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JohnSnowLabs
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State of the Art Natural Language Processing

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Spark NLP: State of the Art Natural Language Processing

build Maven Central PyPI version Anaconda-Cloud License

Spark NLP is a Natural Language Processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. Spark NLP comes with 1100+ pretrained pipelines and models in more than 192+ languages. It supports state-of-the-art transformers such as BERT, XLNet, ELMO, ALBERT, and Universal Sentence Encoder that can be used seamlessly in a cluster. It also offers Tokenization, Word Segmentation, Part-of-Speech Tagging, Named Entity Recognition, Dependency Parsing, Spell Checking, Multi-class Text Classification, Multi-class Sentiment Analysis, Machine Translation (+180 languages), Summarization and Question Answering (Google T5), and many more NLP tasks.

Project's website

Take a look at our official Spark NLP page: http://nlp.johnsnowlabs.com/ for user documentation and examples

Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • YouTube Spark NLP video tutorials

Table of contents

Features

  • Tokenization
  • Trainable Word Segmentation
  • Stop Words Removal
  • Token Normalizer
  • Document Normalizer
  • Stemmer
  • Lemmatizer
  • NGrams
  • Regex Matching
  • Text Matching
  • Chunking
  • Date Matcher
  • Sentence Detector
  • Deep Sentence Detector (Deep learning)
  • Dependency parsing (Labeled/unlabeled)
  • Part-of-speech tagging
  • Sentiment Detection (ML models)
  • Spell Checker (ML and DL models)
  • Word Embeddings (GloVe and Word2Vec)
  • BERT Embeddings (TF Hub models)
  • ELMO Embeddings (TF Hub models)
  • ALBERT Embeddings (TF Hub models)
  • XLNet Embeddings
  • Universal Sentence Encoder (TF Hub models)
  • BERT Sentence Embeddings (42 TF Hub models)
  • Sentence Embeddings
  • Chunk Embeddings
  • Unsupervised keywords extraction
  • Language Detection & Identification (up to 375 languages)
  • Multi-class Sentiment analysis (Deep learning)
  • Multi-label Sentiment analysis (Deep learning)
  • Multi-class Text Classification (Deep learning)
  • Neural Machine Translation
  • Text-To-Text Transfer Transformer (Google T5)
  • Named entity recognition (Deep learning)
  • Easy TensorFlow integration
  • GPU Support
  • Full integration with Spark ML functions
  • +710 pre-trained models in +192 languages!
  • +450 pre-trained pipelines in +192 languages!
  • Multi-lingual NER models: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, German, Hebrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, and Urdu.

Requirements

To use Spark NLP you need the following requirements:

  • Java 8
  • Apache Spark 3.1.x (or 3.0.x, or 2.4.x, or 2.3.x)

Quick Start

This is a quick example of how to use Spark NLP pre-trained pipeline in Python and PySpark:

$ java -version
# should be Java 8 (Oracle or OpenJDK)
$ conda create -n sparknlp python=3.7 -y
$ conda activate sparknlp
# spark-nlp by default is based on pyspark 3.x
$ pip install spark-nlp==3.0.2 pyspark==3.1.1

In Python console or Jupyter

Python3
kernel:
# Import Spark NLP
from sparknlp.base import *
from sparknlp.annotator import *
from sparknlp.pretrained import PretrainedPipeline
import sparknlp

Start SparkSession with Spark NLP

start() functions has 4 parameters: gpu, spark23, spark24, and memory

sparknlp.start(gpu=True) will start the session with GPU support

sparknlp.start(spark23=True) is when you have Apache Spark 2.3.x installed

sparknlp.start(spark24=True) is when you have Apache Spark 2.4.x installed

sparknlp.start(memory="16G") to change the default driver memory in SparkSession

spark = sparknlp.start()

Download a pre-trained pipeline

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

Your testing dataset

text = """ The Mona Lisa is a 16th century oil painting created by Leonardo. It's held at the Louvre in Paris. """

Annotate your testing dataset

result = pipeline.annotate(text)

What's in the pipeline

list(result.keys()) Output: ['entities', 'stem', 'checked', 'lemma', 'document', 'pos', 'token', 'ner', 'embeddings', 'sentence']

Check the results

result['entities'] Output: ['Mona Lisa', 'Leonardo', 'Louvre', 'Paris']

For more examples, you can visit our dedicated repository to showcase all Spark NLP use cases!

Apache Spark Support

Spark NLP 3.0.2 has been built on top of Apache Spark 3.x while fully supports Apache Spark 2.3.x and Apache Spark 2.4.x:

| Spark NLP | Apache Spark 2.3.x | Apache Spark 2.4.x | Apache Spark 3.0.x | Apache Spark 3.1.x | |-------------|-----------------------|--------------------|--------------------|--------------------| | 3.0.x |YES |YES |YES |YES | | 2.7.x |YES |YES |NO |NO | | 2.6.x |YES |YES |NO |NO | | 2.5.x |YES |YES |NO |NO | | 2.4.x |Partially |YES |NO |NO | | 1.8.x |Partially |YES |NO |NO | | 1.7.x |YES |NO |NO |NO | | 1.6.x |YES |NO |NO |NO | | 1.5.x |YES |NO |NO |NO |

NOTE: Starting 3.0.2 release, the default

spark-nlp
and
spark-nlp-gpu
pacakges are based on Scala 2.12 and Apache Spark 3.x by default.

NOTE: Starting the 3.0.2 release, we support all major releases of Apache Spark 2.3.x, Apache Spark 2.4.x, Apache Spark 3.0.x, and Apache Spark 3.1.x

Find out more about

Spark NLP
versions from our release notes.

Databricks Support

Spark NLP 3.0.2 has been tested and is compatible with the following runtimes:

  • 5.5 LTS
  • 5.5 LTS ML & GPU
  • 6.4
  • 6.4 ML & GPU
  • 7.3
  • 7.3 ML & GPU
  • 7.4
  • 7.4 ML & GPU
  • 7.5
  • 7.5 ML & GPU
  • 7.6
  • 7.6 ML & GPU
  • 8.0
  • 8.0 ML
  • 8.1 Beta

NOTE: The Databricks 8.1 Beta ML with GPU is not supported in Spark NLP 3.0.2 due to its default CUDA 11.x incompatibility

EMR Support

Spark NLP 3.0.2 has been tested and is compatible with the following EMR releases:

  • emr-5.20.0
  • emr-5.21.0
  • emr-5.21.1
  • emr-5.22.0
  • emr-5.23.0
  • emr-5.24.0
  • emr-5.24.1
  • emr-5.25.0
  • emr-5.26.0
  • emr-5.27.0
  • emr-5.28.0
  • emr-5.29.0
  • emr-5.30.0
  • emr-5.30.1
  • emr-5.31.0
  • emr-5.32.0
  • emr-6.1.0
  • emr-6.2.0

Full list of Amazon EMR 5.x releases Full list of Amazon EMR 6.x releases

NOTE: The EMR 6.0.0 is not supported by Spark NLP 3.0.2

Usage

Spark Packages

Command line (requires internet connection)

Spark NLP supports all major releases of Apache Spark 2.3.x, Apache Spark 2.4.x, Apache Spark 3.0.x, and Apache Spark 3.1.x. That's being said, you need to choose the right package for the right Apache Spark major release:

Apache Spark 3.x (3.0.x and 3.1.x - Scala 2.12)

# CPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2

The

spark-nlp
has been published to the Maven Repository.
# GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:3.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:3.0.2

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:3.0.2

The

spark-nlp-gpu
has been published to the Maven Repository.

Apache Spark 2.4.x (Scala 2.11)

# CPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-spark24_2.11:3.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-spark24_2.11:3.0.2

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-spark24_2.11:3.0.2

The

spark-nlp-spark24
has been published to the Maven Repository.
# GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu-spark24_2.11:3.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu-spark24_2.11:3.0.2

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu-spark24_2.11:3.0.2

The

spark-nlp-gpu-spark24
has been published to the Maven Repository.

Apache Spark 2.3.x (Scala 2.11)

# CPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-spark23_2.11:3.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-spark23_2.11:3.0.2

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-spark23_2.11:3.0.2

The

spark-nlp-spark23
has been published to the Maven Repository.
# GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu-spark23_2.11:3.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu-spark23_2.11:3.0.2

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu-spark23_2.11:3.0.2

The

spark-nlp-gpu-spark23
has been published to the Maven Repository.

NOTE: In case you are using large pretrained models like UniversalSentenceEncoder, you need to have the following set in your SparkSession:

spark-shell \
  --driver-memory 16g \
  --conf spark.kryoserializer.buffer.max=2000M \
  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2

Scala

Our package is deployed to maven central. To add this package as a dependency in your application:

Maven

spark-nlp on Apache Spark 3.x:

    com.johnsnowlabs.nlp
    spark-nlp_2.12
    3.0.2

spark-nlp-gpu:

    com.johnsnowlabs.nlp
    spark-nlp-gpu_2.12
    3.0.2

spark-nlp on Apache Spark 2.4.x:

    com.johnsnowlabs.nlp
    spark-nlp-spark24_2.11
    3.0.2

spark-nlp-gpu:

    com.johnsnowlabs.nlp
    spark-nlp-gpu_2.11
    3.0.2/version>

spark-nlp on Apache Spark 2.3.x:

    com.johnsnowlabs.nlp
    spark-nlp-spark23_2.11
    3.0.2

spark-nlp-gpu:

    com.johnsnowlabs.nlp
    spark-nlp-gpu-spark23_2.11
    3.0.2

SBT

spark-nlp on Apache Spark 3.x.x:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "3.0.2"

spark-nlp-gpu:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "3.0.2"

spark-nlp on Apache Spark 2.4.x:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-spark24" % "3.0.2"

spark-nlp-gpu:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu-spark24" % "3.0.2"

spark-nlp on Apache Spark 2.3.x:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-spark23
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-spark23" % "3.0.2"

spark-nlp-gpu:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu-spark23
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu-spark23" % "3.0.2"

Maven Central: https://mvnrepository.com/artifact/com.johnsnowlabs.nlp

If you are interested, there is a simple SBT project for Spark NLP to guide you on how to use it in your projects Spark NLP SBT Starter

Python

Python without explicit Pyspark installation

Pip/Conda

If you installed pyspark through pip/conda, you can install

spark-nlp
through the same channel.

Pip:

pip install spark-nlp==3.0.2

Conda:

conda install -c johnsnowlabs spark-nlp

PyPI spark-nlp package / Anaconda spark-nlp package

Then you'll have to create a SparkSession either from Spark NLP:

import sparknlp

spark = sparknlp.start()

or manually:

spark = SparkSession.builder \
    .appName("Spark NLP")\
    .master("local[4]")\
    .config("spark.driver.memory","16G")\
    .config("spark.driver.maxResultSize", "0") \    
    .config("spark.kryoserializer.buffer.max", "2000M")\
    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2")\
    .getOrCreate()

If using local jars, you can use

spark.jars
instead for comma-delimited jar files. For cluster setups, of course, you'll have to put the jars in a reachable location for all driver and executor nodes.

Quick example:

import sparknlp
from sparknlp.pretrained import PretrainedPipeline

#create or get Spark Session

spark = sparknlp.start()

sparknlp.version() spark.version

#download, load and annotate a text by pre-trained pipeline

pipeline = PretrainedPipeline('recognize_entities_dl', 'en') result = pipeline.annotate('The Mona Lisa is a 16th century oil painting created by Leonardo')

Compiled JARs

Build from source

spark-nlp

  • FAT-JAR for CPU on Apache Spark 3.x.x
sbt assembly
  • FAT-JAR for GPU on Apache Spark 3.x.x
sbt -Dis_gpu=true assembly
  • FAT-JAR for CPU on Apache Spark 2.4.x
sbt -Dis_spark24=true assembly
  • FAT-JAR for GPU on Apache Spark 2.4.x
sbt -Dis_gpu=true -Dis_spark24=true assembly
  • FAT-JAR for CPU on Apache Spark 2.3.x
sbt -Dis_spark23=true assembly
  • FAT-JAR for GPU on Apache Spark 2.3.x
sbt -Dis_gpu=true -Dis_spark23=true assembly

Using the jar manually

If for some reason you need to use the JAR, you can either download the Fat JARs provided here or download it from Maven Central.

To add JARs to spark programs use the

--jars
option:
spark-shell --jars spark-nlp.jar

The preferred way to use the library when running spark programs is using the

--packages
option as specified in the
spark-packages
section.

Apache Zeppelin

Use either one of the following options

  • Add the following Maven Coordinates to the interpreter's library list
com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2
  • Add a path to pre-built jar from here in the interpreter's library list making sure the jar is available to driver path

Python in Zeppelin

Apart from the previous step, install the python module through pip

pip install spark-nlp==3.0.2

Or you can install

spark-nlp
from inside Zeppelin by using Conda:
python.conda install -c johnsnowlabs spark-nlp

Configure Zeppelin properly, use cells with %spark.pyspark or any interpreter name you chose.

Finally, in Zeppelin interpreter settings, make sure you set properly zeppelin.python to the python you want to use and install the pip library with (e.g.

python3
).

An alternative option would be to set

SPARK_SUBMIT_OPTIONS
(zeppelin-env.sh) and make sure
--packages
is there as shown earlier since it includes both scala and python side installation.

Q: What if I am still on Zeppelin 0.8.x that only supports Apache Spark 2.4.x?

A: You can simply use the

spark-nlp-spark24:3.0.2
package or Fat JAR instead.

Jupyter Notebook (Python)

Recomended:

The easiest way to get this done on Linux and macOS is to simply install

spark-nlp
and
pyspark
PyPI packages and launch the Jupyter from the same Python environment:
$ conda create -n sparknlp python=3.7 -y
$ conda activate sparknlp
# spark-nlp by default is based on pyspark 3.x
$ pip install spark-nlp==3.0.2 pyspark==3.1.1 jupyter
$ jupyter notebook

The you can use

python3
kernel to run your code with creating SparkSession via
spark = sparknlp.start()
.

Optional:

If you are in different operating systems and require to make Jupyter Notebook run by using pyspark, you can follow these steps:

export SPARK_HOME=/path/to/your/spark/folder
export PYSPARK_PYTHON=python3
export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS=notebook

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2

Alternatively, you can mix in using

--jars
option for pyspark +
pip install spark-nlp

If not using pyspark at all, you'll have to run the instructions pointed here

Google Colab Notebook

Google Colab is perhaps the easiest way to get started with spark-nlp. It requires no installation or setup other than having a Google account.

Run the following code in Google Colab notebook and start using spark-nlp right away.

# This is only to setup PySpark and Spark NLP on Colab
!wget http://setup.johnsnowlabs.com/colab.sh -O - | bash

This script comes with the two options to define

pyspark
and
spark-nlp
versions via options:
# -p is for pyspark
# -s is for spark-nlp
# by default they are set to the latest
!bash colab.sh -p 3.1.1 -s 3.0.2

Spark NLP quick start on Google Colab is a live demo on Google Colab that performs named entity recognitions and sentiment analysis by using Spark NLP pretrained pipelines.

Kaggle Kernel

Run the following code in Kaggle Kernel and start using spark-nlp right away.

# Let's setup Kaggle for Spark NLP and PySpark
!wget http://setup.johnsnowlabs.com/kaggle.sh -O - | bash

Spark NLP quick start on Kaggle Kernel is a live demo on Kaggle Kernel that performs named entity recognitions by using Spark NLP pretrained pipeline.

Databricks Cluster

  1. Create a cluster if you don't have one already

  2. On a new cluster or existing one you need to add the following to the

    Advanced Options -> Spark
    tab:
    spark.kryoserializer.buffer.max 2000M
    spark.serializer org.apache.spark.serializer.KryoSerializer
    
  3. In

    Libraries
    tab inside your cluster you need to follow these steps:

    3.1. Install New -> PyPI ->

    spark-nlp
    -> Install

    3.2. Install New -> Maven -> Coordinates ->

    com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2
    -> Install
  4. Now you can attach your notebook to the cluster and use Spark NLP!

NOTE: If you are launching a Databricks runtime that is not based on Apache Spark 3.x please choose a compatible Spark NLP package

EMR Cluster

To lanuch EMR cluster with Apache Spark/PySpark and Spark NLP correctly you need to have bootstrap and software configuration.

A sample of your bootstrap script

#!/bin/bash
set -x -e

echo -e 'export PYSPARK_PYTHON=/usr/bin/python3 export HADOOP_CONF_DIR=/etc/hadoop/conf export SPARK_JARS_DIR=/usr/lib/spark/jars export SPARK_HOME=/usr/lib/spark' >> $HOME/.bashrc && source $HOME/.bashrc

sudo python3 -m pip install awscli boto spark-nlp

set +x exit 0

A sample of your software configuration in JSON on S3 (must be public access):

[{
  "Classification": "spark-env",
  "Configurations": [{
    "Classification": "export",
    "Properties": {
      "PYSPARK_PYTHON": "/usr/bin/python3"
    }
  }]
},
{
  "Classification": "spark-defaults",
    "Properties": {
      "spark.yarn.stagingDir": "hdfs:///tmp",
      "spark.yarn.preserve.staging.files": "true",
      "spark.kryoserializer.buffer.max": "2000M",
      "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
      "spark.driver.maxResultSize": "0",
      "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.2"
    }
}
]

A sample of AWS CLI to launch EMR cluster:

```.sh aws emr create-cluster
--name "Spark NLP 3.0.2"
--release-label emr-6.2.0
--applications Name=Hadoop Name=Spark Name=Hive
--instance-type m4.4xlarge
--instance-count 3
--use-default-roles
--log-uri "s3:///"
--bootstrap-actions Path=s3:///emr-bootstrap.sh,Name=custome
--configurations "https:///sparknlp-config.json"
--ec2-attributes KeyName=,EmrManagedMasterSecurityGroup=,EmrManagedSlaveSecurityGroup=
--profile

S3 Cluster

With no Hadoop configuration

If your distributed storage is S3 and you don't have a standard Hadoop configuration (i.e. fs.defaultFS) You need to specify where in the cluster distributed storage you want to store Spark NLP's tmp files. First, decide where you want to put your application.conf file

import com.johnsnowlabs.util.ConfigLoader
ConfigLoader.setConfigPath("/somewhere/to/put/application.conf")

And then we need to put in such application.conf the following content

sparknlp {
  settings {
    cluster_tmp_dir = "somewhere in s3n:// path to some folder"
  }
}

Pipelines and Models

Pipelines

Spark NLP offers more than

450+ pre-trained pipelines
in
192 languages
. |
dependency_parse
| 2.4.0 |
en
|

Quick example:

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 2.5.0 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]| +----------------------------------+ */

Please check out our Models Hub for the full list of pre-trained pipelines with examples, demos, benchmarks, and more

Models

Spark NLP offers more than

710+ pre-trained models
in
192 languages
.

Some of the selected languages:

Afrikaans, Arabic, Armenian, Basque, Bengali, Breton, Bulgarian, Catalan, Czech, Dutch, English, Esperanto, Finnish, French, Galician, German, Greek, Hausa, Hebrew, Hindi, Hungarian, Indonesian, Irish, Italian, Japanese, Latin, Latvian, Marathi, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Somali, Southern Sotho, Spanish, Swahili, Swedish, Tswana, Turkish, Ukrainian, Zulu

Quick online example:

# load NER model trained by deep learning approach and GloVe word embeddings
ner_dl = NerDLModel.pretrained('ner_dl')
# load NER model trained by deep learning approach and BERT word embeddings
ner_bert = NerDLModel.pretrained('ner_dl_bert')
// load French POS tagger model trained by Universal Dependencies
val french_pos = PerceptronModel.pretrained("pos_ud_gsd", lang="fr")
// load Italain LemmatizerModel
val italian_lemma = LemmatizerModel.pretrained("lemma_dxc", lang="it")

Quick offline example:

  • Loading
    PerceptronModel
    annotator model inside Spark NLP Pipeline
val french_pos = PerceptronModel.load("/tmp/pos_ud_gsd_fr_2.0.2_2.4_1556531457346/")
      .setInputCols("document", "token")
      .setOutputCol("pos")

Please check out our Models Hub for the full list of pre-trained models with examples, demo, benchmark, and more

Offline

Spark NLP library and all the pre-trained models/pipelines can be used entirely offline with no access to the Internet. If you are behind a proxy or a firewall with no access to the Maven repository (to download packages) or/and no access to S3 (to automatically download models and pipelines), you can simply follow the instructions to have Spark NLP without any limitations offline:

  • Instead of using the Maven package, you need to load our Fat JAR
  • Instead of using PretrainedPipeline for pretrained pipelines or the
    .pretrained()
    function to download pretrained models, you will need to manually download your pipeline/model from Models Hub, extract it, and load it.

Example of

SparkSession
with Fat JAR to have Spark NLP offline:
spark = SparkSession.builder \
    .appName("Spark NLP")\
    .master("local[*]")\
    .config("spark.driver.memory","16G")\
    .config("spark.driver.maxResultSize", "0") \    
    .config("spark.kryoserializer.buffer.max", "2000M")\
    .config("spark.jars", "/tmp/spark-nlp-assembly-3.0.2.jar")\
    .getOrCreate()
  • You can download provided Fat JARs from each release notes, please pay attention to pick the one that suits your environment depending on the device (CPU/GPU) and Apache Spark version (2.3.x, 2.4.x, and 3.x)
  • If you are local, you can load the Fat JAR from your local FileSystem, however, if you are in a cluster setup you need to put the Fat JAR on a distributed FileSystem such as HDFS, DBFS, S3, etc. (i.e.,
    hdfs:///tmp/spark-nlp-assembly-3.0.2.jar
    )

Example of using pretrained Models and Pipelines in offline:

# instead of using pretrained() for online:
# french_pos = PerceptronModel.pretrained("pos_ud_gsd", lang="fr")
# you download this model, extract it, and use .load
french_pos = PerceptronModel.load("/tmp/pos_ud_gsd_fr_2.0.2_2.4_1556531457346/")\
      .setInputCols("document", "token")\
      .setOutputCol("pos")

example for pipelines

instead of using PretrainedPipeline

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

you download this pipeline, extract it, and use PipelineModel

PipelineModel.load("/tmp/explain_document_dl_en_2.0.2_2.4_1556530585689/")

  • Since you are downloading and loading models/pipelines manually, this means Spark NLP is not downloading the most recent and compatible models/pipelines for you. Choosing the right model/pipeline is on you
  • If you are local, you can load the model/pipeline from your local FileSystem, however, if you are in a cluster setup you need to put the model/pipeline on a distributed FileSystem such as HDFS, DBFS, S3, etc. (i.e.,
    hdfs:///tmp/explain_document_dl_en_2.0.2_2.4_1556530585689/
    )

Examples

Need more examples? Check out our dedicated Spark NLP Showcase repository to showcase all Spark NLP use cases!

Also, don't forget to check Spark NLP in Action built by Streamlit.

All examples: spark-nlp-workshop

FAQ

Check our Articles and Videos page here

Citation

We have published a paper that you can cite for the Spark NLP library:

@article{KOCAMAN2021100058,
    title = {Spark NLP: Natural language understanding at scale},
    journal = {Software Impacts},
    pages = {100058},
    year = {2021},
    issn = {2665-9638},
    doi = {https://doi.org/10.1016/j.simpa.2021.100058},
    url = {https://www.sciencedirect.com/science/article/pii/S2665963821000063},
    author = {Veysel Kocaman and David Talby},
    keywords = {Spark, Natural language processing, Deep learning, Tensorflow, Cluster},
    abstract = {Spark NLP is a Natural Language Processing (NLP) library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that can scale easily in a distributed environment. Spark NLP comes with 1100+ pretrained pipelines and models in more than 192+ languages. It supports nearly all the NLP tasks and modules that can be used seamlessly in a cluster. Downloaded more than 2.7 million times and experiencing 9x growth since January 2020, Spark NLP is used by 54% of healthcare organizations as the world’s most widely used NLP library in the enterprise.}
    }
}

Contributing

We appreciate any sort of contributions:

  • ideas
  • feedback
  • documentation
  • bug reports
  • NLP training and testing corpora
  • Development and testing

Clone the repo and submit your pull-requests! Or directly create issues in this repo.

Contact

[email protected]

John Snow Labs

http://johnsnowlabs.com

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