Spark NLP: State of the Art Natural Language Processing

The first production grade versions of the latest deep learning NLP research

The most widely used NLP library in the enterprise

Source:2020 NLP Industry Survey, by Gradient Flow.

100% Open Source

Including pre-trained models and pipelines

Natively scalable

The only NLP library built natively on Apache Spark

Multiple Languages

Full Python, Scala, and Java support

Transformers at Scale

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

Quick and Easy

Spark NLP is available on PyPI, Conda, and Maven
    # Install Spark NLP from PyPI
    $ pip install spark-nlp==3.4.4 pyspark==3.1.2

    # Install Spark NLP from Anaconda/Conda
    $ conda install -c johnsnowlabs spark-nlp

    # Load Spark NLP with Spark Shell
    $ spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.4.4

    # Load Spark NLP with PySpark
    $ pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.4.4

    # Load Spark NLP with Spark Submit
    $ spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.4.4

    # Load Spark NLP as an external Fat JAR
    $ spark-shell --jars spark-nlp-assembly-3.4.4.jar

Right Out of The Box

Spark NLP ships with many NLP features, pre-trained models and pipelines

NLP Features

  • Tokenization
  • Word Segmentation
  • Stop Words Removal
  • Normalizer
  • Stemmer
  • Lemmatizer
  • NGrams
  • Regex Matching
  • Text Matching
  • Chunking
  • Date Matcher
  • Part-of-speech tagging
  • Sentence Detector (DL models)
  • Dependency parsing
  • Sentiment Detection (ML models)
  • Spell Checker (ML & DL models)
  • Doc2Vec Embeddings (Word2Vec)
  • Word2Vec Embeddings (Word2Vec)
  • Word Embeddings (GloVe & Word2Vec)
  • BERT Embeddings
  • DistilBERT Embeddings
  • CamemBERT Embeddings
  • RoBERTa Embeddings
  • DeBERTa Embeddings
  • XLM-RoBERTa Embeddings
  • Longformer Embeddings
  • ALBERT Embeddings
  • XLNet Embeddings
  • ELMO Embeddings
  • Universal Sentence Encoder
  • Sentence Embeddings
  • Chunk Embeddings
  • Neural Machine Translation (MarianMT)
  • Text-To-Text Transfer Transformer (Google T5)
  • Generative Pre-trained Transformer 2 (OpenAI GPT-2)
  • Unsupervised keywords extraction
  • Language Detection & Identification (up to 375 languages)
  • Multi-class Text Classification (DL model)
  • Multi-label Text Classification (DL model)
  • Multi-class Sentiment Analysis (DL model)
  • BERT for Token & Sequence Classification
  • DistilBERT 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
  • Named entity recognition (DL model)
  • Easy TensorFlow integration
  • GPU Support
  • Full integration with Spark ML functions
  • 3200+ pre-trained models in 200+ languages!
  • 1700+ pre-trained pipelines in 200+ languages! 
    from sparknlp.base import *
    from sparknlp.annotator import *
    from sparknlp.pretrained import PretrainedPipeline
    import sparknlp

    # Start Spark Session with Spark NLP
    spark = sparknlp.start()

    # Download a pre-trained pipeline
    pipeline = PretrainedPipeline('explain_document_dl', lang='en')

    # Annotate your testing dataset
    result = pipeline.annotate("The Mona Lisa is a 16th century oil painting created by Leonardo. It's held at the Louvre in Paris.")

    # 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']

Benchmark

Spark NLP 3.x obtained the best performing academic peer-reviewed results

Training NER

  • State-of-the-art Deep Learning algorithms
  • Achieve high accuracy within a few minutes
  • Achieve high accuracy with a few lines of codes
  • Blazing fast training
  • Use CPU or GPU
  • 80+ Pretrained Embeddings including GloVe, Word2Vec, BERT, DistilBERT, CamemBERT, RoBERTa, DeBERTa, XLM-RoBERTa, Longformer, ELMO, ELECTRA, ALBERT, XLNet, BioBERT, etc.
  • Multi-lingual NER models in Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, German, Hebrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, and Urdu
SYSTEM YEAR LANGUAGE CONLL ‘03
Spark NLP v3 2021 Python/Scala/Java/R 93 (test F1)
95 (dev F1)
spaCy v3 2021
 Python 91.6
Stanza (StanfordNLP) 2020
 Python 92.1
Flair 2018 Python 93.1
CoreNLP 2015 Java 89.6
SYSTEM YEAR LANGUAGE ONTONOTES
Spark NLP v3 2021 Python/Scala/Java/R 90.0 (test F1)
92.5 (dev F1)
spaCy RoBERTa 2020
 Python 89.7 (dev F1)
Stanza (StanfordNLP) 2020
 Python 88.8 (dev F1)
Flair 2018 Python 89.7

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