Models hub

docs/_posts/ahmedlone127/2023-05-04-roberta_base_zero_shot_classifier_nli_en.md

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+---
+layout: model
+title: RoBertaZero-Shot Classification Base roberta_base_zero_shot_classifier_nli
+author: John Snow Labs
+name: roberta_base_zero_shot_classifier_nli
+date: 2023-05-04
+tags: [en, open_source, tensorflow]
+task: Zero-Shot Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: [3.0]
+supported: true
+engine: tensorflow
+annotator: RoBertaForZeroShotClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on NLI by using Roberta Base  model.
+
+RoBertaForZeroShotClassificationusing a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of RoBertaForZeroShotClassification 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.
+
+We used TFRobertaForSequenceClassification to train this model and used RoBertaForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_zero_shot_classifier_nli_en_4.4.2_3.0_1683228241365.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_zero_shot_classifier_nli_en_4.4.2_3.0_1683228241365.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler() \
+.setInputCol('text') \
+.setOutputCol('document')
+
+tokenizer = Tokenizer() \
+.setInputCols(['document']) \
+.setOutputCol('token')
+
+zeroShotClassifier = RobertaForSequenceClassification \
+.pretrained('roberta_base_zero_shot_classifier_nli', 'en') \
+.setInputCols(['token', 'document']) \
+.setOutputCol('class') \
+.setCaseSensitive(True) \
+.setMaxSentenceLength(512) \
+.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"])
+
+pipeline = Pipeline(stages=[
+document_assembler,
+tokenizer,
+zeroShotClassifier
+])
+
+example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text")
+result = pipeline.fit(example).transform(example)
+```
+```scala
+val document_assembler = DocumentAssembler()
+.setInputCol("text")
+.setOutputCol("document")
+
+val tokenizer = Tokenizer()
+.setInputCols("document")
+.setOutputCol("token")
+
+val zeroShotClassifier = RobertaForSequenceClassification.pretrained("roberta_base_zero_shot_classifier_nli", "en")
+.setInputCols("document", "token")
+.setOutputCol("class")
+.setCaseSensitive(true)
+.setMaxSentenceLength(512)
+.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"))
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
+val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text")
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|roberta_base_zero_shot_classifier_nli|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[multi_class]|
+|Language:|en|
+|Size:|466.4 MB|
+|Case sensitive:|true|

docs/_posts/ahmedlone127/2023-05-19-dependency_parse_en.md

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+---
+layout: model
+title: Typed Dependency Parsing pipeline for English
+author: John Snow Labs
+name: dependency_parse
+date: 2023-05-19
+tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source]
+task: Dependency Parser
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Typed Dependency parser, trained on the on the CONLL dataset. 
+
+Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.0_1684522392175.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.0_1684522392175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('dependency_parse', lang = 'en')
+annotations =  pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("dependency_parse", lang = "en")
+val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0)
+
+```
+
+{:.nlu-block}
+```python
+
+nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence")
+
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('dependency_parse', lang = 'en')
+annotations =  pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("dependency_parse", lang = "en")
+val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0)
+```
+
+{:.nlu-block}
+```python
+nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+|result                                                                           |result                                                  |
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]|
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|dependency_parse|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|23.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- PerceptronModel
+- DependencyParserModel
+- TypedDependencyParserModel

docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md

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+---
+layout: model
+title: Match Pattern
+author: John Snow Labs
+name: match_pattern
+date: 2023-05-19
+tags: [en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens  .
+	It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.0_1684521353408.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.0_1684521353408.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
+	result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
+	result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|match_pattern|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|29.1 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- RegexMatcherModel