spark-nlp 2.7.2

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.

Spark NLP: State of the Art Natural Language Processing

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(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
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!