tutorials/Certification_Trainings/Public/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"JblJe8DTlGpl"},"source":["![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)"]},{"cell_type":"markdown","metadata":{"id":"_Hmv_AJA1rnu"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Public/2.Text_Preprocessing_with_SparkNLP_Annotators_Transformers.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"H_SG0VCrix5p"},"source":["**Note** Read this article if you want to understand the basic concepts in Spark NLP.\n","\n","[Introduction to Spark NLP: Foundations and Basic Components](https://towardsdatascience.com/introduction-to-spark-nlp-foundations-and-basic-components-part-i-c83b7629ed59)\n"]},{"cell_type":"markdown","metadata":{"id":"MfkkKkbVF309"},"source":["## **0. Colab Setup**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MS62MFbPfPRC"},"outputs":[],"source":["!pip install -q pyspark==3.4.1  spark-nlp==5.3.2"]},{"cell_type":"markdown","metadata":{"id":"SS07N80gEtSt"},"source":["### **1. Annotators and Transformer Concepts**"]},{"cell_type":"markdown","metadata":{"id":"g3_ic8K7E0sy"},"source":["In Spark NLP, all Annotators are either Estimators or Transformers as we see in Spark ML. An Estimator in Spark ML is an algorithm which can be fit on a DataFrame to produce a Transformer. E.g., a learning algorithm is an Estimator which trains on a DataFrame and produces a model. A Transformer is an algorithm which can transform one DataFrame into another DataFrame. E.g., an ML model is a Transformer that transforms a DataFrame with features into a DataFrame with predictions.\n","In Spark NLP, there are two types of annotators: AnnotatorApproach and AnnotatorModel\n","AnnotatorApproach extends Estimators from Spark ML, which are meant to be trained through fit(), and AnnotatorModel extends Transformers which are meant to transform data frames through transform().\n","Some of Spark NLP annotators have a Model suffix and some do not. The model suffix is explicitly stated when the annotator is the result of a training process. Some annotators, such as Tokenizer are transformers but do not contain the suffix Model since they are not trained, annotators. Model annotators have a pre-trained() on its static object, to retrieve the public pre-trained version of a model.\n","Long story short, if it trains on a DataFrame and produces a model, it’s an AnnotatorApproach; and if it transforms one DataFrame into another DataFrame through some models, it’s an AnnotatorModel (e.g. WordEmbeddingsModel) and it doesn’t take Model suffix if it doesn’t rely on a pre-trained annotator while transforming a DataFrame (e.g. Tokenizer)."]},{"cell_type":"markdown","metadata":{"id":"x6SaPXwtFBM-"},"source":["By convention, there are three possible names:\n","\n","**Approach** — Trainable annotator\n","\n","**Model** — Trained annotator\n","\n","**nothing** — Either a non-trainable annotator with pre-processing\n","step or shorthand for a model\n","\n","So for example, Stemmer doesn’t say Approach nor Model, however, it is a Model. On the other hand, Tokenizer doesn’t say Approach nor Model, but it has a TokenizerModel(). Because it is not “training” anything, but it is doing some preprocessing before converting into a Model.\n","When in doubt, please refer to official documentation and API reference.\n","Even though we will do many hands-on practices in the following articles, let us give you a glimpse to let you understand the difference between AnnotatorApproach and AnnotatorModel.\n","As stated above, Tokenizer is an AnnotatorModel. So we need to call fit() and then transform()."]},{"cell_type":"markdown","metadata":{"id":"ALiQ2TsOFNyc"},"source":["Now let’s see how this can be done in Spark NLP using Annotators and Transformers. Assume that we have the following steps that need to be applied one by one on a data frame.\n","\n","- Split text into sentences\n","- Tokenize\n","- Normalize\n","- Get word embeddings"]},{"cell_type":"markdown","metadata":{"id":"WrsEK9Z84CBk"},"source":["![image.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"suLa96N7Fijt"},"source":["**What’s actually happening under the hood?**\n","\n","When we fit() on the pipeline with Spark data frame (df), its text column is fed into DocumentAssembler() transformer at first and then a new column “document” is created in Document type (AnnotatorType). As we mentioned before, this transformer is basically the initial entry point to Spark NLP for any Spark data frame. Then its document column is fed into SentenceDetector() (AnnotatorApproach) and the text is split into an array of sentences and a new column “sentences” in Document type is created. Then “sentences” column is fed into Tokenizer() (AnnotatorModel) and each sentence is tokenized and a new column “token” in Token type is created. And so on."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":254},"executionInfo":{"elapsed":51324,"status":"ok","timestamp":1712243899145,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"GkJzcfBBfSGM","outputId":"73569d07-1bab-4c94-bb92-199d577f0b00"},"outputs":[{"output_type":"stream","name":"stdout","text":["Spark NLP version:  5.3.2\n","Apache Spark version:  3.4.1\n"]},{"output_type":"execute_result","data":{"text/plain":["<pyspark.sql.session.SparkSession at 0x79015b684e80>"],"text/html":["\n","            <div>\n","                <p><b>SparkSession - in-memory</b></p>\n","                \n","        <div>\n","            <p><b>SparkContext</b></p>\n","\n","            <p><a href=\"http://fe26706476fd:4040\">Spark UI</a></p>\n","\n","            <dl>\n","              <dt>Version</dt>\n","                <dd><code>v3.4.1</code></dd>\n","              <dt>Master</dt>\n","                <dd><code>local[*]</code></dd>\n","              <dt>AppName</dt>\n","                <dd><code>Spark NLP</code></dd>\n","            </dl>\n","        </div>\n","        \n","            </div>\n","        "]},"metadata":{},"execution_count":2}],"source":["import sparknlp\n","\n","spark = sparknlp.start()\n","\n","print(\"Spark NLP version: \", sparknlp.version())\n","print(\"Apache Spark version: \", spark.version)\n","\n","spark"]},{"cell_type":"markdown","metadata":{"id":"Ab6V51l_nPyR"},"source":["### **Create Spark Dataframe**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6818,"status":"ok","timestamp":1712243905958,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"IrchhAp_fU5Q","outputId":"3d22cff6-ad03-4668-c03d-ef877465dbb2"},"outputs":[{"output_type":"stream","name":"stdout","text":["+------------------------------------------------+\n","|                                            text|\n","+------------------------------------------------+\n","|Peter Parker is a nice guy and lives in New York|\n","+------------------------------------------------+\n","\n"]}],"source":["text = 'Peter Parker is a nice guy and lives in New York'\n","\n","spark_df = spark.createDataFrame([[text]]).toDF(\"text\")\n","\n","spark_df.show(truncate=60)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":444,"status":"ok","timestamp":1712243906400,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"FZ8wiM3Ff4Ca","outputId":"886b256e-1b8a-4e93-b7aa-be34fea8bb23"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------------+\n","|                                                    text|\n","+--------------------------------------------------------+\n","|       Peter Parker is a nice guy and lives in New York.|\n","|Bruce Wayne is also a nice guy and lives in Gotham City.|\n","+--------------------------------------------------------+\n","\n"]}],"source":["from pyspark.sql.types import StringType, IntegerType\n","\n","# if you want to create a spark dataframe from a list of strings\n","\n","text_list = ['Peter Parker is a nice guy and lives in New York.', 'Bruce Wayne is also a nice guy and lives in Gotham City.']\n","\n","spark.createDataFrame(text_list, StringType()).toDF(\"text\").show(truncate=60)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":619,"status":"ok","timestamp":1712243907017,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"AzeiU6MjgG7p","outputId":"ab6cea95-62b6-4676-a893-3f19053281e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------------+\n","|                                                    text|\n","+--------------------------------------------------------+\n","|       Peter Parker is a nice guy and lives in New York.|\n","|Bruce Wayne is also a nice guy and lives in Gotham City.|\n","+--------------------------------------------------------+\n","\n"]}],"source":["from pyspark.sql import Row\n","\n","spark.createDataFrame(list(map(lambda x: Row(text=x), text_list))).show(truncate=60)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-NDuk41CgShq"},"outputs":[],"source":["!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/open-source-nlp/data/sample-sentences-en.txt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1712243907383,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"d4U9xSY5gUli","outputId":"49b6d725-c56f-4bfc-ec26-5d012cce3544"},"outputs":[{"output_type":"stream","name":"stdout","text":["Peter is a very good person.\n","My life in Russia is very interesting.\n","John and Peter are brothers. However they don't support each other that much.\n","Lucas Nogal Dunbercker is no longer happy. He has a good car though.\n","Europe is very culture rich. There are huge churches! and big houses!\n"]}],"source":["with open('./sample-sentences-en.txt') as f:\n","  print (f.read())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":437,"status":"ok","timestamp":1712243907819,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"4eR6AtO3gXRP","outputId":"6d01d903-1b58-45a8-d21e-907fac7d3564"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+\n","|text                                                                         |\n","+-----------------------------------------------------------------------------+\n","|Peter is a very good person.                                                 |\n","|My life in Russia is very interesting.                                       |\n","|John and Peter are brothers. However they don't support each other that much.|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |\n","|Europe is very culture rich. There are huge churches! and big houses!        |\n","+-----------------------------------------------------------------------------+\n","\n"]}],"source":["spark_df = spark.read.text('./sample-sentences-en.txt').toDF('text')\n","\n","spark_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243907819,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"dmyBzE7hgase","outputId":"7ada05af-b495-4ea4-d515-c0ecfaf0bb67"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+\n","|text                                                                         |\n","+-----------------------------------------------------------------------------+\n","|Peter is a very good person.                                                 |\n","|My life in Russia is very interesting.                                       |\n","|John and Peter are brothers. However they don't support each other that much.|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |\n","|Europe is very culture rich. There are huge churches! and big houses!        |\n","+-----------------------------------------------------------------------------+\n","\n"]}],"source":["spark_df.select('text').show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1008,"status":"ok","timestamp":1712243908825,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"cq7-l7tigdXZ","outputId":"eabc6b3a-5061-4333-b513-eff3838a8a66"},"outputs":[{"output_type":"stream","name":"stdout","text":["+------------------------------+------------------------------+\n","|                          path|                          text|\n","+------------------------------+------------------------------+\n","|file:/content/sample-senten...|Peter is a very good person...|\n","+------------------------------+------------------------------+\n","\n"]}],"source":["textFiles = spark.sparkContext.wholeTextFiles(\"./*.txt\",4)\n","\n","spark_df_folder = textFiles.toDF(schema=['path','text'])\n","\n","spark_df_folder.show(truncate=30)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243908825,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"dN6h00PvgfmX","outputId":"c93f6032-bf91-4787-d75e-1e713816e356"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(text=\"Peter is a very good person.\\r\\nMy life in Russia is very interesting.\\r\\nJohn and Peter are brothers. However they don't support each other that much.\\r\\nLucas Nogal Dunbercker is no longer happy. He has a good car though.\\r\\nEurope is very culture rich. There are huge churches! and big houses!\")]"]},"metadata":{},"execution_count":11}],"source":["spark_df_folder.select('text').take(1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1712243908825,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"uIgZGfeZgjQB","outputId":"2c136a4f-dc00-4f28-f87b-591b10c590a1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(text=\"Peter is a very good person.\\r\\nMy life in Russia is very interesting.\\r\\nJohn and Peter are brothers. However they don't support each other that much.\\r\\nLucas Nogal Dunbercker is no longer happy. He has a good car though.\\r\\nEurope is very culture rich. There are huge churches! and big houses!\")]"]},"metadata":{},"execution_count":12}],"source":["spark_df_folder.select('text').collect()"]},{"cell_type":"markdown","metadata":{"id":"ZTcOJCrieNQK"},"source":["### **Transformers**"]},{"cell_type":"markdown","metadata":{"id":"AwbTXq-keP7V"},"source":["What are we going to do if our DataFrame doesn’t have columns in those type? Here comes transformers. In Spark NLP, we have five different transformers that are mainly used for getting the data in or transform the data from one AnnotatorType to another. Here is the list of transformers:\n","\n","\n","- **DocumentAssembler**      : To get through the NLP process, we need to get raw data annotated. This is a special transformer that does this for us; it creates the first annotation of type Document which may be used by annotators down the road.\n","\n","- **TokenAssembler**      : This transformer reconstructs a Document type annotation from tokens, usually after these have been, lemmatized, normalized, spell checked, etc, to use this document annotation in further annotators.\n","- **Doc2Chunk**      : Converts DOCUMENT type annotations into CHUNK type with the contents of a chunkCol.\n","- **Chunk2Doc**      : Converts a CHUNK type column back into DOCUMENT. Useful when trying to re-tokenize or do further analysis on a CHUNK result.\n","- **Finisher**  : Once we have our NLP pipeline ready to go, we might want to use our annotation results somewhere else where it is easy to use. The Finisher outputs annotation(s) values into a string.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"tOBEa23Odr-C"},"source":["Each annotator accepts certain types of columns and outputs new columns in another type (we call this AnnotatorType).\n","\n","In Spark NLP, we have the following types:\n","\n",">`Document`, `token`, `chunk`, `pos`, `word_embeddings`, `date`, `entity`, `sentiment`, `named_entity`, `dependency`, `labeled_dependency`.\n","\n","That is, the DataFrame you have needs to have a column from one of these types if that column will be fed into an annotator; otherwise, you’d need to use one of the Spark NLP transformers."]},{"cell_type":"markdown","metadata":{"id":"ss59Lk4ULNRT"},"source":["##  **Document Assembler**"]},{"cell_type":"markdown","metadata":{"id":"74S30BktM8p-"},"source":["In Spark NLP, we have five different transformers that are mainly used for getting the data in or transform the data from one AnnotatorType to another."]},{"cell_type":"markdown","metadata":{"id":"TtyKgt_iM_5C"},"source":["That is, the DataFrame you have needs to have a column from one of these types if that column will be fed into an annotator; otherwise, you’d need to use one of the Spark NLP transformers. Here is the list of transformers: DocumentAssembler, TokenAssembler, Doc2Chunk, Chunk2Doc, and the Finisher.\n","\n","So, let’s start with DocumentAssembler(), an entry point to Spark NLP annotators."]},{"cell_type":"markdown","metadata":{"id":"GLiXAucCLWS3"},"source":["To get through the process in Spark NLP, we need to get raw data transformed into Document type at first.\n","\n","DocumentAssembler() is a special transformer that does this for us; it creates the first annotation of type Document which may be used by annotators down the road.\n","\n","DocumentAssembler() comes from sparknlp.base class and has the following settable parameters. See the full list here and the source code here.\n","\n","\n","- **setInputCol()**       : The name of the column that will be converted. We can specify only one column here. It can read either a String column or an Array.\n","\n","- **setOutputCol()** :The name of the column in Document type that is generated. We can specify only one column here. Default is '**document**'.\n","\n","- **setIdCol()**  : String type column with id information\n","\n","- **setMetadataCol()** : Map type column with metadata information.\n","\n","- **setCleanupMode()** : Cleaning up options\n","\n","\n","\n","possible values for setCleanupMode :\n","  ```\n","  disabled: Source kept as original. This is a default.\n","  inplace: removes new lines and tabs.\n","  inplace_full: removes new lines and tabs but also those which were converted to strings (i.e. \\n)\n","  shrink: removes new lines and tabs, plus merging multiple spaces and blank lines to a single space.\n","  shrink_full: remove new lines and tabs, including stringified values, plus shrinking spaces and blank lines.\n","  ```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":349,"status":"ok","timestamp":1712243909173,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"cH59_oKigjSm","outputId":"4d15bb27-b223-4de4-b9de-d8e9c1f078a5"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------+\n","|                text|\n","+--------------------+\n","|Peter is a very g...|\n","|My life in Russia...|\n","|John and Peter ar...|\n","|Lucas Nogal Dunbe...|\n","|Europe is very cu...|\n","+--------------------+\n","\n"]}],"source":["spark_df.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1712243909173,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"DzDyOHIbgjWC","outputId":"61d3ab12-1520-418a-920c-c210975374fc"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+\n","|text                                                                         |\n","+-----------------------------------------------------------------------------+\n","|Peter is a very good person.                                                 |\n","|My life in Russia is very interesting.                                       |\n","|John and Peter are brothers. However they don't support each other that much.|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |\n","|Europe is very culture rich. There are huge churches! and big houses!        |\n","+-----------------------------------------------------------------------------+\n","\n"]}],"source":["spark_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1501,"status":"ok","timestamp":1712243910672,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"ig8Rz7_WgoOu","outputId":"f539be96-8518-403c-f0ca-d111297bbc28"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+\n","|text                                                                         |document                                                                                                               |\n","+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+\n","|Peter is a very good person.                                                 |[{document, 0, 27, Peter is a very good person., {sentence -> 0}, []}]                                                 |\n","|My life in Russia is very interesting.                                       |[{document, 0, 37, My life in Russia is very interesting., {sentence -> 0}, []}]                                       |\n","|John and Peter are brothers. However they don't support each other that much.|[{document, 0, 76, John and Peter are brothers. However they don't support each other that much., {sentence -> 0}, []}]|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |[{document, 0, 67, Lucas Nogal Dunbercker is no longer happy. He has a good car though., {sentence -> 0}, []}]         |\n","|Europe is very culture rich. There are huge churches! and big houses!        |[{document, 0, 68, Europe is very culture rich. There are huge churches! and big houses!, {sentence -> 0}, []}]        |\n","+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["from sparknlp.base import *\n","\n","documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\\\n","    .setCleanupMode(\"shrink\")\n","\n","doc_df = documentAssembler.transform(spark_df)\n","\n","doc_df.show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"4gAyd6D0MSDF"},"source":["At first, we define DocumentAssembler with desired parameters and then transform the data frame with it. The most important point to pay attention to here is that you need to use a String or String[Array] type column in .setInputCol(). So it doesn’t have to be named as text. You just use the column name as it is."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1712243910672,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"2qL5abQVgrLJ","outputId":"1728293d-700a-4388-be6d-c60c81bae045"},"outputs":[{"output_type":"stream","name":"stdout","text":["root\n"," |-- text: string (nullable = true)\n"," |-- document: array (nullable = true)\n"," |    |-- element: struct (containsNull = true)\n"," |    |    |-- annotatorType: string (nullable = true)\n"," |    |    |-- begin: integer (nullable = false)\n"," |    |    |-- end: integer (nullable = false)\n"," |    |    |-- result: string (nullable = true)\n"," |    |    |-- metadata: map (nullable = true)\n"," |    |    |    |-- key: string\n"," |    |    |    |-- value: string (valueContainsNull = true)\n"," |    |    |-- embeddings: array (nullable = true)\n"," |    |    |    |-- element: float (containsNull = false)\n","\n"]}],"source":["doc_df.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":992,"status":"ok","timestamp":1712243911662,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"ImuDF1NVgtwT","outputId":"75107e72-ed17-4fd6-8cba-ff93dce79f8d"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------------------------------------------------------------------------+-----+----+\n","|result                                                                         |begin|end |\n","+-------------------------------------------------------------------------------+-----+----+\n","|[Peter is a very good person.]                                                 |[0]  |[27]|\n","|[My life in Russia is very interesting.]                                       |[0]  |[37]|\n","|[John and Peter are brothers. However they don't support each other that much.]|[0]  |[76]|\n","|[Lucas Nogal Dunbercker is no longer happy. He has a good car though.]         |[0]  |[67]|\n","|[Europe is very culture rich. There are huge churches! and big houses!]        |[0]  |[68]|\n","+-------------------------------------------------------------------------------+-----+----+\n","\n"]}],"source":["doc_df.select('document.result','document.begin','document.end').show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"1zb-mdaNMbS5"},"source":["The new column is in an array of struct type and has the parameters shown above. The annotators and transformers all come with universal metadata that would be filled down the road depending on the annotators being used. Unless you want to append other Spark NLP annotators to DocumentAssembler(), you don’t need to know what all these parameters mean for now. So we will talk about them in the following articles. You can access all these parameters with {column name}.{parameter name}.\n","\n","Let’s print out the first item’s result."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243911663,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"kE8R2UVRgvQY","outputId":"24ae82bf-6ade-4ac0-a437-5c267295d942"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['Peter is a very good person.'])]"]},"metadata":{},"execution_count":18}],"source":["doc_df.select(\"document.result\").take(1)"]},{"cell_type":"markdown","metadata":{"id":"SiogyzI-MjsI"},"source":["If we would like to flatten the document column, we can do as follows.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":497,"status":"ok","timestamp":1712243912158,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"OGgnHWUVgw3r","outputId":"6c44e102-299e-4d3b-d8ed-fc2e7ce5425e"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------+-----+---+-----------------------------------------------------------------------------+---------------+----------+\n","|annotatorType|begin|end|result                                                                       |metadata       |embeddings|\n","+-------------+-----+---+-----------------------------------------------------------------------------+---------------+----------+\n","|document     |0    |27 |Peter is a very good person.                                                 |{sentence -> 0}|[]        |\n","|document     |0    |37 |My life in Russia is very interesting.                                       |{sentence -> 0}|[]        |\n","|document     |0    |76 |John and Peter are brothers. However they don't support each other that much.|{sentence -> 0}|[]        |\n","|document     |0    |67 |Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |{sentence -> 0}|[]        |\n","|document     |0    |68 |Europe is very culture rich. There are huge churches! and big houses!        |{sentence -> 0}|[]        |\n","+-------------+-----+---+-----------------------------------------------------------------------------+---------------+----------+\n","\n"]}],"source":["import pyspark.sql.functions as F\n","\n","doc_df.withColumn(\n","    \"tmp\",\n","    F.explode(\"document\"))\\\n","    .select(\"tmp.*\")\\\n","    .show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"yYxUBF6vMl3o"},"source":["## **Sentence Detector**"]},{"cell_type":"markdown","metadata":{"id":"H8YL-VNMcfQx"},"source":["Finds sentence bounds in raw text."]},{"cell_type":"markdown","metadata":{"id":"oLZ0ci7S50sV"},"source":["- **setCustomBounds()** : Custom sentence separator text e.g. `[\"\\n\"]`\n","\n","- **setUseCustomOnly()** : Use only custom bounds without considering those of Pragmatic Segmenter. Defaults to false. Needs customBounds.\n","\n","- **setUseAbbreviations** : Whether to consider abbreviation strategies for better accuracy but slower performance. Defaults to true.\n","\n","- **setExplodeSentences** : Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jroeCwblgyfb"},"outputs":[],"source":["from sparknlp.annotator import *\n","\n","# we feed the document column coming from Document Assembler\n","\n","sentenceDetector = SentenceDetector()\\\n","      .setInputCols(['document'])\\\n","      .setOutputCol('sentences')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1712243912158,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"RoNkaD5-gz0l","outputId":"4be39d0f-165a-4702-fed1-a4d883213785"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='SentenceDetector_35b3484e62ca', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='SentenceDetector_35b3484e62ca', name='useAbbreviations', doc='whether to apply abbreviations at sentence detection'): True,\n"," Param(parent='SentenceDetector_35b3484e62ca', name='detectLists', doc='whether detect lists during sentence detection'): True,\n"," Param(parent='SentenceDetector_35b3484e62ca', name='useCustomBoundsOnly', doc='Only utilize custom bounds in sentence detection'): False,\n"," Param(parent='SentenceDetector_35b3484e62ca', name='customBounds', doc='characters used to explicitly mark sentence bounds'): [],\n"," Param(parent='SentenceDetector_35b3484e62ca', name='customBoundsStrategy', doc='How to return matched custom bounds'): 'none',\n"," Param(parent='SentenceDetector_35b3484e62ca', name='explodeSentences', doc='whether to explode each sentence into a different row, for better parallelization. Defaults to false.'): False,\n"," Param(parent='SentenceDetector_35b3484e62ca', name='minLength', doc='Set the minimum allowed length for each sentence.'): 0,\n"," Param(parent='SentenceDetector_35b3484e62ca', name='maxLength', doc='Set the maximum allowed length for each sentence'): 99999,\n"," Param(parent='SentenceDetector_35b3484e62ca', name='inputCols', doc='previous annotations columns, if renamed'): ['document'],\n"," Param(parent='SentenceDetector_35b3484e62ca', name='outputCol', doc='output annotation column. can be left default.'): 'sentences'}"]},"metadata":{},"execution_count":21}],"source":["sentenceDetector.extractParamMap()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1712243912159,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"b0U0asw0g3B4","outputId":"489bd98b-1867-44cb-a402-08d0f03aa9ff"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+\n","|text                                                                         |document                                                                                                               |\n","+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+\n","|Peter is a very good person.                                                 |[{document, 0, 27, Peter is a very good person., {sentence -> 0}, []}]                                                 |\n","|My life in Russia is very interesting.                                       |[{document, 0, 37, My life in Russia is very interesting., {sentence -> 0}, []}]                                       |\n","|John and Peter are brothers. However they don't support each other that much.|[{document, 0, 76, John and Peter are brothers. However they don't support each other that much., {sentence -> 0}, []}]|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |[{document, 0, 67, Lucas Nogal Dunbercker is no longer happy. He has a good car though., {sentence -> 0}, []}]         |\n","|Europe is very culture rich. There are huge churches! and big houses!        |[{document, 0, 68, Europe is very culture rich. There are huge churches! and big houses!, {sentence -> 0}, []}]        |\n","+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["doc_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":602,"status":"ok","timestamp":1712243912757,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"g9Cduz_pg4jb","outputId":"810091c9-d282-4e12-91c3-42131d0430e1"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|text                                                                         |document                                                                                                               |sentences                                                                                                                                                                                          |\n","+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|Peter is a very good person.                                                 |[{document, 0, 27, Peter is a very good person., {sentence -> 0}, []}]                                                 |[{document, 0, 27, Peter is a very good person., {sentence -> 0}, []}]                                                                                                                             |\n","|My life in Russia is very interesting.                                       |[{document, 0, 37, My life in Russia is very interesting., {sentence -> 0}, []}]                                       |[{document, 0, 37, My life in Russia is very interesting., {sentence -> 0}, []}]                                                                                                                   |\n","|John and Peter are brothers. However they don't support each other that much.|[{document, 0, 76, John and Peter are brothers. However they don't support each other that much., {sentence -> 0}, []}]|[{document, 0, 27, John and Peter are brothers., {sentence -> 0}, []}, {document, 29, 76, However they don't support each other that much., {sentence -> 1}, []}]                                  |\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |[{document, 0, 67, Lucas Nogal Dunbercker is no longer happy. He has a good car though., {sentence -> 0}, []}]         |[{document, 0, 41, Lucas Nogal Dunbercker is no longer happy., {sentence -> 0}, []}, {document, 43, 67, He has a good car though., {sentence -> 1}, []}]                                           |\n","|Europe is very culture rich. There are huge churches! and big houses!        |[{document, 0, 68, Europe is very culture rich. There are huge churches! and big houses!, {sentence -> 0}, []}]        |[{document, 0, 27, Europe is very culture rich., {sentence -> 0}, []}, {document, 29, 52, There are huge churches!, {sentence -> 1}, []}, {document, 54, 68, and big houses!, {sentence -> 2}, []}]|\n","+-----------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["sent_df = sentenceDetector.transform(doc_df)\n","\n","sent_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712243912757,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"rY_8CGdWg57q","outputId":"01a40c1c-bdb8-4f0a-f57d-3f82635a6c28"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(sentences=[Row(annotatorType='document', begin=0, end=27, result='Peter is a very good person.', metadata={'sentence': '0'}, embeddings=[])]),\n"," Row(sentences=[Row(annotatorType='document', begin=0, end=37, result='My life in Russia is very interesting.', metadata={'sentence': '0'}, embeddings=[])]),\n"," Row(sentences=[Row(annotatorType='document', begin=0, end=27, result='John and Peter are brothers.', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='document', begin=29, end=76, result=\"However they don't support each other that much.\", metadata={'sentence': '1'}, embeddings=[])])]"]},"metadata":{},"execution_count":24}],"source":["sent_df.select('sentences').take(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":425,"status":"ok","timestamp":1712243913180,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"Byi0E6Zsg7Te","outputId":"d8658bc1-878d-4492-d1db-8e63bca44906"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['Peter is a very good person.']),\n"," Row(result=['My life in Russia is very interesting.']),\n"," Row(result=['John and Peter are brothers.', \"However they don't support each other that much.\"])]"]},"metadata":{},"execution_count":25}],"source":["sent_df.select('sentences.result').take(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712243913180,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"wZJBeQAqg8it","outputId":"2981b91d-0c4e-414a-ba2a-094dde076ce8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(metadata=[{'sentence': '0'}]),\n"," Row(metadata=[{'sentence': '0'}]),\n"," Row(metadata=[{'sentence': '0'}, {'sentence': '1'}])]"]},"metadata":{},"execution_count":26}],"source":["sent_df.select('sentences.metadata').take(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":70},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243913180,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"jbzTKt3Rg933","outputId":"134e92b3-b60a-451b-ee76-651bb0b9671d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'The patient was prescribed 1 capsule of Advil for 5 days. 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. It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months.'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":27}],"source":["text ='The patient was prescribed 1 capsule of Advil for 5 days. 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. It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months.'\n","text\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":451,"status":"ok","timestamp":1712243913628,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"y5AvDRqeg_qY","outputId":"d06acac7-6c8e-496a-b0dd-d9fee09c0e60"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|text                                                                                                                                                                                                                                                                                                                                           |\n","+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|The patient was prescribed 1 capsule of Advil for 5 days. 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. It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months.|\n","+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["spark_df = spark.createDataFrame([[text]]).toDF(\"text\")\n","\n","spark_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":983,"status":"ok","timestamp":1712243914609,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"8DLxDOsqhBH1","outputId":"407f06cc-2444-407a-c826-92bfcbff83f4"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------+\n","|                                              text|\n","+--------------------------------------------------+\n","|The patient was prescribed 1 capsule of Advil f...|\n","+--------------------------------------------------+\n","\n"]}],"source":["spark_df.show(truncate=50)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":546,"status":"ok","timestamp":1712243915153,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"lIGtk0LYhCcg","outputId":"69295ee2-4d0f-4461-ea1d-94fef3b542a8"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+\n","|                                              text|                                          document|                                         sentences|\n","+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+\n","|The patient was prescribed 1 capsule of Advil f...|[{document, 0, 334, The patient was prescribed ...|[{document, 0, 56, The patient was prescribed 1...|\n","+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+\n","\n"]}],"source":["doc_df = documentAssembler.transform(spark_df)\n","\n","sent_df = sentenceDetector.transform(doc_df)\n","\n","sent_df.show(truncate=50)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243915154,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"b66Mh61-hDyI","outputId":"dc8eb211-0ef0-4de0-b29f-d705e0808314"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['The patient was prescribed 1 capsule of Advil for 5 days.', '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.', 'It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months.'])]"]},"metadata":{},"execution_count":31}],"source":["sent_df.select('sentences.result').take(1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243915154,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"Iwhl6wa0hFLX","outputId":"6d47af94-6d06-4300-bb39-0f3e20ba489c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["SentenceDetector_35b3484e62ca"]},"metadata":{},"execution_count":32}],"source":["# setExplodeSentences: Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.\n","\n","sentenceDetector.setExplodeSentences(True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":914,"status":"ok","timestamp":1712243916066,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"ofJNmvhKhG3d","outputId":"a0f55201-0b0e-4862-f6fd-a8d88664406e"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+\n","|                                              text|                                          document|                                         sentences|\n","+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+\n","|The patient was prescribed 1 capsule of Advil f...|[{document, 0, 334, The patient was prescribed ...|[{document, 0, 56, The patient was prescribed 1...|\n","|The patient was prescribed 1 capsule of Advil f...|[{document, 0, 334, The patient was prescribed ...|[{document, 58, 240, He was seen by the endocri...|\n","|The patient was prescribed 1 capsule of Advil f...|[{document, 0, 334, The patient was prescribed ...|[{document, 242, 334, It was determined that al...|\n","+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+\n","\n"]}],"source":["sent_df = sentenceDetector.transform(doc_df)\n","\n","sent_df.show(truncate=50)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":774,"status":"ok","timestamp":1712243916839,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"caqAvKcMhKdz","outputId":"d4c276c4-7559-4599-b129-5f847da590bc"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|result                                                                                                                                                                                   |\n","+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|[The patient was prescribed 1 capsule of Advil for 5 days.]                                                                                                                              |\n","|[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.]|\n","|[It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months.]                                                                                          |\n","+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["sent_df.select('sentences.result').show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":577,"status":"ok","timestamp":1712243917415,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"a0yjjiwYhKgp","outputId":"e7b902c6-056a-40bd-f7c5-5721115e4cc7"},"outputs":[{"output_type":"stream","name":"stdout","text":["+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|col                                                                                                                                                                                    |\n","+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|The patient was prescribed 1 capsule of Advil for 5 days.                                                                                                                              |\n","|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.|\n","|It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months.                                                                                          |\n","+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["from pyspark.sql import functions as F\n","\n","sent_df.select(F.explode('sentences.result')).show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"vpSNJ1Z4tzbE"},"source":["**`.setCustomBounds([r\"\\\\.\", \";\"])`**\n","\n","**`.setCustomBoundsStrategy(\"append\")`**\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6XEcZhlKhKjr"},"outputs":[],"source":["text = [\n","    [\"Peter is a very good person.\"],\n","    [\"My life in Russia is very interesting.\"],\n","    [\"John and Peter are brothers. However; they don't support each other that much.\"],\n","    [\"Lucas Nogal Dunbercker is no longer happy. He has a good car though.\"],\n","    [\"Europe is very culture rich. There are huge churches! and big houses!\"]\n","    ]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":485,"status":"ok","timestamp":1712243917898,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"r8faOos6hKmC","outputId":"548903a8-586c-48ac-fc39-2ad26a89e2bf"},"outputs":[{"output_type":"stream","name":"stdout","text":["+------------------------------------------------------------------------------+\n","|text                                                                          |\n","+------------------------------------------------------------------------------+\n","|Peter is a very good person.                                                  |\n","|My life in Russia is very interesting.                                        |\n","|John and Peter are brothers. However; they don't support each other that much.|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.          |\n","|Europe is very culture rich. There are huge churches! and big houses!         |\n","+------------------------------------------------------------------------------+\n","\n"]}],"source":["spark_df = spark.createDataFrame(text).toDF(\"text\")\n","spark_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jYCJRDWChKoW"},"outputs":[],"source":["doc_df = documentAssembler.transform(spark_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1136,"status":"ok","timestamp":1712243919033,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"R40Avel7hKrG","outputId":"61eefd2e-3b69-4265-f9b9-db40a3182afd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['The patient was prescribed 1 capsule of Advil for 5 days.']),\n"," Row(result=['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.']),\n"," Row(result=['It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months.'])]"]},"metadata":{},"execution_count":39}],"source":["sent_df.select('sentences.result').take(5)"]},{"cell_type":"markdown","metadata":{"id":"bg9a63CTv51L"},"source":["**`.setCustomBoundsStrategy(\"prepend\")`**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712243919034,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"0GgrNKpQhKuA","outputId":"7d305b29-5bde-4cd2-cf11-d137548eba3d"},"outputs":[{"output_type":"stream","name":"stdout","text":["+------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|text                                                                          |document                                                                                                                |sentences                                                                                                                                                                                                                                                                                                                                    |\n","+------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|Peter is a very good person.                                                  |[{document, 0, 27, Peter is a very good person., {sentence -> 0}, []}]                                                  |[{document, 0, 26, Peter is a very good person, {sentence -> 0}, []}, {document, 27, 27, ., {sentence -> 1}, []}]                                                                                                                                                                                                                            |\n","|My life in Russia is very interesting.                                        |[{document, 0, 37, My life in Russia is very interesting., {sentence -> 0}, []}]                                        |[{document, 0, 36, My life in Russia is very interesting, {sentence -> 0}, []}, {document, 37, 37, ., {sentence -> 1}, []}]                                                                                                                                                                                                                  |\n","|John and Peter are brothers. However; they don't support each other that much.|[{document, 0, 77, John and Peter are brothers. However; they don't support each other that much., {sentence -> 0}, []}]|[{document, 0, 26, John and Peter are brothers, {sentence -> 0}, []}, {document, 27, 27, ., {sentence -> 1}, []}, {document, 29, 35, However, {sentence -> 2}, []}, {document, 36, 36, ;, {sentence -> 3}, []}, {document, 38, 76, they don't support each other that much, {sentence -> 4}, []}, {document, 77, 77, ., {sentence -> 5}, []}]|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.          |[{document, 0, 67, Lucas Nogal Dunbercker is no longer happy. He has a good car though., {sentence -> 0}, []}]          |[{document, 0, 40, Lucas Nogal Dunbercker is no longer happy, {sentence -> 0}, []}, {document, 41, 41, ., {sentence -> 1}, []}, {document, 43, 66, He has a good car though, {sentence -> 2}, []}, {document, 67, 67, ., {sentence -> 3}, []}]                                                                                               |\n","|Europe is very culture rich. There are huge churches! and big houses!         |[{document, 0, 68, Europe is very culture rich. There are huge churches! and big houses!, {sentence -> 0}, []}]         |[{document, 0, 26, Europe is very culture rich, {sentence -> 0}, []}, {document, 27, 27, ., {sentence -> 1}, []}, {document, 29, 51, There are huge churches, {sentence -> 2}, []}, {document, 52, 52, !, {sentence -> 3}, []}, {document, 54, 67, and big houses, {sentence -> 4}, []}, {document, 68, 68, !, {sentence -> 5}, []}]         |\n","+------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["sentenceDetector = SentenceDetector()\\\n","      .setInputCols(['document'])\\\n","      .setOutputCol('sentences')\\\n","      .setCustomBounds([r\"\\.\", \";\", \"!\"])\\\n","      .setCustomBoundsStrategy(\"prepend\")\n","\n","sent_df = sentenceDetector.transform(doc_df)\n","sent_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1191,"status":"ok","timestamp":1712243920222,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"8A5xFjKUhKwx","outputId":"a64476df-2560-4cba-b284-e41d2ffcd147"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['Peter is a very good person', '.']),\n"," Row(result=['My life in Russia is very interesting', '.']),\n"," Row(result=['John and Peter are brothers', '.', 'However', ';', \"they don't support each other that much\", '.']),\n"," Row(result=['Lucas Nogal Dunbercker is no longer happy', '.', 'He has a good car though', '.']),\n"," Row(result=['Europe is very culture rich', '.', 'There are huge churches', '!', 'and big houses', '!'])]"]},"metadata":{},"execution_count":41}],"source":["sent_df.select('sentences.result').take(5)"]},{"cell_type":"markdown","metadata":{"id":"sk-r3ZiTxrAM"},"source":["The separation of the sentences is determined according to the characters we set with custom bound. When we use `append`, sentences are differentiated according to the characters, if `prepend` is used, it also determines the characters as separate sentences."]},{"cell_type":"markdown","metadata":{"id":"pRiC65UdZ7LC"},"source":["### **Sentence Detector DL**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ywkZKRzUhKzI"},"outputs":[],"source":["text ='The patient was prescribed 1 capsule of Advil for 5 days. 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. It was determined that all SGLT2 inhibitors should be discontinued indefinitely from 3 months.'\n","\n","spark_df = spark.createDataFrame([[text]]).toDF(\"text\")\n","\n","doc_df = documentAssembler.transform(spark_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12099,"status":"ok","timestamp":1712243932319,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"qgPE_HFehK1l","outputId":"6c8478ca-c247-48ae-d655-bdc05c1d7e08"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n","+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|col                                                                                                                                                                                    |\n","+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|The patient was prescribed 1 capsule of Advil for 5 days.                                                                                                                              |\n","|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.|\n","|It was determined that all SGLT2 inhibitors should be discontinued indefinitely from 3 months.                                                                                         |\n","+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["sentencerDL = SentenceDetectorDLModel\\\n","    .pretrained(\"sentence_detector_dl\", \"en\") \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"sentences\")\\\n","\n","sent_dl_df = sentencerDL.transform(doc_df)\n","\n","sent_dl_df.select(F.explode('sentences.result')).show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3917,"status":"ok","timestamp":1712243936227,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"A93MpGJ7hK4f","outputId":"115332bb-f1e6-427a-b146-c5293e89e7cc"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"]}],"source":["documenter = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = SentenceDetector()\\\n","    .setInputCols(['document'])\\\n","    .setOutputCol('sentences')\\\n","\n","sentencerDL = SentenceDetectorDLModel\\\n","    .pretrained(\"sentence_detector_dl\", \"en\") \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"sentences\")\n","\n","\n","sd_pipeline = PipelineModel(stages=[documenter, sentenceDetector])\n","sd_model = LightPipeline(sd_pipeline)\n","\n","\n","# DL version\n","sd_dl_pipeline = PipelineModel(stages=[documenter, sentencerDL])\n","sd_dl_model = LightPipeline(sd_dl_pipeline)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":453,"status":"ok","timestamp":1712243936678,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"Ik6lGwTNhK69","outputId":"b203ce5a-4522-4c9b-fb74-322c69819878"},"outputs":[{"output_type":"stream","name":"stdout","text":["0\t0\t51\tJohn loves Mary.Mary loves Peter\n","Peter loves Helen .\n","1\t52\t68\tHelen loves John;\n","2\t70\t97\tTotal: four people involved.\n"]}],"source":["text = \"\"\"John loves Mary.Mary loves Peter\n","Peter loves Helen .Helen loves John;\n","Total: four people involved.\"\"\"\n","\n","# sd_model\n","for anno in sd_model.fullAnnotate(text)[0][\"sentences\"]:\n","    print(\"{}\\t{}\\t{}\\t{}\".format(\n","        anno.metadata[\"sentence\"], anno.begin, anno.end, anno.result))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243936678,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"oChiU7zThK9l","outputId":"e13d8669-2ae0-47a0-9440-f306e5301e23"},"outputs":[{"output_type":"stream","name":"stdout","text":["0\t0\t15\tJohn loves Mary.\n","1\t16\t31\tMary loves Peter\n","2\t33\t51\tPeter loves Helen .\n","3\t52\t68\tHelen loves John;\n","4\t70\t97\tTotal: four people involved.\n"]}],"source":["# sd_dl_model\n","for anno in sd_dl_model.fullAnnotate(text)[0][\"sentences\"]:\n","    print(\"{}\\t{}\\t{}\\t{}\".format(\n","        anno.metadata[\"sentence\"], anno.begin, anno.end, anno.result))"]},{"cell_type":"markdown","metadata":{"id":"ELZyacNqbgeq"},"source":["## Tokenizer"]},{"cell_type":"markdown","metadata":{"id":"tnkBJxAqbu2b"},"source":["Identifies tokens with tokenization open standards. It is an **Annotator Approach, so it requires .fit()**.\n","\n","A few rules will help customizing it if defaults do not fit user needs.\n","\n","setExceptions(StringArray): List of tokens to not alter at all. Allows composite tokens like two worded tokens that the user may not want to split.\n","\n","- **addException()** : Add a single exception.\n","\n","- **setExceptionsPath()** : Path to txt file with list of token exceptions.\n","\n","- **caseSensitiveExceptions** :  Whether to follow case sensitiveness for matching exceptions in text.\n","\n","- **contextChars()** : List of 1 character string to rip off from tokens, such as parenthesis or question marks. Ignored if using prefix, infix or suffix patterns.\n","\n","- **splitChars()** : List of 1 character string to split tokens inside, such as hyphens. Ignored if using infix, prefix or suffix patterns.\n","\n","- **splitPattern()** : pattern to separate from the inside of tokens. takes priority over splitChars. setTargetPattern: Basic regex rule to identify a candidate for tokenization. Defaults to \\S+ which means anything not a space.\n","\n","- **setSuffixPattern()** : Regex to identify subtokens that are in the end of the token. Regex has to end with \\z and must contain groups (). Each group will become a separate token within the prefix. Defaults to non-letter characters. e.g. quotes or parenthesis\n","\n","- **setPrefixPattern()** : Regex to identify subtokens that come in the beginning of the token. Regex has to start with \\A and must contain groups (). Each group will become a separate token within the prefix. Defaults to non-letter characters. e.g. quotes or parenthesis.\n","\n","- **addInfixPattern()** : Add an extension pattern regex with groups to the top of the rules (will target first, from more specific to the more general).\n","\n","- **minLength()** : Set the minimum allowed legth for each token.\n","\n","- **maxLength()** : Set the maximum allowed legth for each token.\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OCPDLxEnhedS"},"outputs":[],"source":["tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243936678,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"r3qf5PQmhgGy","outputId":"989c467f-3ab6-46d7-f42c-7906ca663156"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='Tokenizer_43ff359167af', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='Tokenizer_43ff359167af', name='targetPattern', doc='pattern to grab from text as token candidates. Defaults \\\\S+'): '\\\\S+',\n"," Param(parent='Tokenizer_43ff359167af', name='contextChars', doc='character list used to separate from token boundaries'): ['.',\n","  ',',\n","  ';',\n","  ':',\n","  '!',\n","  '?',\n","  '*',\n","  '-',\n","  '(',\n","  ')',\n","  '\"',\n","  \"'\"],\n"," Param(parent='Tokenizer_43ff359167af', name='caseSensitiveExceptions', doc='Whether to care for case sensitiveness in exceptions'): True,\n"," Param(parent='Tokenizer_43ff359167af', name='minLength', doc='Set the minimum allowed length for each token'): 0,\n"," Param(parent='Tokenizer_43ff359167af', name='maxLength', doc='Set the maximum allowed length for each token'): 99999,\n"," Param(parent='Tokenizer_43ff359167af', name='inputCols', doc='previous annotations columns, if renamed'): ['document'],\n"," Param(parent='Tokenizer_43ff359167af', name='outputCol', doc='output annotation column. can be left default.'): 'token'}"]},"metadata":{},"execution_count":48}],"source":["tokenizer.extractParamMap()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nBiK4gMlhhiX"},"outputs":[],"source":["text = 'Peter Parker (Spiderman) is a nice guy and lives in New York but has no e-mail!'\n","\n","spark_df = spark.createDataFrame([[text]]).toDF(\"text\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1365,"status":"ok","timestamp":1712243938041,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"wv7-NoJZhiv2","outputId":"ba0f515f-40fb-485e-faf3-4dae869b337b"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","|                                                                           text|                                                                                            document|                                                                                               token|\n","+-------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","|Peter Parker (Spiderman) is a nice guy and lives in New York but has no e-mail!|[{document, 0, 78, Peter Parker (Spiderman) is a nice guy and lives in New York but has no e-mail...|[{token, 0, 4, Peter, {sentence -> 0}, []}, {token, 6, 11, Parker, {sentence -> 0}, []}, {token, ...|\n","+-------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["doc_df = documentAssembler.transform(spark_df)\n","\n","token_df = tokenizer.fit(doc_df).transform(doc_df)\n","\n","token_df.show(truncate=100)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":541,"status":"ok","timestamp":1712243938580,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"7qiKn3iDhkPb","outputId":"32c22639-5b97-4804-975e-06fbd98198de"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['Peter', 'Parker', '(', 'Spiderman', ')', 'is', 'a', 'nice', 'guy', 'and', 'lives', 'in', 'New', 'York', 'but', 'has', 'no', 'e-mail', '!'])]"]},"metadata":{},"execution_count":51}],"source":["token_df.select('token.result').take(1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OrMwy1FZhlWj"},"outputs":[],"source":["tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\") \\\n","    .setSplitChars(['-']) \\\n","    .setContextChars(['?', '!']) \\\n","    .addException(\"New York\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1712243938581,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"UYCsSgnphmvT","outputId":"62d3123c-d4af-479d-baeb-cd1e4005bbe9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['Peter', 'Parker', '(Spiderman)', 'is', 'a', 'nice', 'guy', 'and', 'lives', 'in', 'New York', 'but', 'has', 'no', 'e', 'mail', '!'])]"]},"metadata":{},"execution_count":53}],"source":["token_df = tokenizer.fit(doc_df).transform(doc_df)\n","\n","token_df.select('token.result').take(1)"]},{"cell_type":"markdown","metadata":{"id":"AOn8d1tcBkK3"},"source":["## Regex Tokenizer"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":937,"status":"ok","timestamp":1712243939514,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"uKFXKVgWhoEK","outputId":"4f0c068e-dd5f-4942-da23-9ab15666f074"},"outputs":[{"output_type":"stream","name":"stdout","text":["+---------------------------------------------------+---------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|text                                               |document                                                                                     |sentence                                                                                     |regexToken                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |\n","+---------------------------------------------------+---------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|1. T1-T2 DATE**[12/24/13] $1.99 () (10/12), ph+ 90%|[{document, 0, 50, 1. T1-T2 DATE**[12/24/13] $1.99 () (10/12), ph+ 90%, {sentence -> 0}, []}]|[{document, 0, 50, 1. T1-T2 DATE**[12/24/13] $1.99 () (10/12), ph+ 90%, {sentence -> 0}, []}]|[{token, 0, 0, 1, {sentence -> 0}, []}, {token, 1, 1, ., {sentence -> 0}, []}, {token, 3, 4, T1, {sentence -> 0}, []}, {token, 5, 5, -, {sentence -> 0}, []}, {token, 6, 7, T2, {sentence -> 0}, []}, {token, 9, 12, DATE, {sentence -> 0}, []}, {token, 13, 13, *, {sentence -> 0}, []}, {token, 14, 14, *, {sentence -> 0}, []}, {token, 15, 15, [, {sentence -> 0}, []}, {token, 16, 23, 12/24/13, {sentence -> 0}, []}, {token, 24, 24, ], {sentence -> 0}, []}, {token, 26, 26, $, {sentence -> 0}, []}, {token, 27, 27, 1, {sentence -> 0}, []}, {token, 28, 28, ., {sentence -> 0}, []}, {token, 29, 30, 99, {sentence -> 0}, []}, {token, 32, 33, (), {sentence -> 0}, []}, {token, 35, 41, (10/12), {sentence -> 0}, []}, {token, 42, 42, ,, {sentence -> 0}, []}, {token, 44, 45, ph, {sentence -> 0}, []}, {token, 46, 46, +, {sentence -> 0}, []}, {token, 48, 49, 90, {sentence -> 0}, []}, {token, 50, 50, %, {sentence -> 0}, []}]|\n","+---------------------------------------------------+---------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["from pyspark.sql.types import StringType\n","\n","content = \"1. T1-T2 DATE**[12/24/13] $1.99 () (10/12), ph+ 90%\"\n","pattern = \"\\\\s+|(?=[-.:;*+,$&%\\\\[\\\\]])|(?<=[-.:;*+,$&%\\\\[\\\\]])\"\n","\n","df = spark.createDataFrame([content], StringType()).withColumnRenamed(\"value\", \"text\")\n","\n","documenter = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = SentenceDetector()\\\n","    .setInputCols(['document'])\\\n","    .setOutputCol('sentence')\n","\n","regexTokenizer = RegexTokenizer() \\\n","    .setInputCols([\"sentence\"]) \\\n","    .setOutputCol(\"regexToken\") \\\n","    .setPattern(pattern) \\\n","    .setPositionalMask(False)\n","\n","docPatternRemoverPipeline = Pipeline().setStages([documenter,\n","                                                  sentenceDetector,\n","                                                  regexTokenizer])\n","\n","result = docPatternRemoverPipeline.fit(df).transform(df)\n","result.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":739},"executionInfo":{"elapsed":527,"status":"ok","timestamp":1712243940039,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"5Zd0kv8Zhpal","outputId":"14f51b42-10b6-487a-b2f1-c90f9ae1e300"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   regexToken\n","0           1\n","1           .\n","2          T1\n","3           -\n","4          T2\n","5        DATE\n","6           *\n","7           *\n","8           [\n","9    12/24/13\n","10          ]\n","11          $\n","12          1\n","13          .\n","14         99\n","15         ()\n","16    (10/12)\n","17          ,\n","18         ph\n","19          +\n","20         90\n","21          %"],"text/html":["\n","  <div id=\"df-e6bd58d0-8b77-43fd-95da-91c5a5802197\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>regexToken</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>.</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>T1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>T2</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>DATE</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>*</td>\n","    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So, it’s better to explain Pipeline concept through Spark ML official documentation.\n","\n","What is a Pipeline anyway? In machine learning, it is common to run a sequence of algorithms to process and learn from data.\n","\n","Apache Spark ML represents such a workflow as a Pipeline, which consists of a sequence of PipelineStages (Transformers and Estimators) to be run in a specific order.\n","\n","In simple terms, a pipeline chains multiple Transformers and Estimators together to specify an ML workflow. We use Pipeline to chain multiple Transformers and Estimators together to specify our machine learning workflow.\n","\n","The figure below is for the training time usage of a Pipeline."]},{"cell_type":"markdown","metadata":{"id":"jK5AAYQqhRlG"},"source":["![image.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"dwLlY7i4hhq1"},"source":["A Pipeline is specified as a sequence of stages, and each stage is either a Transformer or an Estimator. These stages are run in order, and the input DataFrame is transformed as it passes through each stage. That is, the data are passed through the fitted pipeline in order. Each stage’s transform() method updates the dataset and passes it to the next stage. With the help of Pipelines, we can ensure that training and test data go through identical feature processing steps.\n","\n","Now let’s see how this can be done in Spark NLP using Annotators and Transformers. Assume that we have the following steps that need to be applied one by one on a data frame.\n","\n","- Split text into sentences\n","- Tokenize\n","\n","And here is how we code this pipeline up in Spark NLP."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iQ0WffKWhrXP"},"outputs":[],"source":["from pyspark.ml import Pipeline\n","\n","documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = SentenceDetector()\\\n","    .setInputCols(['document'])\\\n","    .setOutputCol('sentences')\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"sentences\"]) \\\n","    .setOutputCol(\"token\")\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               sentenceDetector,\n","                               tokenizer])\n","\n","spark_df = spark.read.text('./sample-sentences-en.txt').toDF('text')\n","\n","pipelineModel = nlpPipeline.fit(spark_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1712243940040,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"v6bO0KOEhtin","outputId":"4bbe0e9f-43f2-476d-e33a-542ee796abf3"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+\n","|text                                                                         |\n","+-----------------------------------------------------------------------------+\n","|Peter is a very good person.                                                 |\n","|My life in Russia is very interesting.                                       |\n","|John and Peter are brothers. However they don't support each other that much.|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |\n","|Europe is very culture rich. There are huge churches! and big houses!        |\n","+-----------------------------------------------------------------------------+\n","\n"]}],"source":["spark_df = spark.read.text('./sample-sentences-en.txt').toDF('text')\n","\n","spark_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dDogx9b0hu2D"},"outputs":[],"source":["result = pipelineModel.transform(spark_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1712243940040,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"LtvRyxJmhwBs","outputId":"f4ce3d1a-44af-4145-ba7c-e73b2c8874b2"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","|                                    text|                                document|                               sentences|                                   token|\n","+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","|            Peter is a very good person.|[{document, 0, 27, Peter is a very go...|[{document, 0, 27, Peter is a very go...|[{token, 0, 4, Peter, {sentence -> 0}...|\n","|  My life in Russia is very interesting.|[{document, 0, 37, My life in Russia ...|[{document, 0, 37, My life in Russia ...|[{token, 0, 1, My, {sentence -> 0}, [...|\n","|John and Peter are brothers. However ...|[{document, 0, 76, John and Peter are...|[{document, 0, 27, John and Peter are...|[{token, 0, 3, John, {sentence -> 0},...|\n","|Lucas Nogal Dunbercker is no longer h...|[{document, 0, 67, Lucas Nogal Dunber...|[{document, 0, 41, Lucas Nogal Dunber...|[{token, 0, 4, Lucas, {sentence -> 0}...|\n","|Europe is very culture rich. There ar...|[{document, 0, 68, Europe is very cul...|[{document, 0, 27, Europe is very cul...|[{token, 0, 5, Europe, {sentence -> 0...|\n","+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","\n"]}],"source":["result.show(truncate=40)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243940040,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"6P8vJtObhxUM","outputId":"3a645b7c-97dd-4fdc-e864-ab5e59c05320"},"outputs":[{"output_type":"stream","name":"stdout","text":["root\n"," |-- text: string (nullable = true)\n"," |-- document: array (nullable = true)\n"," |    |-- element: struct (containsNull = true)\n"," |    |    |-- annotatorType: string (nullable = true)\n"," |    |    |-- begin: integer (nullable = false)\n"," |    |    |-- end: integer (nullable = false)\n"," |    |    |-- result: string (nullable = true)\n"," |    |    |-- metadata: map (nullable = true)\n"," |    |    |    |-- key: string\n"," |    |    |    |-- value: string (valueContainsNull = true)\n"," |    |    |-- embeddings: array (nullable = true)\n"," |    |    |    |-- element: float (containsNull = false)\n"," |-- sentences: array (nullable = true)\n"," |    |-- element: struct (containsNull = true)\n"," |    |    |-- annotatorType: string (nullable = true)\n"," |    |    |-- begin: integer (nullable = false)\n"," |    |    |-- end: integer (nullable = false)\n"," |    |    |-- result: string (nullable = true)\n"," |    |    |-- metadata: map (nullable = true)\n"," |    |    |    |-- key: string\n"," |    |    |    |-- value: string (valueContainsNull = true)\n"," |    |    |-- embeddings: array (nullable = true)\n"," |    |    |    |-- element: float (containsNull = false)\n"," |-- token: array (nullable = true)\n"," |    |-- element: struct (containsNull = true)\n"," |    |    |-- annotatorType: string (nullable = true)\n"," |    |    |-- begin: integer (nullable = false)\n"," |    |    |-- end: integer (nullable = false)\n"," |    |    |-- result: string (nullable = true)\n"," |    |    |-- metadata: map (nullable = true)\n"," |    |    |    |-- key: string\n"," |    |    |    |-- value: string (valueContainsNull = true)\n"," |    |    |-- embeddings: array (nullable = true)\n"," |    |    |    |-- element: float (containsNull = false)\n","\n"]}],"source":["result.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":500,"status":"ok","timestamp":1712243940536,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"_LKPEFfShyyt","outputId":"ed95705c-54ac-41d4-ab22-b13cd14d7731"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['Peter is a very good person.']),\n"," Row(result=['My life in Russia is very interesting.']),\n"," Row(result=['John and Peter are brothers.', \"However they don't support each other that much.\"])]"]},"metadata":{},"execution_count":61}],"source":["result.select('sentences.result').take(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1712243940537,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"eZ0fsfHlh0cC","outputId":"a641f993-071f-45a5-afb0-36fb7d6986d2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Row(token=[Row(annotatorType='token', begin=0, end=3, result='John', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=5, end=7, result='and', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=9, end=13, result='Peter', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=15, end=17, result='are', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=19, end=26, result='brothers', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=27, end=27, result='.', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=29, end=35, result='However', metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=37, end=40, result='they', metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=42, end=46, result=\"don't\", metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=48, end=54, result='support', metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=56, end=59, result='each', metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=61, end=65, result='other', metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=67, end=70, result='that', metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=72, end=75, result='much', metadata={'sentence': '1'}, embeddings=[]), Row(annotatorType='token', begin=76, end=76, result='.', metadata={'sentence': '1'}, embeddings=[])])"]},"metadata":{},"execution_count":62}],"source":["result.select('token').take(3)[2]"]},{"cell_type":"markdown","metadata":{"id":"42dSp9dGmtmr"},"source":["## Normalizer"]},{"cell_type":"markdown","metadata":{"id":"spOjcducnAsR"},"source":["Removes all dirty characters from text following a regex pattern and transforms words based on a provided dictionary\n","\n","`setCleanupPatterns(patterns)`: Regular expressions list for normalization, defaults [^A-Za-z]\n","\n","`setLowercase(value)`: lowercase tokens, default false\n","\n","`setSlangDictionary(path)`: txt file with delimited words to be transformed into something else\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243940537,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"VSx5snVUh1xy","outputId":"632d0916-2977-402d-d3ad-05ad429ad850"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":63}],"source":["import string\n","string.punctuation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KJ6gXciVh3Fr"},"outputs":[],"source":["from sparknlp.base import *\n","from sparknlp.annotator import *\n","\n","documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")\n","\n","normalizer = Normalizer() \\\n","    .setInputCols([\"token\"]) \\\n","    .setOutputCol(\"normalized\")\\\n","    .setLowercase(True)\\\n","    .setCleanupPatterns([\"[^\\w\\d\\s]\"]) # remove punctuations (keep alphanumeric chars)\n","    # if we don't set CleanupPatterns, it will only keep alphabet letters ([^A-Za-z])\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               tokenizer,\n","                               normalizer])\n","\n","result = nlpPipeline.fit(spark_df).transform(spark_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":693,"status":"ok","timestamp":1712243941225,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"O7WmJLGqh43n","outputId":"24126b8b-1fb8-46cd-a690-9eb9e7160e75"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[DocumentAssembler_3addfed15ead,\n"," REGEX_TOKENIZER_159fe4ef89b1,\n"," NORMALIZER_4319651f02be]"]},"metadata":{},"execution_count":65}],"source":["nlpPipeline.fit(spark_df).stages"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243941226,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"cKTRpEfIh6lc","outputId":"2e9bbfeb-f20a-4764-f27d-2474837d562c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(token=[Row(annotatorType='token', begin=0, end=4, result='Peter', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=6, end=7, result='is', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=9, end=9, result='a', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=11, end=14, result='very', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=16, end=19, result='good', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=21, end=26, result='person', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=27, end=27, result='.', metadata={'sentence': '0'}, embeddings=[])]),\n"," Row(token=[Row(annotatorType='token', begin=0, end=1, result='My', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=3, end=6, result='life', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=8, end=9, result='in', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=11, end=16, result='Russia', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=18, end=19, result='is', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=21, end=24, result='very', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=26, end=36, result='interesting', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=37, end=37, result='.', metadata={'sentence': '0'}, embeddings=[])])]"]},"metadata":{},"execution_count":66}],"source":["result.select('token').take(2)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712243941226,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"0tBhnAwnh7vN","outputId":"667606a6-3f8e-4a32-c53e-b927e37610c7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['peter', 'is', 'a', 'very', 'good', 'person']),\n"," Row(result=['my', 'life', 'in', 'russia', 'is', 'very', 'interesting'])]"]},"metadata":{},"execution_count":67}],"source":["result.select('normalized.result').take(2)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243941226,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"gxtJ8TBVh9T_","outputId":"3aa2e8d7-2d8a-46c3-f8ca-cd21f84df25a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(normalized=[Row(annotatorType='token', begin=0, end=4, result='peter', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=6, end=7, result='is', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=9, end=9, result='a', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=11, end=14, result='very', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=16, end=19, result='good', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=21, end=26, result='person', metadata={'sentence': '0'}, embeddings=[])]),\n"," Row(normalized=[Row(annotatorType='token', begin=0, end=1, result='my', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=3, end=6, result='life', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=8, end=9, result='in', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=11, end=16, result='russia', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=18, end=19, result='is', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=21, end=24, result='very', metadata={'sentence': '0'}, embeddings=[]), Row(annotatorType='token', begin=26, end=36, result='interesting', metadata={'sentence': '0'}, embeddings=[])])]"]},"metadata":{},"execution_count":68}],"source":["result.select('normalized').take(2)"]},{"cell_type":"markdown","metadata":{"id":"__iJ4EMeVb3n"},"source":["## Document Normalizer"]},{"cell_type":"markdown","metadata":{"id":"wfLIupJFZi3c"},"source":["The DocumentNormalizer is an annotator that can be used after the DocumentAssembler to narmalize documents once that they have been processed and indexed .\n","It takes in input annotated documents of type Array AnnotatorType.DOCUMENT and gives as output annotated document of type AnnotatorType.DOCUMENT .\n","\n","Parameters are:  \n","\n","\n","- **inputCol**    : input column name string which targets a column of type Array(AnnotatorType.DOCUMENT).\n","\n","- **outputCol**      : output column name string which targets a column of type AnnotatorType.DOCUMENT.\n","\n","- **action** : action string to perform applying regex patterns, i.e. (clean | extract). Default is \"clean\".\n","\n","- **cleanupPatterns**  : normalization regex patterns which match will be removed from document. Default is \"<[^>]*>\" (e.g., it removes all HTML tags).\n","\n","- **replacement** : replacement string to apply when regexes match. Default is \" \".\n","\n","- **lowercase** : whether to convert strings to lowercase. Default is False.\n","\n","- **removalPolicy** : removalPolicy to remove patterns from text with a given policy. Valid policy values are: \"all\", \"pretty_all\", \"first\", \"pretty_first\". Defaults is \"pretty_all\".\n","\n","- **encoding** : file encoding to apply on normalized documents. Supported encodings are: UTF_8, UTF_16, US_ASCII, ISO-8859-1, UTF-16BE, UTF-16LE. Default is \"UTF-8\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Yv6n-SB6h-na"},"outputs":[],"source":["text = '''\n","  <div id=\"theworldsgreatest\" class='my-right my-hide-small my-wide toptext' style=\"font-family:'Segoe UI',Arial,sans-serif\">\n","    THE WORLD'S LARGEST WEB DEVELOPER SITE\n","    <h1 style=\"font-size:300%;\">THE WORLD'S LARGEST WEB DEVELOPER SITE</h1>\n","    <p style=\"font-size:160%;\">Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum..</p>\n","  </div>\n","\n","</div>'''"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":340,"status":"ok","timestamp":1712243941564,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"yn0-jPkLiAMf","outputId":"f115a086-f0c8-47d0-f3c7-4926e023508a"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n","+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|\\n  <div id=\"theworldsgreatest\" class='my-right my-hide-small my-wide toptext' style=\"font-family:'Segoe UI',Arial,sans-serif\">\\n    THE WORLD'S LARGEST WEB DEVELOPER SITE\\n    <h1 style=\"font-size:300%;\">THE WORLD'S LARGEST WEB DEVELOPER SITE</h1>\\n    <p style=\"font-size:160%;\">Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum..</p>\\n  </div>\\n\\n</div>|\n","+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["spark_df = spark.createDataFrame([[text]]).toDF(\"text\")\n","\n","spark_df.show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712243941565,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"I8u7mO90iCYq","outputId":"568e4448-6e0a-49be-cc59-863742a41599"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='DocumentNormalizer_95e15dc15485', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='action', doc='action to perform applying regex patterns on text'): 'clean',\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='patterns', doc='normalization regex patterns which match will be removed from document. Defaults is <[^>]*>'): ['<[^>]*>'],\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='replacement', doc='replacement string to apply when regexes match'): ' ',\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='lowercase', doc='whether to convert strings to lowercase'): False,\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='policy', doc='policy to remove pattern from text'): 'pretty_all',\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='encoding', doc='file encoding to apply on normalized documents'): 'UTF-8',\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='inputCols', doc='previous annotations columns, if renamed'): ['document'],\n"," Param(parent='DocumentNormalizer_95e15dc15485', name='outputCol', doc='output annotation column. can be left default.'): 'normalizedDocument'}"]},"metadata":{},"execution_count":71}],"source":["documentNormalizer = DocumentNormalizer() \\\n","    .setInputCols(\"document\") \\\n","    .setOutputCol(\"normalizedDocument\")\n","\n","documentNormalizer.extractParamMap()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6uezRQy6iDum"},"outputs":[],"source":["documentAssembler = DocumentAssembler() \\\n","    .setInputCol('text') \\\n","    .setOutputCol('document')\n","\n","#default\n","cleanUpPatterns = [\"<[^>]*>\"]\n","\n","documentNormalizer = DocumentNormalizer() \\\n","    .setInputCols(\"document\") \\\n","    .setOutputCol(\"normalizedDocument\") \\\n","    .setAction(\"clean\") \\\n","    .setPatterns(cleanUpPatterns) \\\n","    .setReplacement(\" \") \\\n","    .setPolicy(\"pretty_all\") \\\n","    .setLowercase(True)\n","\n","docPatternRemoverPipeline = Pipeline() \\\n","    .setStages([documentAssembler,\n","                documentNormalizer])\n","\n","pipelineModel = docPatternRemoverPipeline.fit(spark_df).transform(spark_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":912,"status":"ok","timestamp":1712243942474,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"QBQ07rveiFFp","outputId":"6c335f73-4a6b-4ac5-fb1f-32c27a64983a"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|result                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |\n","+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|[ the world's largest web developer site the world's largest web developer site lorem ipsum is simply dummy text of the printing and typesetting industry. lorem ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. it has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. it was popularised in the 1960s with the release of letraset sheets containing lorem ipsum passages, and more recently with desktop publishing software like aldus pagemaker including versions of lorem ipsum..]|\n","+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["pipelineModel.select('normalizedDocument.result').show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"_Kxd4cCegROX"},"source":[" for more examples : https://sparknlp.org/docs/en/annotators#documentnormalizer"]},{"cell_type":"markdown","metadata":{"id":"g4jmmGZRwdwo"},"source":["## Stopwords Cleaner"]},{"cell_type":"markdown","metadata":{"id":"7fgcop89yIT-"},"source":["This annotator excludes from a sequence of strings (e.g. the output of a Tokenizer, Normalizer, Lemmatizer, and Stemmer) and drops all the stop words from the input sequences.\n","\n","**Functions**:\n","\n","\n","\n","- **setStopWords**      : The words to be filtered out. Array[String]\n","\n","- **setCaseSensitive**   : Whether to do a case sensitive comparison over the stop words.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fVNek15riGbu"},"outputs":[],"source":["stopwords_cleaner = StopWordsCleaner()\\\n","    .setInputCols(\"token\")\\\n","    .setOutputCol(\"cleanTokens\")\\\n","    .setCaseSensitive(False)\\\n","    #.setStopWords([\"no\", \"without\"]) (e.g. read a list of words from a txt)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243942475,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"ViTQDbnsiIPD","outputId":"be9a6e13-127e-4f81-f831-a9f55f20de48"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['i',\n"," 'me',\n"," 'my',\n"," 'myself',\n"," 'we',\n"," 'our',\n"," 'ours',\n"," 'ourselves',\n"," 'you',\n"," 'your',\n"," 'yours',\n"," 'yourself',\n"," 'yourselves',\n"," 'he',\n"," 'him',\n"," 'his',\n"," 'himself',\n"," 'she',\n"," 'her',\n"," 'hers',\n"," 'herself',\n"," 'it',\n"," 'its',\n"," 'itself',\n"," 'they',\n"," 'them',\n"," 'their',\n"," 'theirs',\n"," 'themselves',\n"," 'what',\n"," 'which',\n"," 'who',\n"," 'whom',\n"," 'this',\n"," 'that',\n"," 'these',\n"," 'those',\n"," 'am',\n"," 'is',\n"," 'are',\n"," 'was',\n"," 'were',\n"," 'be',\n"," 'been',\n"," 'being',\n"," 'have',\n"," 'has',\n"," 'had',\n"," 'having',\n"," 'do',\n"," 'does',\n"," 'did',\n"," 'doing',\n"," 'a',\n"," 'an',\n"," 'the',\n"," 'and',\n"," 'but',\n"," 'if',\n"," 'or',\n"," 'because',\n"," 'as',\n"," 'until',\n"," 'while',\n"," 'of',\n"," 'at',\n"," 'by',\n"," 'for',\n"," 'with',\n"," 'about',\n"," 'against',\n"," 'between',\n"," 'into',\n"," 'through',\n"," 'during',\n"," 'before',\n"," 'after',\n"," 'above',\n"," 'below',\n"," 'to',\n"," 'from',\n"," 'up',\n"," 'down',\n"," 'in',\n"," 'out',\n"," 'on',\n"," 'off',\n"," 'over',\n"," 'under',\n"," 'again',\n"," 'further',\n"," 'then',\n"," 'once',\n"," 'here',\n"," 'there',\n"," 'when',\n"," 'where',\n"," 'why',\n"," 'how',\n"," 'all',\n"," 'any',\n"," 'both',\n"," 'each',\n"," 'few',\n"," 'more',\n"," 'most',\n"," 'other',\n"," 'some',\n"," 'such',\n"," 'no',\n"," 'nor',\n"," 'not',\n"," 'only',\n"," 'own',\n"," 'same',\n"," 'so',\n"," 'than',\n"," 'too',\n"," 'very',\n"," 's',\n"," 't',\n"," 'can',\n"," 'will',\n"," 'just',\n"," 'don',\n"," 'should',\n"," 'now',\n"," \"i'll\",\n"," \"you'll\",\n"," \"he'll\",\n"," \"she'll\",\n"," \"we'll\",\n"," \"they'll\",\n"," \"i'd\",\n"," \"you'd\",\n"," \"he'd\",\n"," \"she'd\",\n"," \"we'd\",\n"," \"they'd\",\n"," \"i'm\",\n"," \"you're\",\n"," \"he's\",\n"," \"she's\",\n"," \"it's\",\n"," \"we're\",\n"," \"they're\",\n"," \"i've\",\n"," \"we've\",\n"," \"you've\",\n"," \"they've\",\n"," \"isn't\",\n"," \"aren't\",\n"," \"wasn't\",\n"," \"weren't\",\n"," \"haven't\",\n"," \"hasn't\",\n"," \"hadn't\",\n"," \"don't\",\n"," \"doesn't\",\n"," \"didn't\",\n"," \"won't\",\n"," \"wouldn't\",\n"," \"shan't\",\n"," \"shouldn't\",\n"," \"mustn't\",\n"," \"can't\",\n"," \"couldn't\",\n"," 'cannot',\n"," 'could',\n"," \"here's\",\n"," \"how's\",\n"," \"let's\",\n"," 'ought',\n"," \"that's\",\n"," \"there's\",\n"," \"what's\",\n"," \"when's\",\n"," \"where's\",\n"," \"who's\",\n"," \"why's\",\n"," 'would']"]},"metadata":{},"execution_count":75}],"source":["stopwords_cleaner.getStopWords()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":654,"status":"ok","timestamp":1712243943126,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"g2G8iaSUiNYx","outputId":"98673959-de1f-4726-cd43-9ce7155b5bbc"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","|                                    text|                                document|                                   token|                             cleanTokens|\n","+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","|            Peter is a very good person.|[{document, 0, 27, Peter is a very go...|[{token, 0, 4, Peter, {sentence -> 0}...|[{token, 0, 4, Peter, {sentence -> 0}...|\n","|  My life in Russia is very interesting.|[{document, 0, 37, My life in Russia ...|[{token, 0, 1, My, {sentence -> 0}, [...|[{token, 3, 6, life, {sentence -> 0},...|\n","|John and Peter are brothers. However ...|[{document, 0, 76, John and Peter are...|[{token, 0, 3, John, {sentence -> 0},...|[{token, 0, 3, John, {sentence -> 0},...|\n","|Lucas Nogal Dunbercker is no longer h...|[{document, 0, 67, Lucas Nogal Dunber...|[{token, 0, 4, Lucas, {sentence -> 0}...|[{token, 0, 4, Lucas, {sentence -> 0}...|\n","|Europe is very culture rich. There ar...|[{document, 0, 68, Europe is very cul...|[{token, 0, 5, Europe, {sentence -> 0...|[{token, 0, 5, Europe, {sentence -> 0...|\n","+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","\n"]}],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               tokenizer,\n","                               stopwords_cleaner])\n","\n","spark_df = spark.read.text('./sample-sentences-en.txt').toDF('text')\n","\n","result = nlpPipeline.fit(spark_df).transform(spark_df)\n","\n","result.show(truncate=40)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243943126,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"qcnBmZ0ciOCT","outputId":"4289d333-5ddb-43f6-9a85-8e6550995bae"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['Peter', 'good', 'person', '.'])]"]},"metadata":{},"execution_count":77}],"source":["result.select('cleanTokens.result').take(1)"]},{"cell_type":"markdown","metadata":{"id":"zN9HpW_Lix7_"},"source":["## Token Assembler"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":609,"status":"ok","timestamp":1712243943732,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"GNU17l3-iR2C","outputId":"4ccbacad-8b24-4458-ff1d-617b2a87c960"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|                text|            document|           sentences|               token|          normalized|         cleanTokens|          clean_text|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|Peter is a very g...|[{document, 0, 27...|[{document, 0, 27...|[{token, 0, 4, Pe...|[{token, 0, 4, Pe...|[{token, 0, 4, Pe...|[{document, 0, 16...|\n","|My life in Russia...|[{document, 0, 37...|[{document, 0, 37...|[{token, 0, 1, My...|[{token, 0, 1, My...|[{token, 3, 6, li...|[{document, 0, 22...|\n","|John and Peter ar...|[{document, 0, 76...|[{document, 0, 27...|[{token, 0, 3, Jo...|[{token, 0, 3, Jo...|[{token, 0, 3, Jo...|[{document, 0, 18...|\n","|Lucas Nogal Dunbe...|[{document, 0, 67...|[{document, 0, 41...|[{token, 0, 4, Lu...|[{token, 0, 4, Lu...|[{token, 0, 4, Lu...|[{document, 0, 34...|\n","|Europe is very cu...|[{document, 0, 68...|[{document, 0, 27...|[{token, 0, 5, Eu...|[{token, 0, 5, Eu...|[{token, 0, 5, Eu...|[{document, 0, 18...|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","\n"]}],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = SentenceDetector()\\\n","    .setInputCols(['document'])\\\n","    .setOutputCol('sentences')\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"sentences\"]) \\\n","    .setOutputCol(\"token\")\n","\n","normalizer = Normalizer() \\\n","    .setInputCols([\"token\"]) \\\n","    .setOutputCol(\"normalized\")\\\n","    .setLowercase(False)\\\n","\n","stopwords_cleaner = StopWordsCleaner()\\\n","    .setInputCols(\"normalized\")\\\n","    .setOutputCol(\"cleanTokens\")\\\n","    .setCaseSensitive(False)\\\n","\n","tokenassembler = TokenAssembler()\\\n","    .setInputCols([\"sentences\", \"cleanTokens\"]) \\\n","    .setOutputCol(\"clean_text\")\n","\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               sentenceDetector,\n","                               tokenizer,\n","                               normalizer,\n","                               stopwords_cleaner,\n","                               tokenassembler])\n","\n","result = nlpPipeline.fit(spark_df).transform(spark_df)\n","result.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243943732,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"5OzQ7i00iTzg","outputId":"22c83236-5743-41d0-8af6-a7c4b588a65f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(clean_text=[Row(annotatorType='document', begin=0, end=16, result='Peter good person', metadata={'sentence': '0'}, embeddings=[])])]"]},"metadata":{},"execution_count":79}],"source":["# if we use TokenAssembler().setPreservePosition(True), the original borders will be preserved (dropped & unwanted chars will be replaced by spaces)\n","\n","result.select('clean_text').take(1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":391,"status":"ok","timestamp":1712243944120,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"nuO1kW7ciVaV","outputId":"a17dbefb-584d-4d35-acf6-8660397d3420"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+-----------------------------------+\n","|text                                                                         |clean_text                         |\n","+-----------------------------------------------------------------------------+-----------------------------------+\n","|Peter is a very good person.                                                 |Peter good person                  |\n","|My life in Russia is very interesting.                                       |life Russia interesting            |\n","|John and Peter are brothers. However they don't support each other that much.|John Peter brothers                |\n","|John and Peter are brothers. However they don't support each other that much.|However dont support much          |\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |Lucas Nogal Dunbercker longer happy|\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |good car though                    |\n","|Europe is very culture rich. There are huge churches! and big houses!        |Europe culture rich                |\n","|Europe is very culture rich. There are huge churches! and big houses!        |huge churches                      |\n","|Europe is very culture rich. There are huge churches! and big houses!        |big houses                         |\n","+-----------------------------------------------------------------------------+-----------------------------------+\n","\n"]}],"source":["result.select('text', F.explode(result.clean_text.result).alias('clean_text')).show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":331},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243944120,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"sJ3DIWlxiWqo","outputId":"6c9e6ded-8ba8-45fc-eedd-7a8e2b31a709"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                text  \\\n","0                       Peter is a very good person.   \n","1             My life in Russia is very interesting.   \n","2  John and Peter are brothers. However they don'...   \n","3  John and Peter are brothers. However they don'...   \n","4  Lucas Nogal Dunbercker is no longer happy. He ...   \n","5  Lucas Nogal Dunbercker is no longer happy. He ...   \n","6  Europe is very culture rich. There are huge ch...   \n","7  Europe is very culture rich. There are huge ch...   \n","8  Europe is very culture rich. There are huge ch...   \n","\n","                            clean_text  \n","0                    Peter good person  \n","1              life Russia interesting  \n","2                  John Peter brothers  \n","3            However dont support much  \n","4  Lucas Nogal Dunbercker longer happy  \n","5                      good car though  \n","6                  Europe culture rich  \n","7                        huge churches  \n","8                           big houses  "],"text/html":["\n","  <div id=\"df-b137062e-353d-4bc6-bbc4-dc360719be95\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>clean_text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Peter is a very good person.</td>\n","      <td>Peter good person</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>My life in Russia is very interesting.</td>\n","      <td>life Russia interesting</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>John and Peter are brothers. However they don'...</td>\n","      <td>John Peter brothers</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>John and Peter are brothers. However they don'...</td>\n","      <td>However dont support much</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Lucas Nogal Dunbercker is no longer happy. He ...</td>\n","      <td>Lucas Nogal Dunbercker longer happy</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Lucas Nogal Dunbercker is no longer happy. He ...</td>\n","      <td>good car though</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Europe is very culture rich. There are huge ch...</td>\n","      <td>Europe culture rich</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Europe is very culture rich. There are huge ch...</td>\n","      <td>huge churches</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Europe is very culture rich. 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However they don't support each other that much.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"huge churches\",\n          \"life Russia interesting\",\n          \"good car though\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":81}],"source":["result.select('text', F.explode(result.clean_text.result).alias('clean_text')).toPandas()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":700,"status":"ok","timestamp":1712243944817,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"ccfVGnh2iYRI","outputId":"5df55e0e-f536-429b-f84d-d20910cbfbed"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----+---+-----------------------------------+--------+\n","|begin|end|result                             |sentence|\n","+-----+---+-----------------------------------+--------+\n","|0    |16 |Peter good person                  |0       |\n","|0    |22 |life Russia interesting            |0       |\n","|0    |18 |John Peter brothers                |0       |\n","|29   |53 |However dont support much          |1       |\n","|0    |34 |Lucas Nogal Dunbercker longer happy|0       |\n","|43   |57 |good car though                    |1       |\n","|0    |18 |Europe culture rich                |0       |\n","|29   |41 |huge churches                      |1       |\n","|54   |63 |big houses                         |2       |\n","+-----+---+-----------------------------------+--------+\n","\n"]}],"source":["import pyspark.sql.functions as F\n","\n","result.withColumn(\n","    \"tmp\",\n","    F.explode(\"clean_text\")) \\\n","    .select(\"tmp.*\").select(\"begin\",\"end\",\"result\",\"metadata.sentence\").show(truncate = False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":530,"status":"ok","timestamp":1712243945346,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"rgCELPE8iZu4","outputId":"fbfaacac-a4cf-4460-fd22-6937654101b9"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------------------------------------------------------------------------+-----------------------------------------------------+\n","|text                                                                         |result                                               |\n","+-----------------------------------------------------------------------------+-----------------------------------------------------+\n","|Peter is a very good person.                                                 |[Peter good person]                                  |\n","|My life in Russia is very interesting.                                       |[life Russia interesting]                            |\n","|John and Peter are brothers. However they don't support each other that much.|[John Peter brothers However dont support much]      |\n","|Lucas Nogal Dunbercker is no longer happy. He has a good car though.         |[Lucas Nogal Dunbercker longer happy good car though]|\n","|Europe is very culture rich. There are huge churches! and big houses!        |[Europe culture rich huge churches big houses]       |\n","+-----------------------------------------------------------------------------+-----------------------------------------------------+\n","\n"]}],"source":["# if we hadn't used Sentence Detector, this would be what we got. (tokenizer gets document instead of sentences column)\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")\n","\n","tokenassembler = TokenAssembler()\\\n","    .setInputCols([\"document\", \"cleanTokens\"]) \\\n","    .setOutputCol(\"clean_text\")\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               tokenizer,\n","                               normalizer,\n","                               stopwords_cleaner,\n","                               tokenassembler])\n","\n","result = nlpPipeline.fit(spark_df).transform(spark_df)\n","result.select('text', 'clean_text.result').show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712243945346,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"bADrD-atibdN","outputId":"248e56ae-8f93-4aec-e031-8bff3cf5d7ad"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----+---+---------------------------------------------------+--------+\n","|begin|end|result                                             |sentence|\n","+-----+---+---------------------------------------------------+--------+\n","|0    |16 |Peter good person                                  |0       |\n","|0    |22 |life Russia interesting                            |0       |\n","|0    |44 |John Peter brothers However dont support much      |0       |\n","|0    |50 |Lucas Nogal Dunbercker longer happy good car though|0       |\n","|0    |43 |Europe culture rich huge churches big houses       |0       |\n","+-----+---+---------------------------------------------------+--------+\n","\n"]}],"source":["result.withColumn(\n","    \"tmp\",\n","    F.explode(\"clean_text\")) \\\n","    .select(\"tmp.*\").select(\"begin\",\"end\",\"result\",\"metadata.sentence\").show(truncate = False)"]},{"cell_type":"markdown","metadata":{"id":"iPrXwVA9GCCS"},"source":["**IMPORTANT NOTE:**\n","\n","If you have some other steps & annotators in your pipeline that will need to use the tokens from cleaned text (assembled tokens), you will need to tokenize the processed text again as the original text is probably changed completely."]},{"cell_type":"markdown","metadata":{"id":"XO_ZWY2z1Ka6"},"source":["## Stemmer"]},{"cell_type":"markdown","metadata":{"id":"aE2PEX0x1NgQ"},"source":["Returns hard-stems out of words with the objective of retrieving the meaningful part of the word\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kNEnn1BPic5O"},"outputs":[],"source":["stemmer = Stemmer() \\\n","    .setInputCols([\"token\"]) \\\n","    .setOutputCol(\"stem\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243945347,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"_q1bQsJgifN0","outputId":"9a4a7fa4-6e36-4179-d5bb-44e43ec53806"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","|                                    text|                                document|                                   token|                                    stem|\n","+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","|            Peter is a very good person.|[{document, 0, 27, Peter is a very go...|[{token, 0, 4, Peter, {sentence -> 0}...|[{token, 0, 4, peter, {sentence -> 0}...|\n","|  My life in Russia is very interesting.|[{document, 0, 37, My life in Russia ...|[{token, 0, 1, My, {sentence -> 0}, [...|[{token, 0, 1, my, {sentence -> 0}, [...|\n","|John and Peter are brothers. However ...|[{document, 0, 76, John and Peter are...|[{token, 0, 3, John, {sentence -> 0},...|[{token, 0, 3, john, {sentence -> 0},...|\n","|Lucas Nogal Dunbercker is no longer h...|[{document, 0, 67, Lucas Nogal Dunber...|[{token, 0, 4, Lucas, {sentence -> 0}...|[{token, 0, 4, luca, {sentence -> 0},...|\n","|Europe is very culture rich. There ar...|[{document, 0, 68, Europe is very cul...|[{token, 0, 5, Europe, {sentence -> 0...|[{token, 0, 5, europ, {sentence -> 0}...|\n","+----------------------------------------+----------------------------------------+----------------------------------------+----------------------------------------+\n","\n"]}],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               tokenizer,\n","                               stemmer])\n","\n","result = nlpPipeline.fit(spark_df).transform(spark_df)\n","\n","result.show(truncate=40)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":588,"status":"ok","timestamp":1712243945932,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"16mmUbpcigiq","outputId":"9dd30b57-36f1-4db7-cc12-b4c736b1bda0"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------------------------------------------------------------------------------------+\n","|result                                                                                     |\n","+-------------------------------------------------------------------------------------------+\n","|[peter, i, a, veri, good, person, .]                                                       |\n","|[my, life, in, russia, i, veri, interest, .]                                               |\n","|[john, and, peter, ar, brother, ., howev, thei, don't, support, each, other, that, much, .]|\n","|[luca, nogal, dunberck, i, no, longer, happi, ., he, ha, a, good, car, though, .]          |\n","|[europ, i, veri, cultur, rich, ., there, ar, huge, church, !, and, big, hous, !]           |\n","+-------------------------------------------------------------------------------------------+\n","\n"]}],"source":["result.select('stem.result').show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1712243945932,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"674Zlg78iiOe","outputId":"ee0f870f-fc6b-4b57-a638-b22dcdca9598"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    token    stem\n","0   Peter   peter\n","1      is       i\n","2       a       a\n","3    very    veri\n","4    good    good\n","5  person  person\n","6       .       .\n","7      My      my\n","8    life    life\n","9      in      in"],"text/html":["\n","  <div id=\"df-40fbb540-dd30-4882-a774-bae0a483a1c8\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type 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F\n","\n","result_df = result.select(F.explode(F.arrays_zip(result.token.result,\n","                                                 result.stem.result)).alias(\"cols\")) \\\n","                  .select(F.expr(\"cols['0']\").alias(\"token\"),\n","                          F.expr(\"cols['1']\").alias(\"stem\")).toPandas()\n","\n","result_df.head(10)"]},{"cell_type":"markdown","metadata":{"id":"On3UrCoM2RFQ"},"source":["## Lemmatizer"]},{"cell_type":"markdown","metadata":{"id":"tdyzaii374go"},"source":["|index|model|index|model|index|model|index|model|\n","|-----:|:-----|-----:|:-----|-----:|:-----|-----:|:-----|\n","| 1| [lemma_antbnc](https://nlp.johnsnowlabs.com/2021/11/22/lemma_antbnc_en.html)  | 2| [lemma_atis](https://nlp.johnsnowlabs.com/2022/03/31/lemma_atis_en_3_0.html)  | 3| [lemma_esl](https://nlp.johnsnowlabs.com/2022/03/31/lemma_esl_en_3_0.html)  | 4| [lemma_ewt](https://nlp.johnsnowlabs.com/2022/05/01/lemma_ewt_en_3_0.html)  |\n","| 5| [lemma_gum](https://nlp.johnsnowlabs.com/2022/03/31/lemma_gum_en_3_0.html)  | 6| [lemma_lines](https://nlp.johnsnowlabs.com/2022/05/01/lemma_lines_en_3_0.html)  | 7| [lemma_partut](https://nlp.johnsnowlabs.com/2022/03/31/lemma_partut_en_3_0.html)  | 8| [lemma_spacylookup](https://nlp.johnsnowlabs.com/2022/03/08/lemma_spacylookup_en_3_0.html)  |\n"]},{"cell_type":"markdown","metadata":{"id":"onCYFXGO2VSk"},"source":["Retrieves lemmas out of words with the objective of returning a base dictionary word"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ubap85gTikAE"},"outputs":[],"source":["!wget -q https://raw.githubusercontent.com/mahavivo/vocabulary/master/lemmas/AntBNC_lemmas_ver_001.txt"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ZnR4pjdiluD"},"outputs":[],"source":["lemmatizer = Lemmatizer() \\\n","    .setInputCols([\"token\"]) \\\n","    .setOutputCol(\"lemma\") \\\n","    .setDictionary(\"./AntBNC_lemmas_ver_001.txt\", value_delimiter =\"\\t\", key_delimiter = \"->\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712243945932,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"tguwcQ8vinMc","outputId":"38a5c631-21ff-4c9e-da5c-828538b55b25"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='Lemmatizer_536fa9864f98', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='Lemmatizer_536fa9864f98', name='formCol', doc='Column that correspends to CoNLLU(formCol=) output'): 'form',\n"," Param(parent='Lemmatizer_536fa9864f98', name='lemmaCol', doc='Column that correspends to CoNLLU(lemmaCol=) output'): 'lemma',\n"," Param(parent='Lemmatizer_536fa9864f98', name='inputCols', doc='previous annotations columns, if renamed'): ['token'],\n"," Param(parent='Lemmatizer_536fa9864f98', name='outputCol', doc='output annotation column. can be left default.'): 'lemma',\n"," Param(parent='Lemmatizer_536fa9864f98', name='dictionary', doc=\"lemmatizer external dictionary. needs 'keyDelimiter' and 'valueDelimiter' in options for parsing target text\"): JavaObject id=o2747}"]},"metadata":{},"execution_count":91}],"source":["lemmatizer.extractParamMap()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1788,"status":"ok","timestamp":1712243947717,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"gH-N7dHlipJm","outputId":"65211270-09ff-4131-df4a-8b01c727b376"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|                text|            document|               token|                stem|               lemma|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|Peter is a very g...|[{document, 0, 27...|[{token, 0, 4, Pe...|[{token, 0, 4, pe...|[{token, 0, 4, Pe...|\n","|My life in Russia...|[{document, 0, 37...|[{token, 0, 1, My...|[{token, 0, 1, my...|[{token, 0, 1, My...|\n","|John and Peter ar...|[{document, 0, 76...|[{token, 0, 3, Jo...|[{token, 0, 3, jo...|[{token, 0, 3, Jo...|\n","|Lucas Nogal Dunbe...|[{document, 0, 67...|[{token, 0, 4, Lu...|[{token, 0, 4, lu...|[{token, 0, 4, Lu...|\n","|Europe is very cu...|[{document, 0, 68...|[{token, 0, 5, Eu...|[{token, 0, 5, eu...|[{token, 0, 5, Eu...|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+\n","\n"]}],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")\n","\n","stemmer = Stemmer() \\\n","    .setInputCols([\"token\"]) \\\n","    .setOutputCol(\"stem\")\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               tokenizer,\n","                               stemmer,\n","                               lemmatizer])\n","\n","result = nlpPipeline.fit(spark_df).transform(spark_df)\n","result.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712243947717,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"6GqhwkkRirEQ","outputId":"f54cfbc6-8460-4f6f-f05e-7297c0b9f03c"},"outputs":[{"output_type":"stream","name":"stdout","text":["+---------------------------------------------------------------------------------------------+\n","|result                                                                                       |\n","+---------------------------------------------------------------------------------------------+\n","|[Peter, be, a, very, good, person, .]                                                        |\n","|[My, life, in, Russia, be, very, interest, .]                                                |\n","|[John, and, Peter, be, brother, ., However, they, don't, support, each, other, that, much, .]|\n","|[Lucas, Nogal, Dunbercker, be, no, long, happy, ., He, have, a, good, car, though, .]        |\n","|[Europe, be, very, culture, rich, ., There, be, huge, church, !, and, big, house, !]         |\n","+---------------------------------------------------------------------------------------------+\n","\n"]}],"source":["result.select('lemma.result').show(truncate=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"elapsed":557,"status":"ok","timestamp":1712243948272,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"uxV8YjOLispW","outputId":"6ced5781-db91-44de-c3f8-7bf3cdca386c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    token    stem   lemma\n","0   Peter   peter   Peter\n","1      is       i      be\n","2       a       a       a\n","3    very    veri    very\n","4    good    good    good\n","5  person  person  person\n","6       .       .       .\n","7      My      my      My\n","8    life    life    life\n","9      in      in      in"],"text/html":["\n","  <div id=\"df-71a3e8ae-c8de-43a7-89bd-ceedee414393\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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The output will consist of a sequence of n-grams where each n-gram is represented by a space-delimited string of n consecutive words with annotatorType `CHUNK` same as the Chunker annotator.\n","\n","Functions:\n","\n","`setN:` number elements per n-gram (>=1)\n","\n","`setEnableCumulative:` whether to calculate just the actual n-grams or all n-grams from 1 through n\n","\n","`setDelimiter:` Glue character used to join the tokens"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1712243948272,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"kN80hwztiu2S","outputId":"e50af13f-a8b6-499d-a419-0cb900353f22"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|                                                                                                                                                                                                  result|\n","+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|                                    [Peter, is, a, very, good, person, ., Peter_is, is_a, a_very, very_good, good_person, person_., Peter_is_a, is_a_very, a_very_good, very_good_person, good_person_.]|\n","|[My, life, in, Russia, is, very, interesting, ., My_life, life_in, in_Russia, Russia_is, is_very, very_interesting, interesting_., My_life_in, life_in_Russia, in_Russia_is, Russia_is_very, is_very_...|\n","|[John, and, Peter, are, brothers, ., However, they, don't, support, each, other, that, much, ., John_and, and_Peter, Peter_are, are_brothers, brothers_., ._However, However_they, they_don't, don't_...|\n","|[Lucas, Nogal, Dunbercker, is, no, longer, happy, ., He, has, a, good, car, though, ., Lucas_Nogal, Nogal_Dunbercker, Dunbercker_is, is_no, no_longer, longer_happy, happy_., ._He, He_has, has_a, a_...|\n","|[Europe, is, very, culture, rich, ., There, are, huge, churches, !, and, big, houses, !, Europe_is, is_very, very_culture, culture_rich, rich_., ._There, There_are, are_huge, huge_churches, churche...|\n","+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["ngrams_cum = NGramGenerator() \\\n","                .setInputCols([\"token\"]) \\\n","                .setOutputCol(\"ngrams\") \\\n","                .setN(3) \\\n","                .setEnableCumulative(True)\\\n","                .setDelimiter(\"_\") # Default is space\n","\n","# .setN(3) means, take bigrams and trigrams.\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               tokenizer,\n","                               ngrams_cum])\n","\n","result = nlpPipeline.fit(spark_df).transform(spark_df)\n","result.select('ngrams.result').show(truncate=200)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1712243948273,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"Zpyoy_vIiwP_","outputId":"d6b13963-e4c1-4bf0-8ef0-f8be510a30f8"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|                                                                                                                                                                                                  result|\n","+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","|                                                                                                                                   [Peter_is_a, is_a_very, a_very_good, very_good_person, good_person_.]|\n","|                                                                                                     [My_life_in, life_in_Russia, in_Russia_is, Russia_is_very, is_very_interesting, very_interesting_.]|\n","|[John_and_Peter, and_Peter_are, Peter_are_brothers, are_brothers_., brothers_._However, ._However_they, However_they_don't, they_don't_support, don't_support_each, support_each_other, each_other_th...|\n","|   [Lucas_Nogal_Dunbercker, Nogal_Dunbercker_is, Dunbercker_is_no, is_no_longer, no_longer_happy, longer_happy_., happy_._He, ._He_has, He_has_a, has_a_good, a_good_car, good_car_though, car_though_.]|\n","|[Europe_is_very, is_very_culture, very_culture_rich, culture_rich_., rich_._There, ._There_are, There_are_huge, are_huge_churches, huge_churches_!, churches_!_and, !_and_big, and_big_houses, big_ho...|\n","+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["ngrams_nonCum = NGramGenerator() \\\n","                  .setInputCols([\"token\"]) \\\n","                  .setOutputCol(\"ngrams_v2\") \\\n","                  .setN(3) \\\n","                  .setEnableCumulative(False)\\\n","                  .setDelimiter(\"_\") # Default is space\n","\n","ngrams_nonCum.transform(result).select('ngrams_v2.result').show(truncate=200)"]},{"cell_type":"markdown","metadata":{"id":"C55M47nKCL3E"},"source":["## TextMatcher"]},{"cell_type":"markdown","metadata":{"id":"8hLITrkJICKO"},"source":["Annotator to match entire phrases (by token) provided in a file against a Document\n","\n","Functions:\n","\n","`setEntities(path, format, options)`: Provides a file with phrases to match. Default: Looks up path in configuration.\n","\n","`path`: a path to a file that contains the entities in the specified format.\n","\n","`readAs`: the format of the file, can be one of {ReadAs.LINE_BY_LINE, ReadAs.SPARK_DATASET}. Defaults to LINE_BY_LINE.\n","\n","`options`: a map of additional parameters. Defaults to {“format”: “text”}.\n","\n","`entityValue` : Value for the entity metadata field to indicate which chunk comes from which textMatcher when there are multiple textMatchers.\n","\n","`mergeOverlapping` : whether to merge overlapping matched chunks. Defaults false\n","\n","`caseSensitive` : whether to match regardless of case. Defaults true\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1712243948274,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"OsMlW9wEixh3","outputId":"846e7deb-ab39-4a7b-d300-e63c83c8fbdb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='TextMatcher_33f9b016a543', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='TextMatcher_33f9b016a543', name='inputCols', doc='previous annotations columns, if renamed'): ['document',\n","  'token'],\n"," Param(parent='TextMatcher_33f9b016a543', name='caseSensitive', doc='whether to match regardless of case. Defaults true'): True,\n"," Param(parent='TextMatcher_33f9b016a543', name='mergeOverlapping', doc='whether to merge overlapping matched chunks. Defaults false'): False,\n"," Param(parent='TextMatcher_33f9b016a543', name='outputCol', doc='output annotation column. can be left default.'): 'matched_entities'}"]},"metadata":{},"execution_count":97}],"source":["entity_extractor = TextMatcher() \\\n","    .setInputCols([\"document\",'token'])\\\n","    .setOutputCol(\"matched_entities\")\\\n","\n","entity_extractor.extractParamMap()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YMZUitjwiy9a"},"outputs":[],"source":["! wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Public/data/news_category_train.csv\n","\n","news_df = spark.read \\\n","            .option(\"header\", True) \\\n","            .csv(\"news_category_train.csv\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":977,"status":"ok","timestamp":1712243951173,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"qsiDBr7Wi0RB","outputId":"7ebc32fa-d059-4787-e64b-f878d05ad9b0"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------+--------------------------------------------------+\n","|category|                                       description|\n","+--------+--------------------------------------------------+\n","|Business| Short sellers, Wall Street's dwindling band of...|\n","|Business| Private investment firm Carlyle Group, which h...|\n","|Business| Soaring crude prices plus worries about the ec...|\n","|Business| Authorities have halted oil export flows from ...|\n","|Business| Tearaway world oil prices, toppling records an...|\n","+--------+--------------------------------------------------+\n","only showing top 5 rows\n","\n"]}],"source":["news_df.show(5, truncate=50)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ScBS8Aeui11o"},"outputs":[],"source":["# write the target entities to txt file\n","\n","entities = ['Wall Street', 'USD', 'stock', 'NYSE']\n","with open ('financial_entities.txt', 'w') as f:\n","    for i in entities:\n","        f.write(i+'\\n')\n","\n","\n","entities = ['soccer', 'world cup', 'Messi', 'FC Barcelona']\n","with open ('sport_entities.txt', 'w') as f:\n","    for i in entities:\n","        f.write(i+'\\n')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"G0l9yfCki3F8"},"outputs":[],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"description\")\\\n","    .setOutputCol(\"document\")\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")\n","\n","financial_entity_extractor = TextMatcher() \\\n","    .setInputCols([\"document\",'token'])\\\n","    .setOutputCol(\"financial_entities\")\\\n","    .setEntities(\"financial_entities.txt\")\\\n","    .setCaseSensitive(False)\\\n","    .setEntityValue('financial_entity')\n","\n","sport_entity_extractor = TextMatcher() \\\n","    .setInputCols([\"document\",'token'])\\\n","    .setOutputCol(\"sport_entities\")\\\n","    .setEntities(\"sport_entities.txt\")\\\n","    .setCaseSensitive(False)\\\n","    .setEntityValue('sport_entity')\n","\n","nlpPipeline = Pipeline(\n","    stages=[\n","        documentAssembler,\n","        tokenizer,\n","        financial_entity_extractor,\n","        sport_entity_extractor\n","    ])\n","\n","result = nlpPipeline.fit(news_df).transform(news_df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":492,"status":"ok","timestamp":1712243951663,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"},"user_tz":-120},"id":"xqrUmqxdi4uK","outputId":"04c3b7d7-6c99-4d3c-8f0e-10a488191711"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=[], result=[]),\n"," Row(result=[], result=[]),\n"," Row(result=['stock'], result=[])]"]},"metadata":{},"execution_count":102}],"source":["result.select('financial_entities.result','sport_entities.result').take(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-Y6Eb-DOi6aS","executionInfo":{"status":"ok","timestamp":1712243954499,"user_tz":-120,"elapsed":2838,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"aefec1a2-fd58-4378-e8ba-2379cb75c446"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------+----------------------------------+-------------------+\n","|                                                                  text|                 financial_matches|      sport_matches|\n","+----------------------------------------------------------------------+----------------------------------+-------------------+\n","|\"Company launched the biggest electronic auction of stock in Wall S...|              [stock, Wall Street]|                 []|\n","|Google, Inc. significantly cut the expected share price for its ini...|                    [stock, stock]|                 []|\n","|Google, Inc. significantly cut the expected share price this mornin...|                    [stock, stock]|                 []|\n","| Shares of Air Canada  (AC.TO) fell by more than half on Wednesday,...|                    [Stock, stock]|                 []|\n","|Stock prices are lower in moderate trading. The Dow Jones Industria...|                    [Stock, Stock]|                 []|\n","|The bad news just keeps pouring in for mutual fund manager Janus Ca...|                      [NYSE, NYSE]|                 []|\n","|  Shaun Wright Phillips scored in his international debut as Englan...|                                []|[soccer, World Cup]|\n","|NEWCASTLE, ENGLAND - England deservedly beat Ukraine 3-0 today in t...|                                []|[soccer, World Cup]|\n","|MONTREAL (Reuters) - Shares of Air Canada (AC.TO: Quote, Profile, R...|                    [Stock, stock]|                 []|\n","|\"SAN JOSE, California - On the cusp of its voyage into public tradi...|[stock, Wall Street, stock, Stock]|                 []|\n","|\"Shortly before noon today, Google Inc. stock began trading under t...|                    [stock, stock]|                 []|\n","|roundup Plus: EA to take World Cup soccer to Xbox...IBM chalks up t...|                                []|[World Cup, soccer]|\n","|The U.S. Securities and Exchange Commission yesterday approved Goog...|                    [stock, stock]|                 []|\n","|After a bumpy ride toward becoming a publicly traded company, Googl...|                    [stock, stock]|                 []|\n","|In the most highly anticipated Wall Street debut since the heady da...|              [Wall Street, stock]|                 []|\n","|NEW YORK Despite voluble skepticism among investors, Google #39;s s...|                    [stock, stock]|                 []|\n","|If only the rest of my investments worked out this way. One week ag...|                    [stock, stock]|                 []|\n","| U.S. stocks to watch: GOOGLE INC. (GOOG.O) Google shares jumped 18...|                    [stock, stock]|                 []|\n","|\" U.S. stocks to watch: GOOGLE INC.  &lt;A HREF=\"\"http://www.invest...|                    [stock, stock]|                 []|\n","|roundup Plus: KDE updates Linux desktop...EA to take World Cup socc...|                                []|[World Cup, soccer]|\n","+----------------------------------------------------------------------+----------------------------------+-------------------+\n","only showing top 20 rows\n","\n"]}],"source":["result.select('description','financial_entities.result','sport_entities.result')\\\n","      .toDF('text','financial_matches','sport_matches').filter((F.size('financial_matches')>1) | (F.size('sport_matches')>1))\\\n","      .show(truncate=70)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nWKsmT1fi7_Y","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1712243963223,"user_tz":-120,"elapsed":8726,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"30e6c873-ea4c-4e7b-cf2f-8014a83d806e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["  clinical_entities  begin  end\n","0             stock    112  116\n","1             stock    114  118\n","2             stock     45   49\n","3             stock    126  130\n","4             stock    188  192\n","5             stock     52   56\n","6       Wall Street     61   71\n","7             stock     70   74\n","8             stock    143  147\n","9             stock    294  298"],"text/html":["\n","  <div id=\"df-30519fef-df98-40d7-a6a0-bc9b3352530e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr 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<td>stock</td>\n","      <td>188</td>\n","      <td>192</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>stock</td>\n","      <td>52</td>\n","      <td>56</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Wall Street</td>\n","      <td>61</td>\n","      <td>71</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>stock</td>\n","      <td>70</td>\n","      <td>74</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>stock</td>\n","      <td>143</td>\n","      <td>147</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>stock</td>\n","      <td>294</td>\n","      <td>298</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30519fef-df98-40d7-a6a0-bc9b3352530e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","        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\"num_unique_values\": 315,\n        \"samples\": [\n          170,\n          113,\n          210\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":104}],"source":["result_df = result.select(F.explode(F.arrays_zip(result.financial_entities.result,\n","                                                 result.financial_entities.begin,\n","                                                 result.financial_entities.end)).alias(\"cols\")) \\\n","                  .select(F.expr(\"cols['0']\").alias(\"clinical_entities\"),\n","                          F.expr(\"cols['1']\").alias(\"begin\"),\n","                          F.expr(\"cols['2']\").alias(\"end\")).toPandas()\n","\n","result_df.head(10)"]},{"cell_type":"markdown","metadata":{"id":"Zvi6eTthoEXg"},"source":["## RegexMatcher"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BPJBUNXei9w-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243965044,"user_tz":-120,"elapsed":1831,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"af166d01-e2c6-42b2-e06b-3d865ffaa37e"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------+\n","|                                              text|\n","+--------------------------------------------------+\n","|The human KCNJ9 (Kir 3.3, GIRK3) is a member of...|\n","|BACKGROUND: At present, it is one of the most i...|\n","|OBJECTIVE: To investigate the relationship betw...|\n","|Combined EEG/fMRI recording has been used to lo...|\n","|Kohlschutter syndrome is a rare neurodegenerati...|\n","|Statistical analysis of neuroimages is commonly...|\n","|The synthetic DOX-LNA conjugate was characteriz...|\n","|Our objective was to compare three different me...|\n","|We conducted a phase II study to assess the eff...|\n","|\"Monomeric sarcosine oxidase (MSOX) is a flavoe...|\n","|We presented the tachinid fly Exorista japonica...|\n","|The literature dealing with the water conductin...|\n","|A novel approach to synthesize chitosan-O-isopr...|\n","|An HPLC-ESI-MS-MS method has been developed for...|\n","|The localizing and lateralizing values of eye a...|\n","|OBJECTIVE: To evaluate the effectiveness and ac...|\n","|For the construction of new combinatorial libra...|\n","|We report the results of a screen for genetic a...|\n","|Intraparenchymal pericatheter cyst is rarely re...|\n","|It is known that patients with Klinefelter's sy...|\n","+--------------------------------------------------+\n","only showing top 20 rows\n","\n"]}],"source":["! wget -q\thttps://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/pubmed/pubmed-sample.csv\n","\n","pubMedDF = spark.read\\\n","              .option(\"header\", \"true\")\\\n","              .csv(\"./pubmed-sample.csv\")\\\n","              .filter(\"AB IS NOT null\")\\\n","              .withColumnRenamed(\"AB\", \"text\")\\\n","              .drop(\"TI\")\n","\n","pubMedDF.show(truncate=50)"]},{"cell_type":"markdown","metadata":{"id":"NrUltUspdSYi"},"source":["**setExternalRules**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dGFJG_phi_d4"},"outputs":[],"source":["rules = '''\n","renal\\s\\w+, started with 'renal'\n","cardiac\\s\\w+, started with 'cardiac'\n","\\w*ly\\b, ending with 'ly'\n","\\S*\\d+\\S*, match any word that contains numbers\n","(\\d+).?(\\d*)\\s*(mg|ml|g), match medication metrics\n","'''\n","\n","with open('regex_rules.txt', 'w') as f:\n","\n","    f.write(rules)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KWE5n8fEjAya","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243965045,"user_tz":-120,"elapsed":9,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"733acfc4-e12d-4886-ea78-40fbfac49ace"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='RegexMatcher_ac4cd424f3e0', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='RegexMatcher_ac4cd424f3e0', name='strategy', doc='MATCH_FIRST|MATCH_ALL|MATCH_COMPLETE'): 'MATCH_ALL'}"]},"metadata":{},"execution_count":107}],"source":["RegexMatcher().extractParamMap()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6WgE12BCjC6L","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243965045,"user_tz":-120,"elapsed":7,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"d9a05773-d76d-4066-c973-dcbf9b3262bf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(result=['inwardly', 'family', 'spansapproximately', 'byapproximately', 'approximately', 'respectively', 'poly', 'KCNJ9', '3.3,', 'GIRK3)', 'KCNJ9', '1q21-23', '7.6', '2.2', '2.6', 'identified14', 'aVal366Ala', '8', 'KCNJ9', 'KCNJ9', '9 g']),\n"," Row(result=['previously', 'previously', 'intravenously', 'previously', '25', 'mg/m(2)', '1', '8', 'a3', '50', '20.0%', '(10', '50;', '95%', 'interval,10.0-33.7%).', '58.0%', '[10', '18', '50].', '(50%', '115.0', '17.3%', '52).', '25 mg']),\n"," Row(result=['renal failure', 'cardiac surgery', 'cardiac surgery', 'cardiac surgical', 'early', 'statistically', 'analy', '1995', '2005', '=9796).', '2.9', '11years).', '11.3%', '1105),', '7.2%', '30%', '0.0001),', '1.55,95%', '1.42-1.70,', '0.0001).'])]"]},"metadata":{},"execution_count":108}],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","regex_matcher = RegexMatcher()\\\n","    .setInputCols('document')\\\n","    .setStrategy(\"MATCH_ALL\")\\\n","    .setOutputCol(\"regex_matches\")\\\n","    .setExternalRules(path='./regex_rules.txt', delimiter=',')\n","\n","\n","nlpPipeline = Pipeline(\n","    stages=[\n","        documentAssembler,\n","        regex_matcher\n","        ])\n","\n","match_df = nlpPipeline.fit(pubMedDF).transform(pubMedDF)\n","match_df.select('regex_matches.result').take(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"phylwIokjEIK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243965045,"user_tz":-120,"elapsed":5,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"f4e7297f-13d3-4259-eb1c-f12e7220d989"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------+----------------------------------------------------------------------+\n","|                                                                  text|                                                               matches|\n","+----------------------------------------------------------------------+----------------------------------------------------------------------+\n","|The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activ...|[inwardly, family, spansapproximately, byapproximately, approximate...|\n","|BACKGROUND: At present, it is one of the most important issues for ...|[previously, previously, intravenously, previously, 25, mg/m(2), 1,...|\n","|OBJECTIVE: To investigate the relationship between preoperative atr...|[renal failure, cardiac surgery, cardiac surgery, cardiac surgical,...|\n","|Combined EEG/fMRI recording has been used to localize the generator...|[normally, significantly, effectively, analy, only, considerably, 2...|\n","|Statistical analysis of neuroimages is commonly approached with int...|[analy, commonly, overly, normally, thatsuccessfully, recently, ana...|\n","|The synthetic DOX-LNA conjugate was characterized by proton nuclear...|                                             [wasanaly, substantially]|\n","|Our objective was to compare three different methods of blood press...|[daily, only, Conversely, Hourly, hourly, Hourly, hourly, hourly, h...|\n","|We conducted a phase II study to assess the efficacy and tolerabili...|[analy, respectively, generally, 5-fluorouracil, (5-FU)-, 5-FU-base...|\n","|\"Monomeric sarcosine oxidase (MSOX) is a flavoenzyme that catalyzes...|[cataly, methylgly, gly, ethylgly, dimethylgly, spectrally, practic...|\n","|We presented the tachinid fly Exorista japonica with moving host mo...|                                             [fly, fly, fly, fly, fly]|\n","+----------------------------------------------------------------------+----------------------------------------------------------------------+\n","only showing top 10 rows\n","\n"]}],"source":["match_df.select('text','regex_matches.result')\\\n","        .toDF('text','matches').filter(F.size('matches')>1)\\\n","        .show(10, truncate=70)"]},{"cell_type":"markdown","metadata":{"id":"DW8MHp-FoMLF"},"source":["**setRules**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mQ8Rd8uijFg6"},"outputs":[],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","regexmatcher = RegexMatcher()\\\n","    .setRules([\"\\\\d{4}\\\\/\\\\d\\\\d\\\\/\\\\d\\\\d,date\", \"\\\\d{2}\\\\/\\\\d\\\\d\\\\/\\\\d\\\\d,short_date\"])\\\n","    .setDelimiter(\",\") \\\n","    .setInputCols('document')\\\n","    .setStrategy(\"MATCH_ALL\")\\\n","    .setOutputCol(\"regex_matches\")\n","\n","\n","nlpPipeline = Pipeline(\n","    stages=[\n","        documentAssembler,\n","        regexmatcher\n","        ])\n","\n","match_df2 = nlpPipeline.fit(pubMedDF).transform(pubMedDF)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VzDHmpOWjGrb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243965459,"user_tz":-120,"elapsed":417,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"65894f03-b5bc-4ad7-f395-5124b20baced"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------+--------------------+\n","|                                                                  text|             matches|\n","+----------------------------------------------------------------------+--------------------+\n","|BACKGROUND CONTEXT: In the last decade, the number of patients unde...|[64/63/65, 27/24/27]|\n","+----------------------------------------------------------------------+--------------------+\n","\n"]}],"source":["match_df2.select('text','regex_matches.result')\\\n","        .toDF('text','matches').filter(F.size('matches')>1)\\\n","        .show(10, truncate=70)"]},{"cell_type":"markdown","metadata":{"id":"g5tfkArf0XQT"},"source":["## MultiDateMatcher"]},{"cell_type":"markdown","metadata":{"id":"mY7q_7zl9HR5"},"source":["Extract exact & normalize dates from relative date-time phrases. The default anchor date will be the date the code is run."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Bhc7EXJYjIIG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243965459,"user_tz":-120,"elapsed":4,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"623a3dcc-332d-4b4a-9169-113ddf3638ec"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='MultiDateMatcher_6d91e34ffb34', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='MultiDateMatcher_6d91e34ffb34', name='inputFormats', doc='input formats list of patterns to match'): [''],\n"," Param(parent='MultiDateMatcher_6d91e34ffb34', name='outputFormat', doc='desired output format for dates extracted'): 'yyyy/MM/dd',\n"," Param(parent='MultiDateMatcher_6d91e34ffb34', name='readMonthFirst', doc='Whether to parse july 07/05/2015 or as 05/07/2015'): True,\n"," Param(parent='MultiDateMatcher_6d91e34ffb34', name='defaultDayWhenMissing', doc='which day to set when it is missing from parsed input'): 1}"]},"metadata":{},"execution_count":112}],"source":["MultiDateMatcher().extractParamMap()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jkEoNtqPjJvY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243966538,"user_tz":-120,"elapsed":1081,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"abe7a02c-5cba-4b49-dbc6-4e547256fec4"},"outputs":[{"output_type":"stream","name":"stdout","text":["+------------------------+\n","|result                  |\n","+------------------------+\n","|[2024/04/11, 2024/04/03]|\n","+------------------------+\n","\n"]}],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","date_matcher = MultiDateMatcher() \\\n","    .setInputCols('document') \\\n","    .setOutputCol(\"date\")\\\n","    .setOutputFormat(\"yyyy/MM/dd\")\\\n","    .setSourceLanguage(\"en\")\n","\n","\n","date_pipeline = PipelineModel(\n","    stages=[\n","        documentAssembler,\n","        date_matcher\n","        ])\n","\n","sample_df = spark.createDataFrame([['I saw him yesterday and he told me that he will visit us next week']]).toDF(\"text\")\n","\n","result = date_pipeline.transform(sample_df)\n","result.select('date.result').show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"R0Wd3ZLDqM4K"},"source":["Let's set the Input Format and Output Format to specific format"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"j3mOjjnojLQ5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243967118,"user_tz":-120,"elapsed":582,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"ec7dfabb-ee2d-4c55-e5f5-48ab859c8573"},"outputs":[{"output_type":"stream","name":"stdout","text":["+------------+\n","|result      |\n","+------------+\n","|[2022/05/21]|\n","+------------+\n","\n"]}],"source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","date_matcher = MultiDateMatcher() \\\n","    .setInputCols('document') \\\n","    .setOutputCol(\"date\")\\\n","    .setInputFormats([\"dd/MM/yyyy\"])\\\n","    .setOutputFormat(\"yyyy/MM/dd\")\\\n","    .setSourceLanguage(\"en\")\n","\n","date_pipeline = PipelineModel(\n","    stages=[\n","        documentAssembler,\n","        date_matcher\n","        ])\n","\n","sample_df = spark.createDataFrame([[\"the last payment date of this invoice is 21/05/2022\"]]).toDF(\"text\")\n","\n","result = date_pipeline.transform(sample_df)\n","result.select('date.result').show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"ZOwrFGd8ix83"},"source":["## Text Cleaning with UDF"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wvlydmGsjMwB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243968310,"user_tz":-120,"elapsed":1194,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"b861632f-c530-4c9e-9de5-4e18be108291"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------------------------------+-----------------------+\n","|text                                                                                          |cleaned                |\n","+----------------------------------------------------------------------------------------------+-----------------------+\n","|<h1 style=\"color: #5e9ca0;\">Have a great <span  style=\"color: #2b2301;\">birth</span> day!</h1>|Have a great birth day!|\n","+----------------------------------------------------------------------------------------------+-----------------------+\n","\n"]}],"source":["text = '<h1 style=\"color: #5e9ca0;\">Have a great <span  style=\"color: #2b2301;\">birth</span> day!</h1>'\n","\n","text_df = spark.createDataFrame([[text]]).toDF(\"text\")\n","\n","import re\n","from pyspark.sql.functions import udf\n","from pyspark.sql.types import StringType, IntegerType\n","\n","clean_text = lambda s: re.sub(r'<[^>]*>', '', s)\n","\n","text_df.withColumn('cleaned', udf(clean_text, StringType())('text')).select('text','cleaned').show(truncate= False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ybQ1fCwmjPGL","executionInfo":{"status":"ok","timestamp":1712243968310,"user_tz":-120,"elapsed":8,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"bf3526b7-4567-4dc3-cd9b-54ba819ab887"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["2"]},"metadata":{},"execution_count":116}],"source":["find_not_alnum_count = lambda s: len([i for i in s if not i.isalnum() and i!=' '])\n","\n","find_not_alnum_count(\"it's your birth day!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LQmdazXEjQcH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243968310,"user_tz":-120,"elapsed":4,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"ec6e71c8-2b21-4131-9c6d-33aef93c8a11"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["23"]},"metadata":{},"execution_count":117}],"source":["text = '<h1 style=\"color: #5e9ca0;\">Have a great <span  style=\"color: #2b2301;\">birth</span> day!</h1>'\n","\n","find_not_alnum_count(text)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZyfogEYXjRzT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243968953,"user_tz":-120,"elapsed":645,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"4e724dec-ffb4-43c6-b9e5-c254fe8b9efd"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------------------------------+-------+\n","|text                                                                                          |cleaned|\n","+----------------------------------------------------------------------------------------------+-------+\n","|<h1 style=\"color: #5e9ca0;\">Have a great <span  style=\"color: #2b2301;\">birth</span> day!</h1>|23     |\n","+----------------------------------------------------------------------------------------------+-------+\n","\n"]}],"source":["text_df.withColumn('cleaned', udf(find_not_alnum_count, IntegerType())('text')).select('text','cleaned').show(truncate= False)"]},{"cell_type":"markdown","metadata":{"id":"Tj-RyTZaix8-"},"source":["## Finisher"]},{"cell_type":"markdown","metadata":{"id":"czhvgXuHix8-"},"source":["***Finisher:*** Once we have our NLP pipeline ready to go, we might want to use our annotation results somewhere else where it is easy to use. The Finisher outputs annotation(s) values into a string.\n","\n","If we just want the desired output column in the final dataframe, we can use Finisher to drop previous stages in the final output and get the `result` from the process.\n","\n","This is very handy when you want to use the output from Spark NLP annotator as an input to another Spark ML transformer.\n","\n","**Settable parameters are:**\n","\n","- **setInputCols**      : input column name string which targets a column of type Array\n","\n","- **setOutputCols**      : output column name string which targets a column of type AnnotatorType\n","\n","- **setCleanAnnotations(True)** : Whether to remove intermediate annotations\n","\n","- **setValueSplitSymbol(“#”)**: split values within an annotation character\n","\n","- **setAnnotationSplitSymbol(“@”)** : split values between annotations character\n","\n","- **setIncludeMetadata(False)**: Whether to include metadata keys. Sometimes useful in some annotations.\n","\n","- **setOutputAsArray(False)** : Whether to output as Array. Useful as input for other Spark transformers.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MlX0bERdjTfm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243969421,"user_tz":-120,"elapsed":469,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"e1d88111-94d2-4443-a858-301c887cc4a0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(text='The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.', finished_regex_matches=['inwardly', 'family', 'spansapproximately', 'byapproximately', 'approximately', 'respectively', 'poly', 'KCNJ9', '3.3,', 'GIRK3)', 'KCNJ9', '1q21-23', '7.6', '2.2', '2.6', 'identified14', 'aVal366Ala', '8', 'KCNJ9', 'KCNJ9', '9 g'])]"]},"metadata":{},"execution_count":119}],"source":["finisher = Finisher() \\\n","    .setInputCols([\"regex_matches\"]) \\\n","    .setIncludeMetadata(False) # set to False to remove metadata\n","\n","nlpPipeline = Pipeline(stages=[documentAssembler,\n","                               regex_matcher,\n","                               finisher])\n","\n","match_df = nlpPipeline.fit(pubMedDF).transform(pubMedDF)\n","match_df.take(1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WJCF53NkjU3E","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243969421,"user_tz":-120,"elapsed":8,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"1bdf5a5d-7e0a-42f8-c862-7bc70fcb7bd9"},"outputs":[{"output_type":"stream","name":"stdout","text":["root\n"," |-- text: string (nullable = true)\n"," |-- finished_regex_matches: array (nullable = true)\n"," |    |-- element: string (containsNull = true)\n","\n"]}],"source":["match_df.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b6GVYNwRjakk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243969421,"user_tz":-120,"elapsed":6,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"ca218eb8-569f-4003-c682-98bfa1595d2d"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------+--------------------------------------------------+\n","|                                              text|                            finished_regex_matches|\n","+--------------------------------------------------+--------------------------------------------------+\n","|The human KCNJ9 (Kir 3.3, GIRK3) is a member of...|[inwardly, family, spansapproximately, byapprox...|\n","|BACKGROUND: At present, it is one of the most i...|[previously, previously, intravenously, previou...|\n","|OBJECTIVE: To investigate the relationship betw...|[renal failure, cardiac surgery, cardiac surger...|\n","|Combined EEG/fMRI recording has been used to lo...|[normally, significantly, effectively, analy, o...|\n","|Statistical analysis of neuroimages is commonly...|[analy, commonly, overly, normally, thatsuccess...|\n","|Our objective was to compare three different me...|[daily, only, Conversely, Hourly, hourly, Hourl...|\n","|We conducted a phase II study to assess the eff...|[analy, respectively, generally, 5-fluorouracil...|\n","|\"Monomeric sarcosine oxidase (MSOX) is a flavoe...|[cataly, methylgly, gly, ethylgly, dimethylgly,...|\n","|We presented the tachinid fly Exorista japonica...|                         [fly, fly, fly, fly, fly]|\n","|The literature dealing with the water conductin...|           [generally, mathematically, especially]|\n","|A novel approach to synthesize chitosan-O-isopr...|[efficiently, poly, chitosan-O-isopropyl-5'-O-d...|\n","|An HPLC-ESI-MS-MS method has been developed for...|[chromatographically, respectively, successfull...|\n","|OBJECTIVE: To evaluate the effectiveness and ac...|[weekly, respectively, theanaly, 2006, 2007,, 2...|\n","|We report the results of a screen for genetic a...|[poly, threepoly, significantly, analy, actuall...|\n","|Intraparenchymal pericatheter cyst is rarely re...|              [rarely, possibly, unusually, Early]|\n","|PURPOSE: To compare the effectiveness, potentia...|[analy, comparatively, wassignificantly, respec...|\n","|We have demonstrated a new type of all-optical ...|[approximately, fully, approximately, approxima...|\n","|Physalis peruviana (PP) is a widely used medici...|[widely, (20,, 40,, 60,, 80, 95%, 100, 95%, (82...|\n","|We report the discovery of a series of substitu...|[highly, potentially, highly, respectively, tub...|\n","|The purpose of this study was to identify and c...|[family, Nearly, only, 43, 10, 44%, 32%, 64%, 4...|\n","+--------------------------------------------------+--------------------------------------------------+\n","only showing top 20 rows\n","\n"]}],"source":["match_df.filter(F.size('finished_regex_matches')>2).show(truncate = 50)"]},{"cell_type":"markdown","metadata":{"id":"RBeh9Yv44elz"},"source":["## LightPipeline\n","\n","https://medium.com/spark-nlp/spark-nlp-101-lightpipeline-a544e93f20f1"]},{"cell_type":"markdown","metadata":{"id":"eaANY-Bg4paN"},"source":["LightPipelines are Spark NLP specific Pipelines, equivalent to Spark ML Pipeline, but meant to deal with smaller amounts of data. They’re useful working with small datasets, debugging results, or when running either training or prediction from an API that serves one-off requests.\n","\n","Spark NLP LightPipelines are Spark ML pipelines converted into a single machine but the multi-threaded task, becoming more than 10x times faster for smaller amounts of data (small is relative, but 50k sentences are roughly a good maximum). To use them, we simply plug in a trained (fitted) pipeline and then annotate a plain text. We don't even need to convert the input text to DataFrame in order to feed it into a pipeline that's accepting DataFrame as an input in the first place. This feature would be quite useful when it comes to getting a prediction for a few lines of text from a trained ML model.\n","\n"," **It is nearly 10x faster than using Spark ML Pipeline**\n","\n","`LightPipeline(someTrainedPipeline).annotate(someStringOrArray)`"]},{"cell_type":"code","source":["documentAssembler = DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","tokenizer = Tokenizer() \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"token\")\n","\n","stemmer = Stemmer() \\\n","    .setInputCols([\"token\"]) \\\n","    .setOutputCol(\"stem\")\n","\n","nlpPipeline = Pipeline(\n","    stages=[\n","        documentAssembler,\n","        tokenizer,\n","        stemmer,\n","        lemmatizer\n","        ])\n","\n","empty_df = spark.createDataFrame([['']]).toDF(\"text\")\n","\n","pipelineModel = nlpPipeline.fit(empty_df)"],"metadata":{"id":"dsMSfE_wjod_"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zTVtbS-qjdlg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243972111,"user_tz":-120,"elapsed":2692,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"9735b641-2333-453f-d114-0cd92671ddba"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|                text|            document|               token|                stem|               lemma|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|Peter is a very g...|[{document, 0, 27...|[{token, 0, 4, Pe...|[{token, 0, 4, pe...|[{token, 0, 4, Pe...|\n","|My life in Russia...|[{document, 0, 37...|[{token, 0, 1, My...|[{token, 0, 1, my...|[{token, 0, 1, My...|\n","|John and Peter ar...|[{document, 0, 76...|[{token, 0, 3, Jo...|[{token, 0, 3, jo...|[{token, 0, 3, Jo...|\n","|Lucas Nogal Dunbe...|[{document, 0, 67...|[{token, 0, 4, Lu...|[{token, 0, 4, lu...|[{token, 0, 4, Lu...|\n","|Europe is very cu...|[{document, 0, 68...|[{token, 0, 5, Eu...|[{token, 0, 5, eu...|[{token, 0, 5, Eu...|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+\n","\n"]}],"source":["nlpPipeline.fit(empty_df).transform(spark_df).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0zmTWqJIjfDG"},"outputs":[],"source":["from sparknlp.base import LightPipeline\n","\n","light_model = LightPipeline(pipelineModel)\n","light_result = light_model.annotate(\"John and Peter are brothers. However they don't support each other that much.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m7CArYm0jg9o","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243972111,"user_tz":-120,"elapsed":5,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"c13e62bf-e69d-4e51-c8df-1616f454e2a0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['document', 'token', 'stem', 'lemma'])"]},"metadata":{},"execution_count":124}],"source":["light_result.keys()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ALrTj7VjiNy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243972111,"user_tz":-120,"elapsed":4,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"dabaae8f-b13f-4811-a775-3a801bb8fd1e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('John', 'john', 'John'),\n"," ('and', 'and', 'and'),\n"," ('Peter', 'peter', 'Peter'),\n"," ('are', 'ar', 'be'),\n"," ('brothers', 'brother', 'brother'),\n"," ('.', '.', '.'),\n"," ('However', 'howev', 'However'),\n"," ('they', 'thei', 'they'),\n"," (\"don't\", \"don't\", \"don't\"),\n"," ('support', 'support', 'support'),\n"," ('each', 'each', 'each'),\n"," ('other', 'other', 'other'),\n"," ('that', 'that', 'that'),\n"," ('much', 'much', 'much'),\n"," ('.', '.', '.')]"]},"metadata":{},"execution_count":125}],"source":["list(zip(light_result['token'], light_result['stem'], light_result['lemma']))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NaL_CC_Mjj0w"},"outputs":[],"source":["light_result = light_model.fullAnnotate(\"John and Peter are brothers. However they don't support each other that much.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3RjngqxZjocM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243973628,"user_tz":-120,"elapsed":1519,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"075abb4a-0953-4ca9-8285-e135ac606676"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'document': [Annotation(document, 0, 76, John and Peter are brothers. However they don't support each other that much., {}, [])],\n","  'token': [Annotation(token, 0, 3, John, {'sentence': '0'}, []),\n","   Annotation(token, 5, 7, and, {'sentence': '0'}, []),\n","   Annotation(token, 9, 13, Peter, {'sentence': '0'}, []),\n","   Annotation(token, 15, 17, are, {'sentence': '0'}, []),\n","   Annotation(token, 19, 26, brothers, {'sentence': '0'}, []),\n","   Annotation(token, 27, 27, ., {'sentence': '0'}, []),\n","   Annotation(token, 29, 35, However, {'sentence': '0'}, []),\n","   Annotation(token, 37, 40, they, {'sentence': '0'}, []),\n","   Annotation(token, 42, 46, don't, {'sentence': '0'}, []),\n","   Annotation(token, 48, 54, support, {'sentence': '0'}, []),\n","   Annotation(token, 56, 59, each, {'sentence': '0'}, []),\n","   Annotation(token, 61, 65, other, {'sentence': '0'}, []),\n","   Annotation(token, 67, 70, that, {'sentence': '0'}, []),\n","   Annotation(token, 72, 75, much, {'sentence': '0'}, []),\n","   Annotation(token, 76, 76, ., {'sentence': '0'}, [])],\n","  'stem': [Annotation(token, 0, 3, john, {'sentence': '0'}, []),\n","   Annotation(token, 5, 7, and, {'sentence': '0'}, []),\n","   Annotation(token, 9, 13, peter, {'sentence': '0'}, []),\n","   Annotation(token, 15, 17, ar, {'sentence': '0'}, []),\n","   Annotation(token, 19, 26, brother, {'sentence': '0'}, []),\n","   Annotation(token, 27, 27, ., {'sentence': '0'}, []),\n","   Annotation(token, 29, 35, howev, {'sentence': '0'}, []),\n","   Annotation(token, 37, 40, thei, {'sentence': '0'}, []),\n","   Annotation(token, 42, 46, don't, {'sentence': '0'}, []),\n","   Annotation(token, 48, 54, support, {'sentence': '0'}, []),\n","   Annotation(token, 56, 59, each, {'sentence': '0'}, []),\n","   Annotation(token, 61, 65, other, {'sentence': '0'}, []),\n","   Annotation(token, 67, 70, that, {'sentence': '0'}, []),\n","   Annotation(token, 72, 75, much, {'sentence': '0'}, []),\n","   Annotation(token, 76, 76, ., {'sentence': '0'}, [])],\n","  'lemma': [Annotation(token, 0, 3, John, {'sentence': '0'}, []),\n","   Annotation(token, 5, 7, and, {'sentence': '0'}, []),\n","   Annotation(token, 9, 13, Peter, {'sentence': '0'}, []),\n","   Annotation(token, 15, 17, be, {'sentence': '0'}, []),\n","   Annotation(token, 19, 26, brother, {'sentence': '0'}, []),\n","   Annotation(token, 27, 27, ., {'sentence': '0'}, []),\n","   Annotation(token, 29, 35, However, {'sentence': '0'}, []),\n","   Annotation(token, 37, 40, they, {'sentence': '0'}, []),\n","   Annotation(token, 42, 46, don't, {'sentence': '0'}, []),\n","   Annotation(token, 48, 54, support, {'sentence': '0'}, []),\n","   Annotation(token, 56, 59, each, {'sentence': '0'}, []),\n","   Annotation(token, 61, 65, other, {'sentence': '0'}, []),\n","   Annotation(token, 67, 70, that, {'sentence': '0'}, []),\n","   Annotation(token, 72, 75, much, {'sentence': '0'}, []),\n","   Annotation(token, 76, 76, ., {'sentence': '0'}, [])]}]"]},"metadata":{},"execution_count":127}],"source":["light_result"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QbYjvkGcjlMQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712243973628,"user_tz":-120,"elapsed":4,"user":{"displayName":"Akar Öztürk","userId":"07602883577262601067"}},"outputId":"2e2d6ca6-61a6-4f37-db1e-c744fa751688"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'document': ['How did serfdom develop in and then leave Russia ?'],\n","  'token': ['How',\n","   'did',\n","   'serfdom',\n","   'develop',\n","   'in',\n","   'and',\n","   'then',\n","   'leave',\n","   'Russia',\n","   '?'],\n","  'stem': ['how',\n","   'did',\n","   'serfdom',\n","   'develop',\n","   'in',\n","   'and',\n","   'then',\n","   'leav',\n","   'russia',\n","   '?'],\n","  'lemma': ['How',\n","   'do',\n","   'serfdom',\n","   'develop',\n","   'in',\n","   'and',\n","   'then',\n","   'leave',\n","   'Russia',\n","   '?']},\n"," {'document': ['There will be some exciting breakthroughs in NLP this year.'],\n","  'token': ['There',\n","   'will',\n","   'be',\n","   'some',\n","   'exciting',\n","   'breakthroughs',\n","   'in',\n","   'NLP',\n","   'this',\n","   'year',\n","   '.'],\n","  'stem': ['there',\n","   'will',\n","   'be',\n","   'some',\n","   'excit',\n","   'breakthrough',\n","   'in',\n","   'nlp',\n","   'thi',\n","   'year',\n","   '.'],\n","  'lemma': ['There',\n","   'will',\n","   'be',\n","   'some',\n","   'exciting',\n","   'breakthrough',\n","   'in',\n","   'NLP',\n","   'this',\n","   'year',\n","   '.']}]"]},"metadata":{},"execution_count":128}],"source":["text_list= [\"How did serfdom develop in and then leave Russia ?\",\n","\"There will be some exciting breakthroughs in NLP this year.\"]\n","\n","light_model.annotate(text_list)"]},{"cell_type":"markdown","metadata":{"id":"-44fmATEqrL_"},"source":["**important note:** When you use Finisher in your pipeline, regardless of setting `cleanAnnotations` to False or True, LigtPipeline will only return the finished columns."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}