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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: 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


  • 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, Chinese, Danish, Dutch, English, Finnish, French, German, Hewbrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, and Urdu.


In order to use Spark NLP you need the following requirements:

  • Java 8
  • Apache Spark 2.4.x (or Apache Spark 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=3.6 -y
$ conda activate sparknlp
$ pip install spark-nlp==2.7.2 pyspark==2.4.7

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 Spark Session with Spark NLP
# start() functions has two parameters: gpu and spark23
# sparknlp.start(gpu=True) will start the session with GPU support
# sparknlp.start(sparrk23=True) is when you have Apache Spark 2.3.x installed
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
Output: ['entities', 'stem', 'checked', 'lemma', 'document',
'pos', 'token', 'ner', 'embeddings', 'sentence']

# Check the results
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 2.7.2 has been built on top of Apache Spark 2.4.x and fully supports Apache Spark 2.3.x:

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

NOTE: Starting 2.5.4 release, we support both Apache Spark 2.4.x and Apache Spark 2.3.x at the same time.

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

Databricks Support

Spark NLP 2.7.2 has been tested and is compatible with the following runtimes:

  • 6.2
  • 6.2 ML
  • 6.3
  • 6.3 ML
  • 6.4
  • 6.4 ML
  • 6.5
  • 6.5 ML

EMR Support

Spark NLP 2.7.2 has been tested and is compatible with the following EMR releases:

  • 5.26.0
  • 5.27.0

Full list of EMR releases.


Spark Packages

Command line (requires internet connection)

This library has been uploaded to the spark-packages repository.

The benefit of spark-packages is that makes it available for both Scala-Java and Python

To use the most recent version on Apache Spark 2.4.x just add the --packages com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2 to you spark command:

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2
pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2
spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2

This can also be used to create a SparkSession manually by using the spark.jars.packages option in both Python and Scala.

NOTE: To use Spark NLP with GPU you can use the dedicated GPU package com.johnsnowlabs.nlp:spark-nlp-gpu_2.11:2.7.2

NOTE: To use Spark NLP on Apache Spark 2.3.x you should instead use the following packages:

  • CPU: com.johnsnowlabs.nlp:spark-nlp-spark23_2.11:2.7.2
  • GPU: com.johnsnowlabs.nlp:spark-nlp-gpu-spark23_2.11:2.7.2

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=1000M --packages com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2


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


spark-nlp on Apache Spark 2.4.x:

<!-- -->


<!-- -->

spark-nlp on Apache Spark 2.3.x:

<!-- -->


<!-- -->


spark-nlp on Apache Spark 2.4.x:

libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "2.7.2"


libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "2.7.2"

spark-nlp on Apache Spark 2.3.x:

libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-spark23" % "2.7.2"


libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu-spark23" % "2.7.2"

Maven Central:


Python without explicit Pyspark installation


If you installed pyspark through pip/conda, you can install spark-nlp through the same channel.


pip install spark-nlp==2.7.2


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")\
    .config("spark.driver.maxResultSize", "0") \
    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2")\
    .config("spark.kryoserializer.buffer.max", "1000M")\

If using local jars, you can use spark.jars instead for a 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()


#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


  • FAT-JAR for CPU on Apache Spark 2.4.x
sbt assembly
  • FAT-JAR for GPU on Apache Spark 2.4.x
sbt -Dis_gpu=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
  • Add 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 previous step, install python module through pip

pip install spark-nlp==2.7.2

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 ( and make sure --packages is there as shown earlier, since it includes both scala and python side installation.

Jupyter Notebook (Python)

The easiest way to get this done is by making Jupyter Notebook run using pyspark as follows:

export SPARK_HOME=/path/to/your/spark/folder
export PYSPARK_PYTHON=python3

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2

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 set up other than having a Google account.

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

import os

# Install java
! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
os.environ["PATH"] = os.environ["JAVA_HOME"] + "/bin:" + os.environ["PATH"]
! java -version

# Install pyspark
! pip install --ignore-installed pyspark==2.4.7

# Install Spark NLP
! pip install --ignore-installed spark-nlp==2.7.2

# Quick SparkSession start
import sparknlp
spark = sparknlp.start()

print("Spark NLP version")
print("Apache Spark version")

Here is a live demo on Google Colab that performs sentiment analysis and NER using pretrained spark-nlp models.

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 1000M
spark.serializer org.apache.spark.serializer.KryoSerializer
  1. Check Enable autoscaling local storage box to have persistent local storage

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

    4.1. Install New -> PyPI -> spark-nlp -> Install

    4.2. Install New -> Maven -> Coordinates -> com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2 -> Install

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

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

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


Spark NLP offers more than 450+ pre-trained pipelines in 192 languages.

English pipelines:

Pipeline Name Build lang
Explain Document ML explain_document_ml 2.4.0 en
Explain Document DL explain_document_dl 2.4.3 en
Recognize Entities DL recognize_entities_dl 2.4.3 en
Recognize Entities DL recognize_entities_bert 2.4.3 en
OntoNotes Entities Small onto_recognize_entities_sm 2.4.0 en
OntoNotes Entities Large onto_recognize_entities_lg 2.4.0 en
Match Datetime match_datetime 2.4.0 en
Match Pattern match_pattern 2.4.0 en
Match Chunk match_chunks 2.4.0 en
Match Phrases match_phrases 2.4.0 en
Clean Stop clean_stop 2.4.0 en
Clean Pattern clean_pattern 2.4.0 en
Clean Slang clean_slang 2.4.0 en
Check Spelling check_spelling 2.4.0 en
Check Spelling DL check_spelling_dl 2.5.0 en
Analyze Sentiment analyze_sentiment 2.4.0 en
Analyze Sentiment DL analyze_sentimentdl_use_imdb 2.5.0 en
Analyze Sentiment DL analyze_sentimentdl_use_twitter 2.5.0 en
Dependency Parse dependency_parse 2.4.0 en

Quick example:

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


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

|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


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

English Models:

Model Name Build Lang
LemmatizerModel (Lemmatizer) lemma_antbnc 2.0.2 en
PerceptronModel (POS) pos_anc 2.0.2 en
PerceptronModel (POS UD) pos_ud_ewt 2.2.2 en
NerCrfModel (NER with GloVe) ner_crf 2.4.0 en
NerDLModel (NER with GloVe) ner_dl 2.4.3 en
NerDLModel (NER with BERT) ner_dl_bert 2.4.3 en
NerDLModel (OntoNotes with GloVe 100d) onto_100 2.4.0 en
NerDLModel (OntoNotes with GloVe 300d) onto_300 2.4.0 en
SymmetricDeleteModel (Spell Checker) spellcheck_sd 2.0.2 en
NorvigSweetingModel (Spell Checker) spellcheck_norvig 2.0.2 en
ViveknSentimentModel (Sentiment) sentiment_vivekn 2.0.2 en
DependencyParser (Dependency) dependency_conllu 2.0.8 en
TypedDependencyParser (Dependency) dependency_typed_conllu 2.0.8 en


Model Name Build Lang
WordEmbeddings (GloVe) glove_100d 2.4.0 en
BertEmbeddings bert_base_uncased 2.4.0 en
BertEmbeddings bert_base_cased 2.4.0 en
BertEmbeddings bert_large_uncased 2.4.0 en
BertEmbeddings bert_large_cased 2.4.0 en
ElmoEmbeddings elmo 2.4.0 en
UniversalSentenceEncoder (USE) tfhub_use 2.4.0 en
UniversalSentenceEncoder (USE) tfhub_use_lg 2.4.0 en
AlbertEmbeddings albert_base_uncased 2.5.0 en
AlbertEmbeddings albert_large_uncased 2.5.0 en
AlbertEmbeddings albert_xlarge_uncased 2.5.0 en
AlbertEmbeddings albert_xxlarge_uncased 2.5.0 en
XlnetEmbeddings xlnet_base_cased 2.5.0 en
XlnetEmbeddings xlnet_large_cased 2.5.0 en


Model Name Build Lang
ClassifierDL (with tfhub_use) classifierdl_use_trec6 2.5.0 en
ClassifierDL (with tfhub_use) classifierdl_use_trec50 2.5.0 en
SentimentDL (with tfhub_use) sentimentdl_use_imdb 2.5.0 en
SentimentDL (with tfhub_use) sentimentdl_use_twitter 2.5.0 en
SentimentDL (with glove_100d) sentimentdl_glove_imdb 2.5.0 en

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")

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


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

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

All examples: spark-nlp-workshop


Check our Articles and FAQ page here


Special community acknowledgments

Thanks in general to the community who have been lately reporting important issues and pull request with bugfixes. The community has been key in the last releases with feedback in various Spark-based environments.

Here a few specific mentions for recurring feedback and slack participation

  • @maziyarpanahi - For contributing with testing and valuable feedback
  • @easimadi - For contributing with documentation and valuable feedback


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