/spark-nlp

State of the Art Natural Language Processing

Primary LanguageScalaApache License 2.0Apache-2.0

Spark NLP: State-of-the-Art Natural Language Processing

Spark NLP is a state-of-the-art Natural Language Processing library built on top of Apache Spark. It provides simple, performant & accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. Spark NLP comes with 11000+ pretrained pipelines and models in more than 200+ languages. It also offers tasks such as Tokenization, Word Segmentation, Part-of-Speech Tagging, Word and Sentence Embeddings, Named Entity Recognition, Dependency Parsing, Spell Checking, Text Classification, Sentiment Analysis, Token Classification, Machine Translation (+180 languages), Summarization, Question Answering, Table Question Answering, Text Generation, Image Classification, Automatic Speech Recognition, and many more NLP tasks.

Spark NLP is the only open-source NLP library in production that offers state-of-the-art transformers such as BERT, CamemBERT, ALBERT, ELECTRA, XLNet, DistilBERT, RoBERTa, DeBERTa, XLM-RoBERTa, Longformer, ELMO, Universal Sentence Encoder, Google T5, MarianMT, GPT2, and Vision Transformers (ViT) not only to Python and R, but also to JVM ecosystem (Java, Scala, and Kotlin) at scale by extending Apache Spark natively.

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)
  • SpanBertCorefModel (Coreference Resolution)
  • Part-of-speech tagging
  • Sentiment Detection (ML models)
  • Spell Checker (ML and DL models)
  • Word Embeddings (GloVe and Word2Vec)
  • Doc2Vec (based on Word2Vec)
  • BERT Embeddings (TF Hub & HuggingFace models)
  • DistilBERT Embeddings (HuggingFace models)
  • CamemBERT Embeddings (HuggingFace models)
  • RoBERTa Embeddings (HuggingFace models)
  • DeBERTa Embeddings (HuggingFace v2 & v3 models)
  • XLM-RoBERTa Embeddings (HuggingFace models)
  • Longformer Embeddings (HuggingFace models)
  • ALBERT Embeddings (TF Hub & HuggingFace models)
  • XLNet Embeddings
  • ELMO Embeddings (TF Hub models)
  • Universal Sentence Encoder (TF Hub models)
  • BERT Sentence Embeddings (TF Hub & HuggingFace models)
  • RoBerta Sentence Embeddings (HuggingFace models)
  • XLM-RoBerta Sentence Embeddings (HuggingFace 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)
  • BERT for Token & Sequence Classification
  • DistilBERT for Token & Sequence Classification
  • CamemBERT for Token & Sequence Classification
  • ALBERT for Token & Sequence Classification
  • RoBERTa for Token & Sequence Classification
  • DeBERTa for Token & Sequence Classification
  • XLM-RoBERTa for Token & Sequence Classification
  • XLNet for Token & Sequence Classification
  • Longformer for Token & Sequence Classification
  • BERT for Token & Sequence Classification
  • DistilBERT for Question Answering
  • ALBERT for Question Answering
  • RoBERTa for Question Answering
  • DeBERTa for Question Answering
  • XLM-RoBERTa for Question Answering
  • Longformer for Question Answering
  • Table Question Answering (TAPAS)
  • Neural Machine Translation (MarianMT)
  • Text-To-Text Transfer Transformer (Google T5)
  • Generative Pre-trained Transformer 2 (OpenAI GPT2)
  • Vision Transformer (ViT)
  • Automatic Speech Recognition (Wav2Vec2)
  • Named entity recognition (Deep learning)
  • Easy TensorFlow integration
  • GPU Support
  • Full integration with Spark ML functions
  • +6150+ pre-trained models in +200 languages!
  • +1840 pre-trained pipelines in +200 languages!
  • Multi-lingual NER models: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, German, Hebrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Urdu, and more.

Requirements

To use Spark NLP you need the following requirements:

  • Java 8 and 11
  • Apache Spark 3.3.x, 3.2.x, 3.1.x, 3.0.x

GPU (optional):

Spark NLP 4.2.4 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support:

  • NVIDIA® GPU drivers version 450.80.02 or higher
  • CUDA® Toolkit 11.2
  • cuDNN SDK 8.1.0

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 or 11 (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==4.2.4 pyspark==3.2.1

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 3 parameters: gpu, m1, and memory
# sparknlp.start(gpu=True) will start the session with GPU support
# sparknlp.start(m1=True) will start the session with macOS M1 support
# 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 4.2.4 has been built on top of Apache Spark 3.2 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x:

Spark NLP Apache Spark 2.3.x Apache Spark 2.4.x Apache Spark 3.0.x Apache Spark 3.1.x Apache Spark 3.2.x Apache Spark 3.3.x
4.2.x NO NO YES YES YES YES
4.1.x NO NO YES YES YES YES
4.0.x NO NO YES YES YES YES
3.4.x YES YES YES YES Partially N/A
3.3.x YES YES YES YES NO NO
3.2.x YES YES YES YES NO NO
3.1.x YES YES YES YES NO NO
3.0.x YES YES YES YES NO NO
2.7.x YES YES NO NO NO NO

NOTE: Starting 4.0.0 release, the default spark-nlp and spark-nlp-gpu packages are based on Scala 2.12.15 and Apache Spark 3.2 by default.

Find out more about Spark NLP versions from our release notes.

Scala and Python Support

Spark NLP Python 3.6 Python 3.7 Python 3.8 Python 3.9 Scala 2.11 Scala 2.12
4.2.x YES YES YES YES NO YES
4.1.x YES YES YES YES NO YES
4.0.x YES YES YES YES NO YES
3.4.x YES YES YES YES YES YES
3.3.x YES YES YES NO YES YES
3.2.x YES YES YES NO YES YES
3.1.x YES YES YES NO YES YES
3.0.x YES YES YES NO YES YES
2.7.x YES YES NO NO YES NO

Databricks Support

Spark NLP 4.2.4 has been tested and is compatible with the following runtimes:

CPU:

  • 7.3
  • 7.3 ML
  • 9.1
  • 9.1 ML
  • 10.1
  • 10.1 ML
  • 10.2
  • 10.2 ML
  • 10.3
  • 10.3 ML
  • 10.4
  • 10.4 ML
  • 10.5
  • 10.5 ML
  • 11.0
  • 11.0 ML
  • 11.1
  • 11.1 ML
  • 11.2
  • 11.2 ML

GPU:

  • 9.1 ML & GPU
  • 10.1 ML & GPU
  • 10.2 ML & GPU
  • 10.3 ML & GPU
  • 10.4 ML & GPU
  • 10.5 ML & GPU
  • 11.0 ML & GPU
  • 11.1 ML & GPU
  • 11.2 ML & GPU

NOTE: Spark NLP 4.0.x is based on TensorFlow 2.7.x which is compatible with CUDA11 and cuDNN 8.0.2. The only Databricks runtimes supporting CUDA 11 are 9.x and above as listed under GPU.

EMR Support

Spark NLP 4.2.4 has been tested and is compatible with the following EMR releases:

  • emr-6.2.0
  • emr-6.3.0
  • emr-6.3.1
  • emr-6.4.0
  • emr-6.5.0
  • emr-6.6.0
  • emr-6.7.0

Full list of Amazon EMR 6.x releases

NOTE: The EMR 6.1.0 and 6.1.1 are not supported.

Usage

Packages Cheatsheet

This is a cheatsheet for corresponding Spark NLP Maven package to Apache Spark / PySpark major version:

Apache Spark Spark NLP on CPU Spark NLP on GPU Spark NLP on AArch64 (linux) Spark NLP on M1
3.0/3.1/3.2/3.3 spark-nlp spark-nlp-gpu spark-nlp-aarch64 spark-nlp-m1
Start Function sparknlp.start() sparknlp.start(gpu=True) sparknlp.start(aarch64=True) sparknlp.start(m1=True)

NOTE: M1 and AArch64 are under experimental support. Access and support to these architectures are limited by the community and we had to build most of the dependencies by ourselves to make them compatible. We support these two architectures, however, they may not work in some environments.

Spark Packages

Command line (requires internet connection)

Spark NLP supports all major releases of Apache Spark 3.0.x, Apache Spark 3.1.x, Apache Spark 3.2.x, and Apache Spark 3.3.x.

Apache Spark 3.x (3.0.x, 3.1.x, 3.2.x, and 3.3.x - Scala 2.12)

# CPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4

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

# GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.2.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.2.4

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.2.4

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

# AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.2.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.2.4

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.2.4

The spark-nlp-aarch64 has been published to the Maven Repository.

# M1

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-m1_2.12:4.2.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-m1_2.12:4.2.4

spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-m1_2.12:4.2.4

The spark-nlp-m1 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_2.12:4.2.4

Scala

Spark NLP supports Scala 2.12.15 if you are using Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x versions. Our packages are deployed to Maven central. To add any of our packages as a dependency in your application you can follow these coordinates:

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>4.2.4</version>
</dependency>

spark-nlp-gpu:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>4.2.4</version>
</dependency>

spark-nlp-aarch64:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64 -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>4.2.4</version>
</dependency>

spark-nlp-m1:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-m1 -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-m1_2.12</artifactId>
    <version>4.2.4</version>
</dependency>

SBT

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.2.4"

spark-nlp-gpu:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.2.4"

spark-nlp-aarch64:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.2.4"

spark-nlp-m1:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-m1
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-m1" % "4.2.4"

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

Spark NLP supports Python 3.6.x and above depending on your major PySpark version.

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==4.2.4

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[*]")\
    .config("spark.driver.memory","16G")\
    .config("spark.driver.maxResultSize", "0") \
    .config("spark.kryoserializer.buffer.max", "2000M")\
    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4")\
    .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 3.0.x, 3.1.x, 3.2.x, and 3.3.x
sbt assembly
  • FAT-JAR for GPU on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x
sbt -Dis_gpu=true assembly
  • FAT-JAR for M! on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x
sbt -Dis_m1=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_2.12:4.2.4
  • 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==4.2.4

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.

Jupyter Notebook (Python)

Recommended:

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=3.8 -y
$ conda activate sparknlp
# spark-nlp by default is based on pyspark 3.x
$ pip install spark-nlp==4.2.4 pyspark==3.2.1 jupyter
$ jupyter notebook

Then 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_2.12:4.2.4

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 https://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
# -g will enable upgrading libcudnn8 to 8.1.0 on Google Colab for GPU usage
# by default they are set to the latest
!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.1 -s 4.2.4

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 https://setup.johnsnowlabs.com/kaggle.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
# -g will enable upgrading libcudnn8 to 8.1.0 on Kaggle for GPU usage
# by default they are set to the latest
!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.1 -s 4.2.4

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==4.2.4 -> Install

    3.2. Install New -> Maven -> Coordinates -> com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4 -> Install

  4. Now you can attach your notebook to the cluster and use Spark NLP!

NOTE: Databricks' runtimes support different Apache Spark major releases. Please make sure you choose the correct Spark NLP Maven package name (Maven Coordinate) for your runtime from our Packages Cheatsheet

EMR Cluster

To launch EMR clusters 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_2.12:4.2.4"
    }
}]

A sample of AWS CLI to launch EMR cluster:

aws emr create-cluster \
--name "Spark NLP 4.2.4" \
--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://<S3_BUCKET>/" \
--bootstrap-actions Path=s3://<S3_BUCKET>/emr-bootstrap.sh,Name=custome \
--configurations "https://<public_access>/sparknlp-config.json" \
--ec2-attributes KeyName=<your_ssh_key>,EmrManagedMasterSecurityGroup=<security_group_with_ssh>,EmrManagedSlaveSecurityGroup=<security_group_with_ssh> \
--profile <aws_profile_credentials>

GCP Dataproc

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

At gcloud shell:

gcloud services enable dataproc.googleapis.com \
  compute.googleapis.com \
  storage-component.googleapis.com \
  bigquery.googleapis.com \
  bigquerystorage.googleapis.com
REGION=<region>
BUCKET_NAME=<bucket_name>
gsutil mb -c standard -l ${REGION} gs://${BUCKET_NAME}
REGION=<region>
ZONE=<zone>
CLUSTER_NAME=<cluster_name>
BUCKET_NAME=<bucket_name>

You can set image-version, master-machine-type, worker-machine-type, master-boot-disk-size, worker-boot-disk-size, num-workers as your needs. If you use the previous image-version from 2.0, you should also add ANACONDA to optional-components. And, you should enable gateway. Don't forget to set the maven coordinates for the jar in properties.

gcloud dataproc clusters create ${CLUSTER_NAME} \
  --region=${REGION} \
  --zone=${ZONE} \
  --image-version=2.0 \
  --master-machine-type=n1-standard-4 \
  --worker-machine-type=n1-standard-2 \
  --master-boot-disk-size=128GB \
  --worker-boot-disk-size=128GB \
  --num-workers=2 \
  --bucket=${BUCKET_NAME} \
  --optional-components=JUPYTER \
  --enable-component-gateway \
  --metadata 'PIP_PACKAGES=spark-nlp spark-nlp-display google-cloud-bigquery google-cloud-storage' \
  --initialization-actions gs://goog-dataproc-initialization-actions-${REGION}/python/pip-install.sh \
  --properties spark:spark.serializer=org.apache.spark.serializer.KryoSerializer,spark:spark.driver.maxResultSize=0,spark:spark.kryoserializer.buffer.max=2000M,spark:spark.jars.packages=com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4

  1. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI.

  2. Now, you can attach your notebook to the cluster and use the Spark NLP!

Spark NLP Configuration

You can change the following Spark NLP configurations via Spark Configuration:

Property Name Default Meaning
spark.jsl.settings.pretrained.cache_folder ~/cache_pretrained The location to download and extract pretrained Models and Pipelines. By default, it will be in User's Home directory under cache_pretrained directory
spark.jsl.settings.storage.cluster_tmp_dir hadoop.tmp.dir The location to use on a cluster for temporarily files such as unpacking indexes for WordEmbeddings. By default, this locations is the location of hadoop.tmp.dir set via Hadoop configuration for Apache Spark. NOTE: S3 is not supported and it must be local, HDFS, or DBFS
spark.jsl.settings.annotator.log_folder ~/annotator_logs The location to save logs from annotators during training such as NerDLApproach, ClassifierDLApproach, SentimentDLApproach, MultiClassifierDLApproach, etc. By default, it will be in User's Home directory under annotator_logs directory
spark.jsl.settings.aws.credentials.access_key_id None Your AWS access key to use your S3 bucket to store log files of training models or access tensorflow graphs used in NerDLApproach
spark.jsl.settings.aws.credentials.secret_access_key None Your AWS secret access key to use your S3 bucket to store log files of training models or access tensorflow graphs used in NerDLApproach
spark.jsl.settings.aws.credentials.session_token None Your AWS MFA session token to use your S3 bucket to store log files of training models or access tensorflow graphs used in NerDLApproach
spark.jsl.settings.aws.s3_bucket None Your AWS S3 bucket to store log files of training models or access tensorflow graphs used in NerDLApproach
spark.jsl.settings.aws.region None Your AWS region to use your S3 bucket to store log files of training models or access tensorflow graphs used in NerDLApproach

How to set Spark NLP Configuration

SparkSession:

You can use .config() during SparkSession creation to set Spark NLP configurations.

from pyspark.sql import SparkSession

spark = SparkSession.builder \
        .master("local[*]") \
        .config("spark.driver.memory", "16G") \
        .config("spark.driver.maxResultSize", "0") \
        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \
        .config("spark.kryoserializer.buffer.max", "2000m") \
        .config("spark.jsl.settings.pretrained.cache_folder", "sample_data/pretrained") \
        .config("spark.jsl.settings.storage.cluster_tmp_dir", "sample_data/storage") \
        .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4") \
        .getOrCreate()

spark-shell:

spark-shell \
  --driver-memory 16g \
  --conf spark.driver.maxResultSize=0 \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer
  --conf spark.kryoserializer.buffer.max=2000M \
  --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \
  --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \
  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4

pyspark:

pyspark \
  --driver-memory 16g \
  --conf spark.driver.maxResultSize=0 \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer
  --conf spark.kryoserializer.buffer.max=2000M \
  --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \
  --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \
  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4

Databricks:

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
spark.jsl.settings.pretrained.cache_folder dbfs:/PATH_TO_CACHE
spark.jsl.settings.storage.cluster_tmp_dir dbfs:/PATH_TO_STORAGE
spark.jsl.settings.annotator.log_folder dbfs:/PATH_TO_LOGS

NOTE: If this is an existing cluster, after adding new configs or changing existing properties you need to restart it.

S3 Integration

In Spark NLP we can define S3 locations to:

  • Export log files of training models
  • Store tensorflow graphs used in NerDLApproach

Logging:

To configure S3 path for logging while training models. We need to set up AWS credentials as well as an S3 path

spark.conf.set("spark.jsl.settings.annotator.log_folder", "s3://my/s3/path/logs")
spark.conf.set("spark.jsl.settings.aws.credentials.access_key_id", "MY_KEY_ID")
spark.conf.set("spark.jsl.settings.aws.credentials.secret_access_key", "MY_SECRET_ACCESS_KEY")
spark.conf.set("spark.jsl.settings.aws.s3_bucket", "my.bucket")
spark.conf.set("spark.jsl.settings.aws.region", "my-region")

Now you can check the log on your S3 path defined in spark.jsl.settings.annotator.log_folder property. Make sure to use the prefix s3://, otherwise it will use the default configuration.

Tensorflow Graphs:

To reference S3 location for downloading graphs. We need to set up AWS credentials

spark.conf.set("spark.jsl.settings.aws.credentials.access_key_id", "MY_KEY_ID")
spark.conf.set("spark.jsl.settings.aws.credentials.secret_access_key", "MY_SECRET_ACCESS_KEY")
spark.conf.set("spark.jsl.settings.aws.region", "my-region")

MFA Configuration:

In case your AWS account is configured with MFA. You will need first to get temporal credentials and add session token to the configuration as shown in the examples below For logging:

spark.conf.set("spark.jsl.settings.aws.credentials.session_token", "MY_TOKEN")

An example of a bash script that gets temporal AWS credentials can be found here This script requires three arguments:

./aws_tmp_credentials.sh iam_user duration serial_number

Pipelines and Models

Pipelines

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

Showing Available Pipelines

There are functions in Spark NLP that will list all the available Pipelines of a particular language for you:

import com.johnsnowlabs.nlp.pretrained.ResourceDownloader

ResourceDownloader.showPublicPipelines(lang="en")
/*
+--------------------------------------------+------+---------+
| Pipeline                                   | lang | version |
+--------------------------------------------+------+---------+
| dependency_parse                           |  en  | 2.0.2   |
| analyze_sentiment_ml                       |  en  | 2.0.2   |
| check_spelling                             |  en  | 2.1.0   |
| match_datetime                             |  en  | 2.1.0   |
                               ...
| explain_document_ml                        |  en  | 3.1.3   |
+--------------------------------------------+------+---------+
*/

Or if we want to check for a particular version:

import com.johnsnowlabs.nlp.pretrained.ResourceDownloader

ResourceDownloader.showPublicPipelines(lang="en", version="3.1.0")
/*
+---------------------------------------+------+---------+
| Pipeline                              | lang | version |
+---------------------------------------+------+---------+
| dependency_parse                      |  en  | 2.0.2   |
                               ...
| clean_slang                           |  en  | 3.0.0   |
| clean_pattern                         |  en  | 3.0.0   |
| check_spelling                        |  en  | 3.0.0   |
| dependency_parse                      |  en  | 3.0.0   |
+---------------------------------------+------+---------+
*/

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

Models

Some 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 Italian 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")

Showing Available Models

There are functions in Spark NLP that will list all the available Models of a particular Annotator and language for you:

import com.johnsnowlabs.nlp.pretrained.ResourceDownloader

ResourceDownloader.showPublicModels(annotator="NerDLModel", lang="en")
/*
+---------------------------------------------+------+---------+
| Model                                       | lang | version |
+---------------------------------------------+------+---------+
| onto_100                                    |  en  | 2.1.0   |
| onto_300                                    |  en  | 2.1.0   |
| ner_dl_bert                                 |  en  | 2.2.0   |
| onto_100                                    |  en  | 2.4.0   |
| ner_conll_elmo                              |  en  | 3.2.2   |
+---------------------------------------------+------+---------+
*/

Or if we want to check for a particular version:

import com.johnsnowlabs.nlp.pretrained.ResourceDownloader

ResourceDownloader.showPublicModels(annotator="NerDLModel", lang="en", version="3.1.0")
/*
+----------------------------+------+---------+
| Model                      | lang | version |
+----------------------------+------+---------+
| onto_100                   |  en  | 2.1.0   |
| ner_aspect_based_sentiment |  en  | 2.6.2   |
| ner_weibo_glove_840B_300d  |  en  | 2.6.2   |
| nerdl_atis_840b_300d       |  en  | 2.7.1   |
| nerdl_snips_100d           |  en  | 2.7.3   |
+----------------------------+------+---------+
*/

And to see a list of available annotators, you can use:

import com.johnsnowlabs.nlp.pretrained.ResourceDownloader

ResourceDownloader.showAvailableAnnotators()
/*
AlbertEmbeddings
AlbertForTokenClassification
AssertionDLModel
...
XlmRoBertaSentenceEmbeddings
XlnetEmbeddings
*/

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-4.2.4.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 (3.0.x, 3.1.x, 3.2.x, and 3.3.x)
  • If you are local, you can load the Fat JAR from your local FileSystem, however, if you are in a cluster setup you need to put the Fat JAR on a distributed FileSystem such as HDFS, DBFS, S3, etc. (i.e., hdfs:///tmp/spark-nlp-assembly-4.2.4.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/S2665963.2.100063},
    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.

John Snow Labs

http://johnsnowlabs.com