Pipelines

 

Pretrained Pipelines moved to its own dedicated repository. Please follow this link for updated list: https://github.com/JohnSnowLabs/spark-nlp-models

English

NOTE: noncontrib pipelines are compatible with Windows operating systems.

Pipelines Name
Explain Document ML explain_document_ml
Explain Document DL explain_document_dl
Explain Document DL Win explain_document_dl_noncontrib
Explain Document DL Fast explain_document_dl_fast
Explain Document DL Fast Win explain_document_dl_fast_noncontrib
Recognize Entities DL recognize_entities_dl
Recognize Entities DL Win recognize_entities_dl_noncontrib
OntoNotes Entities Small onto_recognize_entities_sm
OntoNotes Entities Large onto_recognize_entities_lg
Match Datetime match_datetime
Match Pattern match_pattern
Match Chunk match_chunks
Match Phrases match_phrases
Clean Stop clean_stop
Clean Pattern clean_pattern
Clean Slang clean_slang
Check Spelling check_spelling
Analyze Sentiment analyze_sentiment
Dependency Parse dependency_parse

explain_document_ml

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "Google has announced the release of a beta version of the popular TensorFlow machine learning library"),
(2, "The Paris metro will soon enter the 21st century, ditching single-use paper tickets for rechargeable electronic cards.")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("explain_document_ml", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
2.0.8
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_ml,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 7 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|            sentence|               token|             checked|              lemmas|               stems|                 pos|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|Google has announ...|[[document, 0, 10...|[[document, 0, 10...|[[token, 0, 5, Go...|[[token, 0, 5, Go...|[[token, 0, 5, Go...|[[token, 0, 5, go...|[[pos, 0, 5, NNP,...|
|  2|The Paris metro w...|[[document, 0, 11...|[[document, 0, 11...|[[token, 0, 2, Th...|[[token, 0, 2, Th...|[[token, 0, 2, Th...|[[token, 0, 2, th...|[[pos, 0, 2, DT, ...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

explain_document_dl

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "Google has announced the release of a beta version of the popular TensorFlow machine learning library"),
(2, "Donald John Trump (born June 14, 1946) is the 45th and current president of the United States")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("explain_document_dl", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()
/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_dl,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 10 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|             checked|               lemma|                stem|                 pos|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|Google has announ...|[[document, 0, 10...|[[token, 0, 5, Go...|[[document, 0, 10...|[[token, 0, 5, Go...|[[token, 0, 5, Go...|[[token, 0, 5, go...|[[pos, 0, 5, NNP,...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 5, Go...|
|  2|The Paris metro w...|[[document, 0, 11...|[[token, 0, 2, Th...|[[document, 0, 11...|[[token, 0, 2, Th...|[[token, 0, 2, Th...|[[token, 0, 2, th...|[[pos, 0, 2, DT, ...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 4, 8, Pa...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+----------------------------------+
|result                            |
+----------------------------------+
|[Google, TensorFlow]              |
|[Donald John Trump, United States]|
+----------------------------------+
*/

recognize_entities_dl

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "Google has announced the release of a beta version of the popular TensorFlow machine learning library"),
(2, "Donald John Trump (born June 14, 1946) is the 45th and current president of the United States")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("recognize_entities_dl", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(entity_recognizer_dl,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 6 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|            sentence|               token|          embeddings|                 ner|       ner_converter|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|Google has announ...|[[document, 0, 10...|[[document, 0, 10...|[[token, 0, 5, Go...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 5, Go...|
|  2|Donald John Trump...|[[document, 0, 92...|[[document, 0, 92...|[[token, 0, 5, Do...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 16, D...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+----------------------------------+
|result                            |
+----------------------------------+
|[Google, TensorFlow]              |
|[Donald John Trump, United States]|
+----------------------------------+
*/

onto_recognize_entities_sm

Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the OntoNotes corpus and supports the identification of 18 entities.

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "Johnson first entered politics when elected in 2001 as a member of Parliament. He then served eight years as the mayor of London, from 2008 to 2016, before rejoining Parliament. "),
(2, "A little less than a decade later, dozens of self-driving startups have cropped up while automakers around the world clamor, wallet in hand, to secure their place in the fast-moving world of fully automated transportation.")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("onto_recognize_entities_sm", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.1.0
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(onto_recognize_entities_sm,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 6 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|Johnson first ent...|[[document, 0, 17...|[[token, 0, 6, Jo...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 6, Jo...|
|  2|A little less tha...|[[document, 0, 22...|[[token, 0, 0, A,...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 32, A...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+---------------------------------------------------------------------------------+
|result                                                                           |
+---------------------------------------------------------------------------------+
|[Johnson, first, 2001, Parliament, eight years, London, 2008 to 2016, Parliament]|
|[A little less than a decade later, dozens]                                      |
+---------------------------------------------------------------------------------+
*/

onto_recognize_entities_lg

Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the OntoNotes corpus and supports the identification of 18 entities.

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "Johnson first entered politics when elected in 2001 as a member of Parliament. He then served eight years as the mayor of London, from 2008 to 2016, before rejoining Parliament. "),
(2, "A little less than a decade later, dozens of self-driving startups have cropped up while automakers around the world clamor, wallet in hand, to secure their place in the fast-moving world of fully automated transportation.")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("onto_recognize_entities_lg", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.1.0
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(onto_recognize_entities_lg,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 6 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|Johnson first ent...|[[document, 0, 17...|[[token, 0, 6, Jo...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 6, Jo...|
|  2|A little less tha...|[[document, 0, 22...|[[token, 0, 0, A,...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 32, A...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+-------------------------------------------------------------------------------+
|result                                                                         |
+-------------------------------------------------------------------------------+
|[Johnson, first, 2001, Parliament, eight years, London, 2008, 2016, Parliament]|
|[A little less than a decade later, dozens]                                    |
+-------------------------------------------------------------------------------+
*/

match_datetime

DateMatcher yyyy/MM/dd

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "I would like to come over and see you in 01/02/2019."),
(2, "Donald John Trump (born June 14, 1946) is the 45th and current president of the United States")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("match_datetime", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(match_datetime,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 4 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|            sentence|               token|                date|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|I would like to c...|[[document, 0, 51...|[[document, 0, 51...|[[token, 0, 0, I,...|[[date, 41, 50, 2...|
|  2|Donald John Trump...|[[document, 0, 92...|[[document, 0, 92...|[[token, 0, 5, Do...|[[date, 24, 36, 1...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("date.result").show(false)

/*
+------------+
|result      |
+------------+
|[2019/01/02]|
|[1946/06/14]|
+------------+
*/

match_pattern

RegexMatcher (match phone numbers)

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "You should call Mr. Jon Doe at +33 1 79 01 22 89")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("match_pattern", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(match_pattern,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 4 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|            sentence|               token|               regex|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|You should call M...|[[document, 0, 47...|[[document, 0, 47...|[[token, 0, 2, Yo...|[[chunk, 31, 47, ...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("regex.result").show(false)

/*
+-------------------+
|result             |
+-------------------+
|[+33 1 79 01 22 89]|
+-------------------+
*/

match_chunks

The pipeline uses regex <DT/>?/<JJ/>*<NN>+

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "The book has many chapters"),
(2, "the little yellow dog barked at the cat")
)).toDF("id", "text")

val pipeline = PretrainedPipeline("match_chunks", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(match_chunks,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 5 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|            sentence|               token|                 pos|               chunk|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|The book has many...|[[document, 0, 25...|[[document, 0, 25...|[[token, 0, 2, Th...|[[pos, 0, 2, DT, ...|[[chunk, 0, 7, Th...|
|  2|the little yellow...|[[document, 0, 38...|[[document, 0, 38...|[[token, 0, 2, th...|[[pos, 0, 2, DT, ...|[[chunk, 0, 20, t...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("chunk.result").show(false)

/*
+--------------------------------+
|result                          |
+--------------------------------+
|[The book]                      |
|[the little yellow dog, the cat]|
+--------------------------------+
*/

French

Pipelines Name
Explain Document Large explain_document_lg
Explain Document Medium explain_document_md
Entity Recognizer Large entity_recognizer_lg
Entity Recognizer Medium entity_recognizer_md
Feature Description
NER Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the WikiNER corpus and supports the identification of PER, LOC, ORG and MISC entities
Lemma Trained by Lemmatizer annotator on lemmatization-lists by Michal Měchura
POS Trained by PerceptronApproach annotator on the Universal Dependencies
Size Model size indicator, md and lg. The large pipeline uses glove_840B_300 and the medium uses glove_6B_300 WordEmbeddings

French explain_document_lg

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("explain_document_lg", lang="fr")

val testData = spark.createDataFrame(Seq(
(1, "Contrairement à Quentin Tarantino, le cinéma français ne repart pas les mains vides de la compétition cannoise."),
(2, "Emmanuel Jean-Michel Frédéric Macron est le fils de Jean-Michel Macron, né en 1950, médecin, professeur de neurologie au CHU d'Amiens4 et responsable d'enseignement à la faculté de médecine de cette même ville5, et de Françoise Noguès, médecin conseil à la Sécurité sociale")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_lg,fr,public/models)
testData: org.apache.spark.sql.DataFrame = [id: bigint, text: string]
annotation: org.apache.spark.sql.DataFrame = [id: bigint, text: string ... 8 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|               lemma|                 pos|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  0|Contrairement à Q...|[[document, 0, 11...|[[token, 0, 12, C...|[[document, 0, 11...|[[token, 0, 12, C...|[[pos, 0, 12, ADV...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 16, 32, ...|
|  1|Emmanuel Jean-Mic...|[[document, 0, 27...|[[token, 0, 7, Em...|[[document, 0, 27...|[[token, 0, 7, Em...|[[pos, 0, 7, PROP...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 35, E...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*+-------------------------------------------------------------------------------------------------------------+
|result                                                                                                       |
+-------------------------------------------------------------------------------------------------------------+
|[Quentin Tarantino]                                                                                          |
|[Emmanuel Jean-Michel Frédéric Macron, Jean-Michel Macron, CHU d'Amiens4, Françoise Noguès, Sécurité sociale]|
+-------------------------------------------------------------------------------------------------------------+
*/

French explain_document_md

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("explain_document_md", lang="fr")

val testData = spark.createDataFrame(Seq(
(1, "Contrairement à Quentin Tarantino, le cinéma français ne repart pas les mains vides de la compétition cannoise."),
(2, "Emmanuel Jean-Michel Frédéric Macron est le fils de Jean-Michel Macron, né en 1950, médecin, professeur de neurologie au CHU d'Amiens4 et responsable d'enseignement à la faculté de médecine de cette même ville5, et de Françoise Noguès, médecin conseil à la Sécurité sociale")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_md,fr,public/models)
testData: org.apache.spark.sql.DataFrame = [id: bigint, text: string]
annotation: org.apache.spark.sql.DataFrame = [id: bigint, text: string ... 8 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|               lemma|                 pos|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  0|Contrairement à Q...|[[document, 0, 11...|[[token, 0, 12, C...|[[document, 0, 11...|[[token, 0, 12, C...|[[pos, 0, 12, ADV...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 16, 32, ...|
|  1|Emmanuel Jean-Mic...|[[document, 0, 27...|[[token, 0, 7, Em...|[[document, 0, 27...|[[token, 0, 7, Em...|[[pos, 0, 7, PROP...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 35, E...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
|result                                                                                                          |
+----------------------------------------------------------------------------------------------------------------+
|[Quentin Tarantino]                                                                                             |
|[Emmanuel Jean-Michel Frédéric Macron, Jean-Michel Macron, au CHU d'Amiens4, Françoise Noguès, Sécurité sociale]|
+----------------------------------------------------------------------------------------------------------------+
*/

French entity_recognizer_lg

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("entity_recognizer_lg", lang="fr")

val testData = spark.createDataFrame(Seq(
(1, "Contrairement à Quentin Tarantino, le cinéma français ne repart pas les mains vides de la compétition cannoise."),
(2, "Emmanuel Jean-Michel Frédéric Macron est le fils de Jean-Michel Macron, né en 1950, médecin, professeur de neurologie au CHU d'Amiens4 et responsable d'enseignement à la faculté de médecine de cette même ville5, et de Françoise Noguès, médecin conseil à la Sécurité sociale")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  0|Contrairement à Q...|[[document, 0, 11...|[[token, 0, 12, C...|[[document, 0, 11...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 16, 32, ...|
|  1|Emmanuel Jean-Mic...|[[document, 0, 27...|[[token, 0, 7, Em...|[[document, 0, 27...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 35, E...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+-------------------------------------------------------------------------------------------------------------+
|result                                                                                                       |
+-------------------------------------------------------------------------------------------------------------+
|[Quentin Tarantino]                                                                                          |
|[Emmanuel Jean-Michel Frédéric Macron, Jean-Michel Macron, CHU d'Amiens4, Françoise Noguès, Sécurité sociale]|
+-------------------------------------------------------------------------------------------------------------+
*/

French entity_recognizer_md

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("entity_recognizer_md", lang="fr")

val testData = spark.createDataFrame(Seq(
(1, "Contrairement à Quentin Tarantino, le cinéma français ne repart pas les mains vides de la compétition cannoise."),
(2, "Emmanuel Jean-Michel Frédéric Macron est le fils de Jean-Michel Macron, né en 1950, médecin, professeur de neurologie au CHU d'Amiens4 et responsable d'enseignement à la faculté de médecine de cette même ville5, et de Françoise Noguès, médecin conseil à la Sécurité sociale")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  0|Contrairement à Q...|[[document, 0, 11...|[[token, 0, 12, C...|[[document, 0, 11...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 16, 32, ...|
|  1|Emmanuel Jean-Mic...|[[document, 0, 27...|[[token, 0, 7, Em...|[[document, 0, 27...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 35, E...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*+-------------------------------------------------------------------------------------------------------------+
|result                                                                                                          |
+----------------------------------------------------------------------------------------------------------------+
|[Quentin Tarantino]                                                                                             |
|[Emmanuel Jean-Michel Frédéric Macron, Jean-Michel Macron, au CHU d'Amiens4, Françoise Noguès, Sécurité sociale]|
+----------------------------------------------------------------------------------------------------------------+
*/

Italian

Pipelines Name
Explain Document Large explain_document_lg
Explain Document Medium explain_document_md
Entity Recognizer Large entity_recognizer_lg
Entity Recognizer Medium entity_recognizer_md
Feature Description
NER Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the WikiNER corpus and supports the identification of PER, LOC, ORG and MISC entities
Lemma Trained by Lemmatizer annotator on DXC Technology dataset
POS Trained by PerceptronApproach annotator on the Universal Dependencies
Size Model size indicator, md and lg. The large pipeline uses glove_840B_300 and the medium uses glove_6B_300 WordEmbeddings

Italian explain_document_lg

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("explain_document_lg", lang="it")

val testData = spark.createDataFrame(Seq(
(1, "La FIFA ha deciso: tre giornate a Zidane, due a Materazzi"),
(2, "Reims, 13 giugno 2019 – Domani può essere la giornata decisiva per il passaggio agli ottavi di finale dei Mondiali femminili.")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_lg,it,public/models)
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 8 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|               lemma|                 pos|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|La FIFA ha deciso...|[[document, 0, 56...|[[token, 0, 1, La...|[[document, 0, 56...|[[token, 0, 1, La...|[[pos, 0, 1, DET,...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 3, 6, FI...|
|  2|Reims, 13 giugno ...|[[document, 0, 12...|[[token, 0, 4, Re...|[[document, 0, 12...|[[token, 0, 4, Re...|[[pos, 0, 4, PROP...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 4, Re...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+-----------------------------------+
|result                             |
+-----------------------------------+
|[FIFA, Zidane, Materazzi]          |
|[Reims, Domani, Mondiali femminili]|
+-----------------------------------+
*/

Italian explain_document_md

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("explain_document_md", lang="it")

val testData = spark.createDataFrame(Seq(
(1, "La FIFA ha deciso: tre giornate a Zidane, due a Materazzi"),
(2, "Reims, 13 giugno 2019 – Domani può essere la giornata decisiva per il passaggio agli ottavi di finale dei Mondiali femminili.")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_lg,it,public/models)
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 8 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|               lemma|                 pos|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|La FIFA ha deciso...|[[document, 0, 56...|[[token, 0, 1, La...|[[document, 0, 56...|[[token, 0, 1, La...|[[pos, 0, 1, DET,...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 9, La...|
|  2|Reims, 13 giugno ...|[[document, 0, 12...|[[token, 0, 4, Re...|[[document, 0, 12...|[[token, 0, 4, Re...|[[pos, 0, 4, PROP...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 4, Re...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+-------------------------------+
|result                         |
+-------------------------------+
|[La FIFA, Zidane, Materazzi]|
|[Reims, Domani, Mondiali]      |
+-------------------------------+
*/

Italian entity_recognizer_lg

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("entity_recognizer_lg", lang="it")

val testData = spark.createDataFrame(Seq(
(1, "La FIFA ha deciso: tre giornate a Zidane, due a Materazzi"),
(2, "Reims, 13 giugno 2019 – Domani può essere la giornata decisiva per il passaggio agli ottavi di finale dei Mondiali femminili.")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_lg,it,public/models)
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 8 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|La FIFA ha deciso...|[[document, 0, 56...|[[token, 0, 1, La...|[[document, 0, 56...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 3, 6, FI...|
|  2|Reims, 13 giugno ...|[[document, 0, 12...|[[token, 0, 4, Re...|[[document, 0, 12...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 4, Re...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+-----------------------------------+
|result                             |
+-----------------------------------+
|[FIFA, Zidane, Materazzi]          |
|[Reims, Domani, Mondiali femminili]|
+-----------------------------------+
*/

Italian entity_recognizer_md

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val pipeline = PretrainedPipeline("entity_recognizer_md", lang="it")

val testData = spark.createDataFrame(Seq(
(1, "La FIFA ha deciso: tre giornate a Zidane, due a Materazzi"),
(2, "Reims, 13 giugno 2019 – Domani può essere la giornata decisiva per il passaggio agli ottavi di finale dei Mondiali femminili.")
)).toDF("id", "text")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.0.8
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(explain_document_lg,it,public/models)
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 8 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|               token|            sentence|          embeddings|                 ner|            entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|La FIFA ha deciso...|[[document, 0, 56...|[[token, 0, 1, La...|[[document, 0, 56...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 9, La...|
|  2|Reims, 13 giugno ...|[[document, 0, 12...|[[token, 0, 4, Re...|[[document, 0, 12...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 4, Re...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+-------------------------------+
|result                         |
+-------------------------------+
|[La FIFA, Zidane, Materazzi]|
|[Reims, Domani, Mondiali]      |
+-------------------------------+
*/

Spanish

Pipeline Name Build lang Description Offline
Explain Document Small explain_document_sm 2.4.0 es   Download
Explain Document Medium explain_document_md 2.4.0 es   Download
Explain Document Large explain_document_lg 2.4.0 es   Download
Entity Recognizer Small entity_recognizer_sm 2.4.0 es   Download
Entity Recognizer Medium entity_recognizer_md 2.4.0 es   Download
Entity Recognizer Large entity_recognizer_lg 2.4.0 es   Download
Feature Description
Lemma Trained by Lemmatizer annotator on lemmatization-lists by Michal Měchura
POS Trained by PerceptronApproach annotator on the Universal Dependencies
NER Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the WikiNER corpus and supports the identification of PER, LOC, ORG and MISC entities
Size Model size indicator, sm, md, and lg. The small pipelines use glove_100d, the medium pipelines use glove_6B_300, and large pipelines use glove_840B_300 WordEmbeddings

Russian

Pipeline Name Build lang Description Offline
Explain Document Small explain_document_sm 2.4.4 ru   Download
Explain Document Medium explain_document_md 2.4.4 ru   Download
Explain Document Large explain_document_lg 2.4.4 ru   Download
Entity Recognizer Small entity_recognizer_sm 2.4.4 ru   Download
Entity Recognizer Medium entity_recognizer_md 2.4.4 ru   Download
Entity Recognizer Large entity_recognizer_lg 2.4.4 ru   Download
Feature Description
Lemma Trained by Lemmatizer annotator on the Universal Dependencies
POS Trained by PerceptronApproach annotator on the Universal Dependencies
NER Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the WikiNER corpus and supports the identification of PER, LOC, ORG and MISC entities

How to use

Online

To use Spark NLP pretrained pipelines, you can call PretrainedPipeline with pipeline’s name and its language (default is en):

pipeline = PretrainedPipeline('explain_document_dl', lang='en')

Same in Scala

val pipeline = PretrainedPipeline("explain_document_dl", lang="en")

Offline

If you have any trouble using online pipelines or models in your environment (maybe it’s air-gapped), you can directly download them for offline use.

After downloading offline models/pipelines and extracting them, here is how you can use them iside your code (the path could be a shared storage like HDFS in a cluster):

val advancedPipeline = PipelineModel.load("/tmp/explain_document_dl_en_2.0.2_2.4_1556530585689/")
// To use the loaded Pipeline for prediction
advancedPipeline.transform(predictionDF)
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