp\n", + "\n", + "from sparknlp.base import *\n", + "from sparknlp.common import *\n", + "from sparknlp.annotator import *\n", + "from pyspark.ml import Pipeline\n", + "import pandas as pd\n", + "\n", + "# Comment out this line and uncomment the next one to enable GPU mode and High RAM\n", + "# \n", + "spark = sparknlp.start()\n", + "\n", + "# spark = sparknlp.start(gpu=True)\n", + "\n", + "print(\"Spark NLP version\", sparknlp.version())\n", + "print(\"Apache Spark version:\", spark.version)\n", + "\n", + "spark" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JZyycKyzQcwQ" + }, + "source": [ + "# Download BART Model and Create Spark NLP Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 375 + }, + "id": "gXdynTiFHKLK", + "outputId": "bebd77ef-ac8c-49ab-bc06-dbf8f7f03a32", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "distilbart_xsum_12_6 download started this may take some time.\n", + "Approximate size to download 814.9 MB\n", + "[ / ]distilbart_xsum_12_6 download started this may take some time.\n", + "Approximate size to download 814.9 MB\n", + "Download done! Loading the resource.\n", + "[ — ]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ — ]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-04-08 08:52:08.552206: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[OK!]\n" + ] + } + ], + "source": [ + "documentAssembler = DocumentAssembler() \\\n", + " .setInputCol(\"text\") \\\n", + " .setOutputCol(\"document\") \n", + "\n", + "# Can take in document or sentence columns\n", + "bart = BartTransformer.pretrained(name=\"distilbart_xsum_12_6\",lang='en') \\\n", + " .setInputCols('document')\\\n", + " .setOutputCol(\"Bart\")\\\n", + " .setMaxOutputLength(100)\n", + "\n", + "# Build pipeline with BART\n", + "pipe_components = [documentAssembler, bart]\n", + "pipeline = Pipeline().setStages( pipe_components)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tUfqQBdtNTS6" + }, + "source": [ + "# Summarize documents" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X1-x-TLL5iJK", + "outputId": "e5409d11-8ee2-4f94-d78b-697a53fa584f", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "BartTRANSFORMER_117e3797a285" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set the task for questions on T5\n", + "bart.setTask('summarize')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fzKvMHteNUTB", + "outputId": "f5a3227d-c481-461d-c032-f1ceb9d1571c", + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:=======================================================> (30 + 1) / 31]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------------------------------------------------------------------------------------------------------------------+\n", + "|result |\n", + "+---------------------------------------------------------------------------------------------------------------------------+\n", + "|[The world's biggest credit- card company is joining the growing trend of accepting cryptocurrencies as a form of payment.]|\n", + "+---------------------------------------------------------------------------------------------------------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "# https://www.reuters.com/article/instant-article/idCAKBN2AA2WF\n", + "\n", + "text = \"\"\"(Reuters) - Mastercard Inc said on Wednesday it was planning to offer support for some cryptocurrencies on its network this year, joining a string of big-ticket firms that have pledged similar support.\n", + "\n", + "The credit-card giant’s announcement comes days after Elon Musk’s Tesla Inc revealed it had purchased $1.5 billion of bitcoin and would soon accept it as a form of payment.\n", + "\n", + "Asset manager BlackRock Inc and payments companies Square and PayPal have also recently backed cryptocurrencies.\n", + "\n", + "Mastercard already offers customers cards that allow people to transact using their cryptocurrencies, although without going through its network.\n", + "\n", + "\"Doing this work will create a lot more possibilities for shoppers and merchants, allowing them to transact in an entirely new form of payment. This change may open merchants up to new customers who are already flocking to digital assets,\" Mastercard said. (mstr.cd/3tLaPZM)\n", + "\n", + "Mastercard specified that not all cryptocurrencies will be supported on its network, adding that many of the hundreds of digital assets in circulation still need to tighten their compliance measures.\n", + "\n", + "Many cryptocurrencies have struggled to win the trust of mainstream investors and the general public due to their speculative nature and potential for money laundering.\n", + "\"\"\"\n", + "data = [[text]]\n", + "df=spark.createDataFrame(data).toDF('text')\n", + "#Predict on text data with BART\n", + "model = pipeline.fit(df)\n", + "annotated_df = model.transform(df)\n", + "annotated_df.select(['bart.result']).show(truncate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cdII1LrdNq3p", + "outputId": "5bc00a17-7de1-49e3-d2ca-03ec07d89b73", + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 15:======================================================> (30 + 1) / 31]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Length 1284 Summarized Length : 121 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "v = annotated_df.take(1)\n", + "print(f\"Original Length {len(v[0].text)} Summarized Length : {len(v[0].Bart[0].result)} \")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "do_7s8baOCej", + "outputId": "7425b0b5-b6b5-45b3-a07c-3db2c2fd35c4", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"The world's biggest credit- card company is joining the growing trend of accepting cryptocurrencies as a form of payment.\"" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Full summarized text\n", + "v[0].Bart[0].result" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_XcVWsrDtW6X" + }, + "source": [ + "# Explore BART Parameters and Play with Params\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yxj99xiOtRwX" + }, + "source": [ + "### Sampling Methods\n", + "\n", + "\n", + "Sampling means we **randomly** draw from a distribution of words. \n", + "The probability distribution is conditioned on all previous tokens in a text to generate the next token. \n", + "\n", + "By default the distribution contains all words in the vocabulary of BART, where many candidates are incorrect to generate.\n", + "\n", + "There are two methods of reshaping and drawing from those distributions : \n", + "\n", + "1. **Top-K Sampling** Take the k most likely words from the original distribution. Redistribute probability mass among those k words and draw according to the new probabilities.\n", + "\n", + "2. **Top-P Nucleus sampling** Take smallest possible set of N words, which together have a probability of p. Redistribute probability mass among those N words and draw according to the new probabilities.\n", + "\n", + "\n", + "\n", + "Additionally, both methods can be tweaked ith the following parameters : \n", + "\n", + "- **temperature** : Parameter of the softmax function which affect the distrubtion computed by the model. The closer we are to 0, the more deterministic the probability will become, distribution tails will become slimmer and outlier word probabilites are more close to 0. Temperature values closer values to 1 make tails of probability fatter which makes outliers more probable and generic results less probable. \n", + "\n", + "\n", + "These parameters are shared by all method : \n", + "- **beamSize**: Number of beams in the beam search\n", + "- **ignoreTokenIds**: A list of token ids which are ignored in the decoder's output (default: [])\n", + "- **noRepeatNgramSize**: If set to int > 0, all ngrams of that size can only occur once \n", + "- **repetitionPenalty**: The parameter for repetition penalty. 1.0 means no penalty. https://arxiv.org/pdf/1909.05858.pdf> \n", + "- **task**: Transformer's task, e.g. 'is it true that'> (default: , current: generate)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rtj0kZV5tvBm" + }, + "source": [ + "### Play with temperature \n", + "Set Temperature higher to make GPT more random/creative and text less coherent\n", + "Temperature > 0 and Temperature <=1\n", + "You must set `bart.setDoSample(True)` to have non-deterministic results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "Gmf32IdwgNlO", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "BartTRANSFORMER_117e3797a285" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bart.setTemperature(0.7)\n", + "bart.setDoSample(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "chCkax_jtF4R" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 19:======================================================> (30 + 1) / 31]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "|result |\n", + "+--------------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "|[The bitcoin virtual currency is set to get a major boost from the US credit card giant Mastercard its support for some of the virtual currency's cash.]|\n", + "+--------------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "data = [[text]]\n", + "df=spark.createDataFrame(data).toDF('text')\n", + "#Predict on text data with BART\n", + "model = pipeline.fit(df)\n", + "annotated_df = model.transform(df)\n", + "annotated_df.select(['bart.result']).show(truncate=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_nfbE1r7gSEb" + }, + "source": [ + "### Play with beamSize\n", + "\n", + "When `beamSize = 1` the decoding process produce the greedy search, and when beamSize > 1 it produce the decoded output with beam search\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "Qn4_6A6lhcGX" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "BartTRANSFORMER_117e3797a285" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bart.setBeamSize(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "qkoBNgpuhi-f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 23:======================================================> (30 + 1) / 31]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "|result |\n", + "+-------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "|[The cryptocurrency bitcoin is expected to reach a new level of use this year, after Mastercard said it would accept some of the digital assets.]|\n", + "+-------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "data = [[text]]\n", + "df=spark.createDataFrame(data).toDF('text')\n", + "#Predict on text data with BART\n", + "model = pipeline.fit(df)\n", + "annotated_df = model.transform(df)\n", + "annotated_df.select(['bart.result']).show(truncate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "GRDbFRWDhkte" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "BartTRANSFORMER_117e3797a285" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set beam size to 4\n", + "bart.setBeamSize(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "yrtCol7WhnSF" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 31:======================================================> (30 + 1) / 31]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------------------------------------------------------------------------------------------------------------------------------+\n", + "|result |\n", + "+-----------------------------------------------------------------------------------------------------------------------------------------+\n", + "|[South a row has escalidated over the the value of cryptocurrencies, with Mastercard now pledging to support some of the digital assets.]|\n", + "+-----------------------------------------------------------------------------------------------------------------------------------------+\n", + "\n", + "CPU times: user 48.6 ms, sys: 10.9 ms, total: 59.5 ms\n", + "Wall time: 1min 6s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "%%time\n", + "data = [[text]]\n", + "df=spark.createDataFrame(data).toDF('text')\n", + "#Predict on text data with BART\n", + "model = pipeline.fit(df)\n", + "annotated_df = model.transform(df)\n", + "annotated_df.select(['bart.result']).show(truncate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 6fb47c094f51ed93a2e3a1b2c1da9d3ab32dda17 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Mon, 10 Apr 2023 17:00:05 +0200 Subject: [PATCH 22/26] Add new features and update docs[run doc] --- README.md | 17 ++++++++++------- docs/_layouts/landing.html | 38 ++++++++++++++++++++------------------ docs/en/install.md | 22 +++++++++++----------- python/README.md | 17 ++++++++++------- 4 files changed, 51 insertions(+), 43 deletions(-) diff --git a/README.md b/README.md index fa5682b3092c..82746009c1b8 100644 --- a/README.md +++ b/README.md @@ -19,10 +19,10 @@ Spark NLP is a state-of-the-art Natural Language Processing library built on top of Apache Spark. It provides **simple**, **performant** & **accurate** NLP annotations for machine learning pipelines that **scale** easily in a distributed environment. -Spark NLP comes with **11000+** pretrained **pipelines** and **models** in more than **200+** languages. -It also offers tasks such as **Tokenization**, **Word Segmentation**, **Part-of-Speech Tagging**, Word and Sentence **Embeddings**, **Named Entity Recognition**, **Dependency Parsing**, **Spell Checking**, **Text Classification**, **Sentiment Analysis**, **Token Classification**, **Machine Translation** (+180 languages), **Summarization**, **Question Answering**, **Table Question Answering**, **Text Generation**, **Image Classification**, **Automatic Speech Recognition**, and many more [NLP tasks](#features). +Spark NLP comes with **17000+** pretrained **pipelines** and **models** in more than **200+** languages. +It also offers tasks such as **Tokenization**, **Word Segmentation**, **Part-of-Speech Tagging**, Word and Sentence **Embeddings**, **Named Entity Recognition**, **Dependency Parsing**, **Spell Checking**, **Text Classification**, **Sentiment Analysis**, **Token Classification**, **Machine Translation** (+180 languages), **Summarization**, **Question Answering**, **Table Question Answering**, **Text Generation**, **Image Classification**, **Automatic Speech Recognition**, **Zero-Shot Learning**, and many more [NLP tasks](#features). -**Spark NLP** is the only open-source NLP library in **production** that offers state-of-the-art transformers such as **BERT**, **CamemBERT**, **ALBERT**, **ELECTRA**, **XLNet**, **DistilBERT**, **RoBERTa**, **DeBERTa**, **XLM-RoBERTa**, **Longformer**, **ELMO**, **Universal Sentence Encoder**, **Google T5**, **MarianMT**, **GPT2**, and **Vision Transformers (ViT)** not only to **Python** and **R**, but also to **JVM** ecosystem (**Java**, **Scala**, and **Kotlin**) at **scale** by extending **Apache Spark** natively. +**Spark NLP** is the only open-source NLP library in **production** that offers state-of-the-art transformers such as **BERT**, **CamemBERT**, **ALBERT**, **ELECTRA**, **XLNet**, **DistilBERT**, **RoBERTa**, **DeBERTa**, **XLM-RoBERTa**, **Longformer**, **ELMO**, **Universal Sentence Encoder**, **Facebook BART**, **Google T5**, **MarianMT**, **OpenAI GPT2**, and **Vision Transformers (ViT)** not only to **Python** and **R**, but also to **JVM** ecosystem (**Java**, **Scala**, and **Kotlin**) at **scale** by extending **Apache Spark** natively. ## Project's website @@ -137,19 +137,22 @@ documentation and examples - Longformer for Question Answering - Table Question Answering (TAPAS) - Zero-Shot NER Model +- Zero Shot Text Classification by BERT (ZSL) - Neural Machine Translation (MarianMT) - Text-To-Text Transfer Transformer (Google T5) - Generative Pre-trained Transformer 2 (OpenAI GPT2) -- Vision Transformer (ViT) -- Swin Image Classification +- Seq2Seq for NLG, Translation, and Comprehension (Facebook BART) +- Vision Transformer (Google ViT) +- Swin Image Classification (Microsoft Swin Transformer) +- ConvNext Image Classification (Facebook ConvNext) - Automatic Speech Recognition (Wav2Vec2) - Automatic Speech Recognition (HuBERT) - Named entity recognition (Deep learning) - Easy TensorFlow integration - GPU Support - Full integration with Spark ML functions -- +9400 pre-trained models in +200 languages! -- +3200 pre-trained pipelines in +200 languages! +- +12000 pre-trained models in +200 languages! +- +5000 pre-trained pipelines in +200 languages! - Multi-lingual NER models: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, German, Hebrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Urdu, and more. diff --git a/docs/_layouts/landing.html b/docs/_layouts/landing.html index 4ffd3f28d5b8..2c33f6187ec6 100755 --- a/docs/_layouts/landing.html +++ b/docs/_layouts/landing.html @@ -201,22 +201,22 @@

{{ _section.title }}

{% highlight bash %} # Install Spark NLP from PyPI - $ pip install spark-nlp==4.3.2 pyspark==3.3.0 + $ pip install spark-nlp==4.4.0 pyspark==3.3.0 # Install Spark NLP from Anaconda/Conda $ conda install -c johnsnowlabs spark-nlp # Load Spark NLP with Spark Shell - $ spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + $ spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 # Load Spark NLP with PySpark - $ pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + $ pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 # Load Spark NLP with Spark Submit - $ spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.3.2 + $ spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0 # Load Spark NLP as an external Fat JAR - $ spark-shell --jars spark-nlp-assembly-4.3.2.jar + $ spark-shell --jars spark-nlp-assembly-4.4.0.jar {% endhighlight %}
@@ -236,8 +236,8 @@

Transformers at Scale

Spark NLP is the only open-source NLP library in production that offers state-of-the-art transformers such as BERT, CamemBERT, ALBERT, ELECTRA, XLNet, DistilBERT, RoBERTa, DeBERTa, - XLM-RoBERTa, Longformer, ELMO, Universal Sentence Encoder, Google T5, MarianMT, and OpenAI GPT2 not only to Python, and R - but also to JVM ecosystem (Java, Scala, and Kotlin) at scale by extending Apache Spark natively + XLM-RoBERTa, Longformer, ELMO, Universal Sentence Encoder, Facebook BART, Google T5, MarianMT, OpenAI GPT2, + Google ViT, ASR Wav2Vec2 and many more not only to Python, and R but also to JVM ecosystem (Java, Scala, and Kotlin) at scale by extending Apache Spark natively
@@ -313,18 +313,11 @@

NLP Features

  • ALBERT Embeddings
  • XLNet Embeddings
  • ELMO Embeddings
    • - - -
      -
    • Universal Sentence Encoder
    • Sentence Embeddings
    • Chunk Embeddings
      • -
    • Neural Machine Translation (MarianMT)
      • -
    • Text-To-Text Transfer Transformer (Google T5)
      • -
    • Generative Pre-trained Transformer 2 (OpenAI GPT-2)
      • -
    • Vision Transformer (ViT) Image Classification
      • -
    • Automatic Speech Recognition (Wav2Vec2)
      • +
    +
    • Table Question Answering (TAPAS)
    • Unsupervised keywords extraction
    • Language Detection & Identification (up to 375 languages)
      • @@ -342,11 +335,20 @@

      NLP Features

    • Longformer for Token & Sequence Classification
    • Transformer-based Question Answering
    • Named entity recognition (DL model)
      • +
    • Facebook BART NLG, Translation, and Comprehension
      • +
    • Zero-Shot NER & Text Classification (ZSL)
      • +
    • Neural Machine Translation (MarianMT)
      • +
    • Text-To-Text Transfer Transformer (Google T5)
      • +
    • Generative Pre-trained Transformer 2 (OpenAI GPT-2)
      • +
    • Vision Transformer (Google ViT) Image Classification
      • +
    • Microsoft Swin Transformer Image Classification
      • +
    • Facebook ConvNext Image Classification
      • +
    • Automatic Speech Recognition (Wav2Vec2 & HuBERT)
    • Easy TensorFlow integration
    • GPU Support
    • Full integration with Spark ML functions
      • -
    • 9400+ pre-trained models in 200+ languages! -
    • 3200+ pre-trained pipelines in 200+ languages! +
    • 12000+ pre-trained models in 200+ languages! +
    • 5000+ pre-trained pipelines in 200+ languages!
    {% highlight python %} diff --git a/docs/en/install.md b/docs/en/install.md index 0a777d03bef0..eb5744580b1e 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -106,13 +106,13 @@ spark = SparkSession.builder \ ``` -**spark-nlp-m1:** +**spark-nlp-silicon:** ```xml - + com.johnsnowlabs.nlp - spark-nlp-m1_2.12 + spark-nlp-silicon_2.12 4.4.0 ``` @@ -144,11 +144,11 @@ libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.4.0" libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.4.0" ``` -**spark-nlp-m1:** +**spark-nlp-silicon:** ```scala -// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-m1 -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-m1" % "4.4.0" +// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-silicon +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.4.0" ``` **spark-nlp-aarch64:** @@ -220,7 +220,7 @@ as expected. Adding Spark NLP to your Scala or Java project is easy: -Simply change to dependency coordinates to `spark-nlp-m1` and add the dependency to your +Simply change to dependency coordinates to `spark-nlp-silicon` and add the dependency to your project. How to do this is mentioned above: [Scala And Java](#scala-and-java) @@ -229,10 +229,10 @@ So for example for Spark NLP with Apache Spark 3.0.x and 3.1.x you will end up w maven coordinates like these: ```xml - + com.johnsnowlabs.nlp - spark-nlp-m1_2.12 + spark-nlp-silicon_2.12 4.4.0 ``` @@ -241,7 +241,7 @@ or in case of sbt: ```scala // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-m1" % "4.4.0" +libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.4.0" ``` If everything went well, you can now start Spark NLP with the `m1` flag set to `true`: @@ -269,7 +269,7 @@ If everything went well, you can now start Spark NLP with the `m1` flag set to ` ```python import sparknlp -spark = sparknlp.start(m1=True) +spark = sparknlp.start(apple_silicon=True) ``` ## Installation for Linux Aarch64 Systems diff --git a/python/README.md b/python/README.md index fa5682b3092c..82746009c1b8 100644 --- a/python/README.md +++ b/python/README.md @@ -19,10 +19,10 @@ Spark NLP is a state-of-the-art Natural Language Processing library built on top of Apache Spark. It provides **simple**, **performant** & **accurate** NLP annotations for machine learning pipelines that **scale** easily in a distributed environment. -Spark NLP comes with **11000+** pretrained **pipelines** and **models** in more than **200+** languages. -It also offers tasks such as **Tokenization**, **Word Segmentation**, **Part-of-Speech Tagging**, Word and Sentence **Embeddings**, **Named Entity Recognition**, **Dependency Parsing**, **Spell Checking**, **Text Classification**, **Sentiment Analysis**, **Token Classification**, **Machine Translation** (+180 languages), **Summarization**, **Question Answering**, **Table Question Answering**, **Text Generation**, **Image Classification**, **Automatic Speech Recognition**, and many more [NLP tasks](#features). +Spark NLP comes with **17000+** pretrained **pipelines** and **models** in more than **200+** languages. +It also offers tasks such as **Tokenization**, **Word Segmentation**, **Part-of-Speech Tagging**, Word and Sentence **Embeddings**, **Named Entity Recognition**, **Dependency Parsing**, **Spell Checking**, **Text Classification**, **Sentiment Analysis**, **Token Classification**, **Machine Translation** (+180 languages), **Summarization**, **Question Answering**, **Table Question Answering**, **Text Generation**, **Image Classification**, **Automatic Speech Recognition**, **Zero-Shot Learning**, and many more [NLP tasks](#features). -**Spark NLP** is the only open-source NLP library in **production** that offers state-of-the-art transformers such as **BERT**, **CamemBERT**, **ALBERT**, **ELECTRA**, **XLNet**, **DistilBERT**, **RoBERTa**, **DeBERTa**, **XLM-RoBERTa**, **Longformer**, **ELMO**, **Universal Sentence Encoder**, **Google T5**, **MarianMT**, **GPT2**, and **Vision Transformers (ViT)** not only to **Python** and **R**, but also to **JVM** ecosystem (**Java**, **Scala**, and **Kotlin**) at **scale** by extending **Apache Spark** natively. +**Spark NLP** is the only open-source NLP library in **production** that offers state-of-the-art transformers such as **BERT**, **CamemBERT**, **ALBERT**, **ELECTRA**, **XLNet**, **DistilBERT**, **RoBERTa**, **DeBERTa**, **XLM-RoBERTa**, **Longformer**, **ELMO**, **Universal Sentence Encoder**, **Facebook BART**, **Google T5**, **MarianMT**, **OpenAI GPT2**, and **Vision Transformers (ViT)** not only to **Python** and **R**, but also to **JVM** ecosystem (**Java**, **Scala**, and **Kotlin**) at **scale** by extending **Apache Spark** natively. ## Project's website @@ -137,19 +137,22 @@ documentation and examples - Longformer for Question Answering - Table Question Answering (TAPAS) - Zero-Shot NER Model +- Zero Shot Text Classification by BERT (ZSL) - Neural Machine Translation (MarianMT) - Text-To-Text Transfer Transformer (Google T5) - Generative Pre-trained Transformer 2 (OpenAI GPT2) -- Vision Transformer (ViT) -- Swin Image Classification +- Seq2Seq for NLG, Translation, and Comprehension (Facebook BART) +- Vision Transformer (Google ViT) +- Swin Image Classification (Microsoft Swin Transformer) +- ConvNext Image Classification (Facebook ConvNext) - Automatic Speech Recognition (Wav2Vec2) - Automatic Speech Recognition (HuBERT) - Named entity recognition (Deep learning) - Easy TensorFlow integration - GPU Support - Full integration with Spark ML functions -- +9400 pre-trained models in +200 languages! -- +3200 pre-trained pipelines in +200 languages! +- +12000 pre-trained models in +200 languages! +- +5000 pre-trained pipelines in +200 languages! - Multi-lingual NER models: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, German, Hebrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Urdu, and more. From f1ff4c1e836e55feb30de55203f8620a59fba1f5 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Mon, 10 Apr 2023 17:01:35 +0200 Subject: [PATCH 23/26] Update install.md --- docs/en/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en/install.md b/docs/en/install.md index eb5744580b1e..684e1f5ddb09 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -249,10 +249,10 @@ If everything went well, you can now start Spark NLP with the `m1` flag set to ` ```scala import com.johnsnowlabs.nlp.SparkNLP -val spark = SparkNLP.start(m1 = true) +val spark = SparkNLP.start(apple_silicon = true) ``` -### Python for M1 +### Python for M1 & M2 First, make sure you have a recent Python 3 installation. From f83c4e4d3d1eb50c3849928c9a828bc17e1d71c1 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Mon, 10 Apr 2023 18:03:23 +0200 Subject: [PATCH 24/26] Update CHANGELOG [run doc] --- CHANGELOG | 20 + ...ero_Shot_Text_Classification_by_BERT.ipynb | 445 ++++++++++++++++++ 2 files changed, 465 insertions(+) create mode 100644 examples/python/annotation/text/english/zero-shot text classification/Zero_Shot_Text_Classification_by_BERT.ipynb diff --git a/CHANGELOG b/CHANGELOG index 8a87d011587c..47dcdddaba3a 100644 --- a/CHANGELOG +++ b/CHANGELOG @@ -1,3 +1,23 @@ +======== +4.4.0 +======== +---------------- +New Features +---------------- +* Implement a new Zero-Shot Text Classification for BERT annotator called `BertForZeroShotClassification` +* Implement a new ConvNextForImageClassification annotator +* Introducing BART Transformer for text-to-text generation tasks like translation and summarization +* Set custom entity name in Data2Chunk via `setEntityName` param +* Add a new `nerHasNoSchema` param for NerConverter when labels coming from NerDLMOdel and NerCrfModel don't have any schema +---------------- +Bug Fixes & Enhancements +---------------- +* Fix loading `WordEmbeddingsModel` bug when loading a model from S3 via `cache_folder` config +* Fix `WordEmbeddingsModel` bug failing when it's used with `setEnableInMemoryStorage` set to `True` and LightPipeline +* Remove deprecated parameter enablePatternRegex from EntityRulerApproach & EntityRulerModel +* Deprecate Python 3.6 + + ======== 4.3.2 ======== diff --git a/examples/python/annotation/text/english/zero-shot text classification/Zero_Shot_Text_Classification_by_BERT.ipynb b/examples/python/annotation/text/english/zero-shot text classification/Zero_Shot_Text_Classification_by_BERT.ipynb new file mode 100644 index 000000000000..a4a5feba2151 --- /dev/null +++ b/examples/python/annotation/text/english/zero-shot text classification/Zero_Shot_Text_Classification_by_BERT.ipynb @@ -0,0 +1,445 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Zero-Shot Learning in Modern NLP\n", + "### State-of-the-art NLP models for text classification without annotated data\n", + "\n", + ">Natural language processing is a very exciting field right now. In recent years, the community has begun to figure out some pretty effective methods of learning from the enormous amounts of unlabeled data available on the internet. The success of transfer learning from unsupervised models has allowed us to surpass virtually all existing benchmarks on downstream supervised learning tasks. As we continue to develop new model architectures and unsupervised learning objectives, \"state of the art\" continues to be a rapidly moving target for many tasks where large amounts of labeled data are available.\n", + "\n" + ], + "metadata": { + "id": "hQEd60qjN_0t" + } + }, + { + "cell_type": "code", + "source": [ + "# This is only to setup PySpark and Spark NLP on Colab\n", + "!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash" + ], + "metadata": { + "id": "z5EXpkAOFRu9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import sparknlp\n", + "spark = sparknlp.start()" + ], + "metadata": { + "id": "5fyaA76PFZA6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sparknlp.base import *\n", + "from sparknlp.annotator import *\n", + "from pyspark.ml import Pipeline, PipelineModel\n", + "\n", + "document_assembler = DocumentAssembler() \\\n", + " .setInputCol(\"text\") \\\n", + " .setOutputCol(\"document\")\n", + "\n", + "tokenizer = Tokenizer().setInputCols(\"document\").setOutputCol(\"token\")\n", + "\n", + "zero_shot_classifier = BertForZeroShotClassification \\\n", + " .pretrained() \\\n", + " .setInputCols([\"document\", \"token\"]) \\\n", + " .setOutputCol(\"class\") \\\n", + " .setCandidateLabels([\"urgent\", \"mobile\", \"travel\", \"movie\", \"music\", \"sport\", \"weather\", \"technology\"])\n", + "\n", + "pipeline = Pipeline(stages=[\n", + " document_assembler,\n", + " tokenizer,\n", + " zero_shot_classifier\n", + "])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_N2AoLRkFYdZ", + "outputId": "f1a1e08f-d20d-4efa-e8d4-f38914ab9c69" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "bert_base_cased_zero_shot_classifier_xnli download started this may take some time.\n", + "Approximate size to download 387.7 MB\n", + "[OK!]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "text = [[\"I have a problem with my iphone that needs to be resolved asap!!\"],\n", + " [\"Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\"],\n", + " [\"I have a phone and I love it!\"],\n", + " [\"I really want to visit Germany and I am planning to go there next year.\"],\n", + " [\"Let's watch some movies tonight! I am in the mood for a horror movie.\"],\n", + " [\"Have you watched the match yesterday? It was a great game!\"],\n", + " [\"We need to harry up and get to the airport. We are going to miss our flight!\"]]\n", + "\n", + "# create a DataFrame in PySpark\n", + "inputDataset = spark.createDataFrame(text, [\"text\"])\n", + "model = pipeline.fit(inputDataset)\n", + "predictionDF = model.transform(inputDataset)" + ], + "metadata": { + "id": "5LKrJRuFNQNs" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "predictionDF.select(\"document.result\", \"class.result\").show(10, False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BCIiOgJtN83v", + "outputId": "319bc31a-384f-4793-80b3-8e8aa08e159e" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+----------------------------------------------------------------------------------------------------------------+--------+\n", + "|result |result |\n", + "+----------------------------------------------------------------------------------------------------------------+--------+\n", + "|[I have a problem with my iphone that needs to be resolved asap!!] |[mobile]|\n", + "|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[mobile]|\n", + "|[I have a phone and I love it!] |[mobile]|\n", + "|[I really want to visit Germany and I am planning to go there next year.] |[travel]|\n", + "|[Let's watch some movies tonight! I am in the mood for a horror movie.] |[movie] |\n", + "|[Have you watched the match yesterday? It was a great game!] |[sport] |\n", + "|[We need to harry up and get to the airport. We are going to miss our flight!] |[urgent]|\n", + "+----------------------------------------------------------------------------------------------------------------+--------+\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "sample_text = \"Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\"\n", + "\n", + "light_pipeline = LightPipeline(model)\n", + "\n", + "results = light_pipeline.annotate(sample_text)\n", + "results\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MaRW4KPGNkjc", + "outputId": "49c0e99b-da4f-42e2-ba98-960bcdc22cdd" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'document': ['Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.'],\n", + " 'token': ['Last',\n", + " 'week',\n", + " 'I',\n", + " 'upgraded',\n", + " 'my',\n", + " 'iOS',\n", + " 'version',\n", + " 'and',\n", + " 'ever',\n", + " 'since',\n", + " 'then',\n", + " 'my',\n", + " 'phone',\n", + " 'has',\n", + " 'been',\n", + " 'overheating',\n", + " 'whenever',\n", + " 'I',\n", + " 'use',\n", + " 'your',\n", + " 'app',\n", + " '.'],\n", + " 'class': ['mobile']}" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "for tx in text:\n", + " res = light_pipeline.annotate(tx[0])\n", + " print(f\"document: {res['document']} prediction: {res['class']}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Vbg44saZRtnw", + "outputId": "6d16b175-6014-45a3-b7cb-311b840deb72" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "document: ['I have a problem with my iphone that needs to be resolved asap!!'] prediction: ['mobile']\n", + "document: ['Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.'] prediction: ['mobile']\n", + "document: ['I have a phone and I love it!'] prediction: ['mobile']\n", + "document: ['I really want to visit Germany and I am planning to go there next year.'] prediction: ['travel']\n", + "document: [\"Let's watch some movies tonight! I am in the mood for a horror movie.\"] prediction: ['movie']\n", + "document: ['Have you watched the match yesterday? It was a great game!'] prediction: ['sport']\n", + "document: ['We need to harry up and get to the airport. We are going to miss our flight!'] prediction: ['urgent']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "Ss4pE3i4S_SU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Multi Label vs. Multi Class\n", + "\n", + "We can use `activation` parameter to set whether or not the result should be multi-class (the sum of all probabilities is `1.0`) or multi-label (each label has a probability between `0.0` to `1.0`)\n", + "\n", + "- multi-class: `softmax` (default)\n", + "- multi-label: `sigmoid`" + ], + "metadata": { + "id": "RfQrLMxATYyY" + } + }, + { + "cell_type": "code", + "source": [ + "zero_shot_classifier\\\n", + " .setCandidateLabels([\"urgent\", \"mobile\", \"travel\", \"movie\", \"music\", \"sport\", \"weather\", \"technology\"])\\\n", + " .setActivation(\"sigmoid\")\n", + "\n", + "pipeline = Pipeline(stages=[\n", + " document_assembler,\n", + " tokenizer,\n", + " zero_shot_classifier\n", + "])" + ], + "metadata": { + "id": "zHB6w1-dTaQC" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "text = [[\"I have a problem with my iphone that needs to be resolved asap!!\"],\n", + " [\"Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\"],\n", + " [\"I have a phone and I love it!\"],\n", + " [\"I really want to visit Germany and I am planning to go there next year.\"],\n", + " [\"Let's watch some movies tonight! I am in the mood for a horror movie.\"],\n", + " [\"Have you watched the match yesterday? It was a great game!\"],\n", + " [\"We need to harry up and get to the airport. We are going to miss our flight!\"]]\n", + "\n", + "# create a DataFrame in PySpark\n", + "inputDataset = spark.createDataFrame(text, [\"text\"])\n", + "model = pipeline.fit(inputDataset)\n", + "predictionDF = model.transform(inputDataset)" + ], + "metadata": { + "id": "F0QgxciuUG4V" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "predictionDF.select(\"document.result\", \"class.result\").show(10, False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VkvAGrbAUJQn", + "outputId": "902a43eb-8c3e-4d63-9273-455460a7e6a4" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+----------------------------------------------------------------------------------------------------------------+-----------------------------------+\n", + "|result |result |\n", + "+----------------------------------------------------------------------------------------------------------------+-----------------------------------+\n", + "|[I have a problem with my iphone that needs to be resolved asap!!] |[urgent, mobile, movie, technology]|\n", + "|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[urgent, technology] |\n", + "|[I have a phone and I love it!] |[mobile] |\n", + "|[I really want to visit Germany and I am planning to go there next year.] |[travel] |\n", + "|[Let's watch some movies tonight! I am in the mood for a horror movie.] |[movie] |\n", + "|[Have you watched the match yesterday? It was a great game!] |[sport] |\n", + "|[We need to harry up and get to the airport. We are going to miss our flight!] |[urgent, travel] |\n", + "+----------------------------------------------------------------------------------------------------------------+-----------------------------------+\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# check the scores\n", + "predictionDF.select(\"class.metadata\").show(10, False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DAcVGaBVUJ4a", + "outputId": "7ffe547b-df85-478f-b214-5e8dde8d5527" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "|metadata |\n", + "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "|[{urgent -> 0.94203824, music -> 0.273361, movie -> 0.8155173, technology -> 0.9432713, travel -> 0.19155306, sport -> 7.5020565E-4, weather -> 0.012860995, mobile -> 0.81718224, sentence -> 0}, {urgent -> 0.94203824, music -> 0.273361, movie -> 0.8155173, technology -> 0.9432713, travel -> 0.19155306, sport -> 7.5020565E-4, weather -> 0.012860995, mobile -> 0.81718224, sentence -> 0}, {urgent -> 0.94203824, music -> 0.273361, movie -> 0.8155173, technology -> 0.9432713, travel -> 0.19155306, sport -> 7.5020565E-4, weather -> 0.012860995, mobile -> 0.81718224, sentence -> 0}, {urgent -> 0.94203824, music -> 0.273361, movie -> 0.8155173, technology -> 0.9432713, travel -> 0.19155306, sport -> 7.5020565E-4, weather -> 0.012860995, mobile -> 0.81718224, sentence -> 0}]|\n", + "|[{urgent -> 0.5989145, music -> 0.009265149, movie -> 0.004853843, technology -> 0.83162993, travel -> 0.2232338, sport -> 0.0011306163, weather -> 0.0057875705, mobile -> 0.39459854, sentence -> 0}, {urgent -> 0.5989145, music -> 0.009265149, movie -> 0.004853843, technology -> 0.83162993, travel -> 0.2232338, sport -> 0.0011306163, weather -> 0.0057875705, mobile -> 0.39459854, sentence -> 0}] |\n", + "|[{urgent -> 0.0024619207, music -> 0.030493928, movie -> 0.10384921, technology -> 0.01819679, travel -> 0.025316363, sport -> 5.448147E-4, weather -> 5.9901236E-4, mobile -> 0.822738, sentence -> 0}] |\n", + "|[{urgent -> 0.0074373055, music -> 0.06391685, movie -> 0.009793872, technology -> 0.08052815, travel -> 0.95946944, sport -> 0.0033297893, weather -> 0.11855726, mobile -> 0.011235076, sentence -> 0}] |\n", + "|[{urgent -> 0.0032734834, music -> 0.00897034, movie -> 0.7762647, technology -> 2.2468095E-4, travel -> 0.0016853365, sport -> 5.059199E-4, weather -> 7.164882E-4, mobile -> 0.0029863138, sentence -> 0}] |\n", + "|[{urgent -> 6.5108406E-4, music -> 0.0819624, movie -> 0.07504165, technology -> 0.0013726762, travel -> 0.0011811191, sport -> 0.9228904, weather -> 0.003503337, mobile -> 0.0048036124, sentence -> 0}] |\n", + "|[{urgent -> 0.9887839, music -> 0.13705872, movie -> 0.3061645, technology -> 0.008889678, travel -> 0.8829653, sport -> 0.0101994965, weather -> 0.105074614, mobile -> 0.011163117, sentence -> 0}, {urgent -> 0.9887839, music -> 0.13705872, movie -> 0.3061645, technology -> 0.008889678, travel -> 0.8829653, sport -> 0.0101994965, weather -> 0.105074614, mobile -> 0.011163117, sentence -> 0}] |\n", + "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Zero-Shot Learning (ZSL)\n", + "> Traditionally, zero-shot learning (ZSL) most often referred to a fairly specific type of task: learn a classifier on one set of labels and then evaluate on a different set of labels that the classifier has never seen before. Recently, especially in NLP, it's been used much more broadly to mean get a model to do something that it wasn't explicitly trained to do. A well-known example of this is in the [GPT-2 paper](https://pdfs.semanticscholar.org/9405/cc0d6169988371b2755e573cc28650d14dfe.pdf) where the authors evaluate a language model on downstream tasks like machine translation without fine-tuning on these tasks directly.\n", + "\n", + "Let's see how easy it is to just use any set of lables our trained model has never seen via `setCandidateLabels()` param:" + ], + "metadata": { + "id": "ICDQOrwPOeu4" + } + }, + { + "cell_type": "code", + "source": [ + "zero_shot_classifier\\\n", + " .setCandidateLabels([\"space & cosmos\", \"scientific discovery\", \"microbiology\", \"robots\", \"archeology\", \"politics\"])\\\n", + " .setActivation(\"sigmoid\") # multi-label\n", + "\n", + "pipeline = Pipeline(stages=[\n", + " document_assembler,\n", + " tokenizer,\n", + " zero_shot_classifier\n", + "])\n", + "\n", + "input_text3 = [\n", + " [\"Learn about the presidential election process, including the Electoral College, caucuses and primaries, and the national conventions.\"],\n", + " [\"\"\"In a new book, Sean Carroll brings together physics and philosophy while advocating for \"poetic naturalism.\" Ramin Skibba, Contributor. Space ...\"\"\"],\n", + " [\"Who are you voting for in 2024?\"]]\n", + "\n", + "# create a DataFrame in PySpark\n", + "inputDataset = spark.createDataFrame(input_text3, [\"text\"])\n", + "model = pipeline.fit(inputDataset)\n", + "predictionDF = model.transform(inputDataset)\n", + "\n", + "predictionDF.select(\"document.result\", \"class.result\").show(3, False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JnVfw4T9PWyb", + "outputId": "ee01e459-2a07-4adb-8cf0-625465d4d4c7" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+\n", + "|result |result |\n", + "+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+\n", + "|[Learn about the presidential election process, including the Electoral College, caucuses and primaries, and the national conventions.] |[politics] |\n", + "|[In a new book, Sean Carroll brings together physics and philosophy while advocating for \"poetic naturalism.\" Ramin Skibba, Contributor. Space ...]|[space & cosmos, scientific discovery]|\n", + "|[Who are you voting for in 2024?] |[politics] |\n", + "+---------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "pJK49gj_RqOQ" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 23a055904ef75a0bb7b2875798d4c61c4a3b317b Mon Sep 17 00:00:00 2001 From: github-actions Date: Mon, 10 Apr 2023 16:12:33 +0000 Subject: [PATCH 25/26] Update Scala and Python APIs --- docs/api/com/index.html | 8 +- .../johnsnowlabs/client/CredentialParams.html | 8 +- .../client/aws/AWSAnonymousCredentials.html | 8 +- .../client/aws/AWSBasicCredentials.html | 8 +- .../client/aws/AWSCredentialsProvider.html | 8 +- .../johnsnowlabs/client/aws/AWSGateway.html | 14 +- 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.../nlp/annotators/audio/Wav2Vec2ForCTC.scala | 3 +- .../dl/AlbertForQuestionAnswering.scala | 4 +- .../dl/AlbertForSequenceClassification.scala | 4 +- .../dl/AlbertForTokenClassification.scala | 4 +- .../dl/BertForQuestionAnswering.scala | 4 +- .../dl/BertForSequenceClassification.scala | 4 +- .../dl/BertForTokenClassification.scala | 4 +- .../dl/BertForZeroShotClassification.scala | 4 +- .../dl/CamemBertForQuestionAnswering.scala | 4 +- .../CamemBertForSequenceClassification.scala | 4 +- .../dl/CamemBertForTokenClassification.scala | 4 +- .../dl/DeBertaForQuestionAnswering.scala | 4 +- .../dl/DeBertaForSequenceClassification.scala | 4 +- .../dl/DeBertaForTokenClassification.scala | 4 +- .../dl/DistilBertForQuestionAnswering.scala | 4 +- .../DistilBertForSequenceClassification.scala | 4 +- .../dl/DistilBertForTokenClassification.scala | 4 +- .../dl/LongformerForQuestionAnswering.scala | 4 +- .../LongformerForSequenceClassification.scala | 4 +- .../dl/LongformerForTokenClassification.scala | 4 +- .../dl/RoBertaForQuestionAnswering.scala | 4 +- .../dl/RoBertaForSequenceClassification.scala | 4 +- .../dl/RoBertaForTokenClassification.scala | 4 +- .../dl/TapasForQuestionAnswering.scala | 4 +- .../dl/XlmRoBertaForQuestionAnswering.scala | 4 +- .../XlmRoBertaForSequenceClassification.scala | 4 +- .../dl/XlmRoBertaForTokenClassification.scala | 4 +- .../dl/XlnetForSequenceClassification.scala | 4 +- .../dl/XlnetForTokenClassification.scala | 4 +- .../nlp/annotators/ner/crf/NerCrfModel.scala | 3 +- .../nlp/annotators/ner/dl/NerDLModel.scala | 4 +- .../nlp/embeddings/AlbertEmbeddings.scala | 4 +- .../nlp/embeddings/BertEmbeddings.scala | 4 +- .../embeddings/BertSentenceEmbeddings.scala | 4 +- .../nlp/embeddings/CamemBertEmbeddings.scala | 4 +- .../nlp/embeddings/DeBertaEmbeddings.scala | 4 +- .../nlp/embeddings/DistilBertEmbeddings.scala | 7 +- .../nlp/embeddings/Doc2VecModel.scala | 3 +- .../nlp/embeddings/LongformerEmbeddings.scala | 7 +- .../nlp/embeddings/RoBertaEmbeddings.scala | 4 +- .../RoBertaSentenceEmbeddings.scala | 4 +- .../embeddings/UniversalSentenceEncoder.scala | 4 +- .../nlp/embeddings/Word2VecModel.scala | 3 +- .../nlp/embeddings/WordEmbeddings.scala | 4 +- .../nlp/embeddings/WordEmbeddingsModel.scala | 4 +- .../nlp/embeddings/XlmRoBertaEmbeddings.scala | 4 +- .../XlmRoBertaSentenceEmbeddings.scala | 4 +- .../nlp/embeddings/XlnetEmbeddings.scala | 4 +- .../nlp/pretrained/PretrainedPipeline.scala | 7 +- 1337 files changed, 53272 insertions(+), 10275 deletions(-) create mode 100644 docs/api/com/johnsnowlabs/ml/ai/util/Generation/Generate.html create mode 100644 docs/api/com/johnsnowlabs/ml/ai/util/Generation/Logit/Logit.html create mode 100644 docs/api/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/LogitProcessor.html create mode 100644 docs/api/com/johnsnowlabs/ml/ai/util/Generation/Logit/LogitProcess/MinLengthLogitProcessor.html create mode 100644 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a/docs/api/com/index.html +++ b/docs/api/com/index.html @@ -3,9 +3,9 @@ - Spark NLP 4.3.2 ScalaDoc - com - - + Spark NLP 4.4.0 ScalaDoc - com + + @@ -28,7 +28,7 @@
  • @@ -815,7 +815,7 @@

    Type Members

    in 2019. It is a model trained on 138GB of French text.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = CamemBertEmbeddings.pretrained()
   .setInputCols("token", "document")
   .setOutputCol("camembert_embeddings")

    The default model is "camembert_base", if no name is provided.

    For available pretrained models please see the -Models Hub.

    For extended examples of usage, see the +Models Hub.

    For extended examples of usage, see the Examples and the CamemBertEmbeddingsTestSpec. @@ -873,8 +873,8 @@

    Type Members

    |[0.02690504491329193,0.12104076147079468,0.012526623904705048,-0.031543646007...| |[0.05877285450696945,-0.08773420006036758,-0.06381352990865707,0.122621834278...| +--------------------------------------------------------------------------------+
    See also

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
  • @@ -1043,8 +1043,8 @@

    Type Members

    |[-0.04192575812339783,-0.5764210224151611,-0.3196685314178467,-0.527840495109...| |[0.15583214163780212,-0.1614152491092682,-0.28423872590065,-0.135491415858268...| +--------------------------------------------------------------------------------+
    See also

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
  • @@ -1066,8 +1066,7 @@

    Type Members

    over 95% of BERT's performances as measured on the GLUE language understanding benchmark.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = DistilBertEmbeddings.pretrained()
   .setInputCols("document", "token")
   .setOutputCol("embeddings")

    The default model is "distilbert_base_cased", if no name is provided. For available -pretrained models please see the -Models Hub.

    For extended examples of usage, see the +pretrained models please see the Models Hub.

    For extended examples of usage, see the Examples and the DistilBertEmbeddingsTestSpec. @@ -1139,8 +1138,8 @@

    Type Members

    for DistilBertEmbeddings with a token classification layer on top

    DistilBertForSequenceClassification for DistilBertEmbeddings with a sequence classification layer on top

    - Annotators Main Page for a list of - transformer based embeddings

     + Annotators Main Page for a list of transformer + based embeddings

  • @@ -1213,8 +1212,7 @@

    Type Members

    the original C implementation.

    This is the instantiated model of the Doc2VecApproach. For training your own model, please see the documentation of that class.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = Doc2VecModel.pretrained()
   .setInputCols("token")
-  .setOutputCol("embeddings")

    The default model is "doc2vec_gigaword_300", if no name is provided.

    For available pretrained models please see the -Models Hub.

    Sources :

    For the original C implementation, see https://code.google.com/p/word2vec/

    For the research paper, see + .setOutputCol("embeddings")

    The default model is "doc2vec_gigaword_300", if no name is provided.

    For available pretrained models please see the Models Hub.

    Sources :

    For the original C implementation, see https://code.google.com/p/word2vec/

    For the research paper, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

    Example

    import spark.implicits._
@@ -1278,7 +1276,7 @@ 
    Type Members
    
 that only perform embedding lookups. The use of an accelerator is recommended.
    Pretrained models can be loaded with pretrained of the companion object:
    val embeddings = ElmoEmbeddings.pretrained()
   .setInputCols("sentence", "token")
   .setOutputCol("elmo_embeddings")
    The default model is "elmo", if no name is provided.
    For available pretrained models please see the
-Models Hub.
    The pooling layer can be set with setPoolingLayer to the following values:
    • "word_emb": the character-based word representations with shape [batch_size,
+Models Hub.
      The pooling layer can be set with setPoolingLayer to the following values:
      • "word_emb": the character-based word representations with shape [batch_size,
     max_length, 512].
      • "lstm_outputs1": the first LSTM hidden state with shape [batch_size, max_length,
     1024].
      • "lstm_outputs2": the second LSTM hidden state with shape [batch_size, max_length,
     1024].
      • "elmo": the weighted sum of the 3 layers, where the weights are trainable. This tensor
@@ -1339,7 +1337,7 @@ 
        Type Members
        
 |[0.49932169914245605,-0.12706467509269714,0.30969417095184326,0.2643227577209...|
 |[-0.8871506452560425,-0.20039963722229004,-1.0601330995559692,0.0348707810044...|
 +--------------------------------------------------------------------------------+
    See also

    - Annotators Main Page for a list of other + Annotators Main Page for a list of other transformer based embeddings

  • @@ -1395,8 +1393,7 @@

    Type Members

    length up to 4,096.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = LongformerEmbeddings.pretrained()
   .setInputCols("document", "token")
   .setOutputCol("embeddings")

    The default model is "longformer_base_4096", if no name is provided. For available -pretrained models please see the -Models Hub.

    For some examples of usage, see +pretrained models please see the Models Hub.

    For some examples of usage, see LongformerEmbeddingsTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

    Paper Abstract:

    Transformer-based models are unable to process long sequences due to their self-attention @@ -1459,8 +1456,8 @@

    Type Members

    +--------------------------------------------------------------------------------+
    See also

    LongformerForTokenClassification for Longformer embeddings with a token classification layer on top

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings

  • @@ -1999,7 +1996,7 @@

    Type Members

    objective and training with much larger mini-batches and learning rates.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = RoBertaEmbeddings.pretrained()
   .setInputCols("document", "token")
   .setOutputCol("embeddings")

    The default model is "roberta_base", if no name is provided. For available pretrained models -please see the Models Hub.

    For extended examples of usage, see the +please see the Models Hub.

    For extended examples of usage, see the Examples and the RoBertaEmbeddingsTestSpec. @@ -2067,8 +2064,8 @@

    Type Members

    RoBertaSentenceEmbeddings for sentence-level embeddings

    RoBertaForTokenClassification For RoBerta embeddings with a token classification layer on top

    - Annotators Main Page for a list of - transformer based embeddings

     + Annotators Main Page for a list of transformer + based embeddings

  • @@ -2092,7 +2089,7 @@

    Type Members

    objective and training with much larger mini-batches and learning rates.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = RoBertaSentenceEmbeddings.pretrained()
   .setInputCols("sentence")
   .setOutputCol("sentence_embeddings")

    The default model is "sent_roberta_base", if no name is provided. For available pretrained -models please see the Models Hub.

    To see which models are compatible and how to import them see +models please see the Models Hub.

    To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see RoBertaEmbeddingsTestSpec.

    Paper Abstract:

    Language model pretraining has led to significant performance gains but careful comparison @@ -2158,8 +2155,8 @@

    Type Members

    |[0.22409197688102722,-0.4312366545200348,0.1401449590921402,0.356410235166549...| +--------------------------------------------------------------------------------+
    See also

    RoBertaEmbeddings for token-level embeddings

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
  • @@ -2251,7 +2248,7 @@

    Type Members

    text classification, semantic similarity, clustering and other natural language tasks.

    Pretrained models can be loaded with pretrained of the companion object:

    val useEmbeddings = UniversalSentenceEncoder.pretrained()
   .setInputCols("sentence")
   .setOutputCol("sentence_embeddings")

    The default model is "tfhub_use", if no name is provided. For available pretrained models -please see the Models Hub.

    For extended examples of usage, see the +please see the Models Hub.

    For extended examples of usage, see the Examples and the UniversalSentenceEncoderTestSpec.

    References:

    Universal Sentence Encoder

    https://tfhub.dev/google/universal-sentence-encoder/2

    Paper abstract:

    We present models for encoding sentences into embedding vectors that specifically target @@ -2308,8 +2305,8 @@

    Type Members

    +--------------------------------------------------------------------------------+ |[0.04616805538535118,0.022307956591248512,-0.044395286589860916,-0.0016493503...| +--------------------------------------------------------------------------------+
    See also

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
  • @@ -2382,8 +2379,7 @@

    Type Members

    the original C implementation.

    This is the instantiated model of the Word2VecApproach. For training your own model, please see the documentation of that class.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = Word2VecModel.pretrained()
   .setInputCols("token")
-  .setOutputCol("embeddings")

    The default model is "word2vec_gigaword_300", if no name is provided.

    For available pretrained models please see the -Models Hub.

    Sources :

    For the original C implementation, see https://code.google.com/p/word2vec/

    For the research paper, see + .setOutputCol("embeddings")

    The default model is "word2vec_gigaword_300", if no name is provided.

    For available pretrained models please see the Models Hub.

    Sources :

    For the original C implementation, see https://code.google.com/p/word2vec/

    For the research paper, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

    Example

    import spark.implicits._
@@ -2507,8 +2503,8 @@ 
    Type Members
    
 |[0.9840458631515503,0.7599489092826843,0.9417727589607239,0.8624503016471863]     |
 +----------------------------------------------------------------------------------+
    See also

    SentenceEmbeddings to combine embeddings into a sentence-level representation

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
  • @@ -2527,7 +2523,7 @@

    Type Members

    Word Embeddings lookup annotator that maps tokens to vectors

    Word Embeddings lookup annotator that maps tokens to vectors

    This is the instantiated model of WordEmbeddings.

    Pretrained models can be loaded with pretrained of the companion object:

    val embeddings = WordEmbeddingsModel.pretrained()
     .setInputCols("document", "token")
     .setOutputCol("embeddings")

    The default model is "glove_100d", if no name is provided. For available pretrained models -please see the Models Hub.

    There are also two convenient functions to retrieve the embeddings coverage with respect to +please see the Models Hub.

    There are also two convenient functions to retrieve the embeddings coverage with respect to the transformed dataset:

    • withCoverageColumn(dataset, embeddingsCol, outputCol): Adds a custom column with word coverage stats for the embedded field: (coveredWords, totalWords, coveragePercentage). This creates a new column with statistics for each row.
    val wordsCoverage = WordEmbeddingsModel.withCoverageColumn(resultDF, "embeddings", "cov_embeddings")
@@ -2589,8 +2585,8 @@ 
    Type Members
    
 |[-0.3397899866104126,0.20940999686717987,0.46347999572753906,-0.6479200124740...|
 +--------------------------------------------------------------------------------+
    See also

    SentenceEmbeddings to combine embeddings into a sentence-level representation

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
  • @@ -2650,7 +2646,7 @@

    Type Members

    .setInputCols("document", "token") .setOutputCol("embeddings")

    The default model is "xlm_roberta_base", default language is "xx" (meaning multi-lingual), if no values are provided. For available pretrained models please see the -Models Hub.

    For extended examples of usage, see the +Models Hub.

    For extended examples of usage, see the Examples and the XlmRoBertaEmbeddingsTestSpec. @@ -2721,8 +2717,8 @@

    Type Members

    XlmRoBertaSentenceEmbeddings for sentence-level embeddings

    XlmRoBertaForTokenClassification For XlmRoBerta embeddings with a token classification layer on top

    - Annotators Main Page for a list of - transformer based embeddings

     + Annotators Main Page for a list of transformer + based embeddings

  • @@ -2747,7 +2743,7 @@

    Type Members

    .setInputCols("document") .setOutputCol("sentence_embeddings")

    The default model is "sent_xlm_roberta_base", default language is "xx" (meaning multi-lingual), if no values are provided. For available pretrained models please see the -Models Hub.

    To see which models are compatible and how to import them see +Models Hub.

    To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see XlmRoBertaSentenceEmbeddingsTestSpec.

    Paper Abstract:

    This paper shows that pretraining multilingual language models at scale leads to significant @@ -2816,8 +2812,8 @@

    Type Members

    |[0.004710011184215546,-0.022148698568344116,0.011723337695002556,-0.013356896...| +--------------------------------------------------------------------------------+
    See also

    XlmRoBertaEmbeddings for token-level embeddings

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
  • @@ -2909,8 +2905,8 @@

    Type Members

    +--------------------------------------------------------------------------------+
    See also

    XlnetForTokenClassification For Xlnet embeddings with a token classification layer on top

    - Annotators Main Page for a list of - transformer based embeddings

    + Annotators Main Page for a list of transformer + based embeddings
    • diff --git a/docs/api/com/johnsnowlabs/nlp/functions$$EachAnnotations.html b/docs/api/com/johnsnowlabs/nlp/functions$$EachAnnotations.html index 1d5230fd5079..f98a4e148dd7 100644 --- a/docs/api/com/johnsnowlabs/nlp/functions$$EachAnnotations.html +++ b/docs/api/com/johnsnowlabs/nlp/functions$$EachAnnotations.html @@ -3,9 +3,9 @@ - Spark NLP 4.3.2 ScalaDoc - com.johnsnowlabs.nlp.functions.EachAnnotations - - + Spark NLP 4.4.0 ScalaDoc - com.johnsnowlabs.nlp.functions.EachAnnotations + + @@ -28,7 +28,7 @@