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John Snow Labs 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.

Project description

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

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

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 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(spark23=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
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!

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2.7.5

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