nlu-tmp

NLU: The Power of Spark NLP, the Simplicity of Python

John Snow Labs' NLU is a Python library for applying state-of-the-art text mining, directly on any dataframe, with a single line of code. As a facade of the award-winning Spark NLP library, it comes with 1000+ of pretrained models in 100+, all production-grade, scalable, and trainable, with everything in 1 line of code.

NLU in Action

See how easy it is to use any of the thousands of models in 1 line of code, there are hundreds of tutorials and simple examples you can copy and paste into your projects to achieve State Of The Art easily.

NLU & Streamlit in Action

This 1 line let's you visualize and play with 1000+ SOTA NLU & NLP models in 200 languages

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/01_dashboard.py

NLU provides tight and simple integration into Streamlit, which enables building powerful webapps in just 1 line of code which showcase the. View the NLU&Streamlit documentation or NLU & Streamlit examples section. The entire GIF demo and

All NLU resources overview

Take a look at our official NLU page: https://nlu.johnsnowlabs.com/ for user documentation and examples

Ressource	Description
Install NLU	Just run pip install nlu pyspark==3.0.2
The NLU Namespace	Find all the names of models you can load with nlu.load()
The nlu.load(<Model>) function	Load any of the 1000+ models in 1 line
The nlu.load(<Model>).predict(data) function	Predict on Strings, List of Strings, Numpy Arrays, Pandas, Modin and Spark Dataframes
The nlu.load(<train.Model>).fit(data) function	Train a text classifier for 2-Class, N-Classes Multi-N-Classes, Named-Entitiy-Recognition or Parts of Speech Tagging
The nlu.load(<Model>).viz(data) function	Visualize the results of Word Embedding Similarity Matrix, Named Entity Recognizers, Dependency Trees & Parts of Speech, Entity Resolution,Entity Linking or Entity Status Assertion
The nlu.load(<Model>).viz_streamlit(data) function	Display an interactive GUI which lets you explore and test every model and feature in NLU in 1 click.
General Concepts	General concepts in NLU
The latest release notes	Newest features added to NLU
Overview NLU 1-liners examples	Most common used models and their results
Overview NLU 1-liners examples for healthcare models	Most common used healthcare models and their results
Overview of all NLU tutorials and Examples	100+ tutorials on how to use NLU on text datasets for various problems and from various sources like Twitter, Chinese News, Crypto News Headlines, Airline Traffic communication, Product review classifier training,
Connect with us on Slack	Problems, questions or suggestions? We have a very active and helpful community of over 2000+ AI enthusiasts putting NLU, Spark NLP & Spark OCR to good use
Discussion Forum	More indepth discussion with the community? Post a thread in our discussion Forum
John Snow Labs Medium	Articles and Tutorials on the NLU, Spark NLP and Spark OCR
John Snow Labs Youtube	Videos and Tutorials on the NLU, Spark NLP and Spark OCR
NLU Website	The official NLU website
Github Issues	Report a bug

Getting Started with NLU

To get your hands on the power of NLU, you just need to install it via pip and ensure Java 8 is installed and properly configured. Checkout Quickstart for more infos

pip install nlu pyspark==3.0.2

Loading and predicting with any model in 1 line python

import nlu 
nlu.load('sentiment').predict('I love NLU! <3') 

Loading and predicting with multiple models in 1 line

Get 6 different embeddings in 1 line and use them for downstream data science tasks!

nlu.load('bert elmo albert xlnet glove use').predict('I love NLU! <3') 

What kind of models does NLU provide?

NLU provides everything a data scientist might want to wish for in one line of code!

• NLU provides everything a data scientist might want to wish for in one line of code!
• 1000 + pre-trained models
• 100+ of the latest NLP word embeddings ( BERT, ELMO, ALBERT, XLNET, GLOVE, BIOBERT, ELECTRA, COVIDBERT) and different variations of them
• 50+ of the latest NLP sentence embeddings ( BERT, ELECTRA, USE) and different variations of them
• 100+ Classifiers (NER, POS, Emotion, Sarcasm, Questions, Spam)
• 300+ Supported Languages
• Summarize Text and Answer Questions with T5
• Labeled and Unlabeled Dependency parsing
• Various Text Cleaning and Pre-Processing methods like Stemming, Lemmatizing, Normalizing, Filtering, Cleaning pipelines and more

Classifiers trained on many different datasets

Choose the right tool for the right task! Whether you analyze movies or twitter, NLU has the right model for you!

• trec6 classifier
• trec10 classifier
• spam classifier
• fake news classifier
• emotion classifier
• cyberbullying classifier
• sarcasm classifier
• sentiment classifier for movies
• IMDB Movie Sentiment classifier
• Twitter sentiment classifier
• NER pretrained on ONTO notes
• NER trainer on CONLL
• Language classifier for 20 languages on the wiki 20 lang dataset.

Utilities for the Data Science NLU applications

Working with text data can sometimes be quite a dirty job. NLU helps you keep your hands clean by providing components that take away from data engineering intensive tasks.

• Datetime Matcher
• Pattern Matcher
• Chunk Matcher
• Phrases Matcher
• Stopword Cleaners
• Pattern Cleaners
• Slang Cleaner

Where can I see all models available in NLU?

For NLU models to load, see the NLU Namespace or the John Snow Labs Modelshub or go straight to the source.

Supported Data Types

• Pandas DataFrame and Series
• Spark DataFrames
• Modin with Ray backend
• Modin with Dask backend
• Numpy arrays
• Strings and lists of strings

Overview of all tutorials using the NLU-Library

In the following tabular, all available tutorials using NLU are listed. These tutorials will help you learn the usage of the NLU library and on how to use it for your own tasks. Some of the tasks NLU does are translating from any language to the english language, lemmatizing, tokenizing, cleaning text from Symbol or unwanted syntax, spellchecking, detecting entities, analyzing sentiments and many more!

{:.table2}

Tutorial Description	NLU Spells Used	Open In Colab	Dataset and Paper References
Albert Word Embeddings with NLU	albert, sentiment pos albert emotion		Albert-Paper, Albert on Github, Albert on TensorFlow, T-SNE, T-SNE-Albert, Albert_Embedding
Bert Word Embeddings with NLU	bert, pos sentiment emotion bert		Bert-Paper, Bert Github, T-SNE, T-SNE-Bert, Bert_Embedding
BIOBERT Word Embeddings with NLU	biobert , sentiment pos biobert emotion		BioBert-Paper, Bert Github , BERT: Deep Bidirectional Transformers, Bert Github, T-SNE, T-SNE-Biobert, Biobert_Embedding
COVIDBERT Word Embeddings with NLU	covidbert, sentiment covidbert pos		CovidBert-Paper, Bert Github, T-SNE, T-SNE-CovidBert, Covidbert_Embedding
ELECTRA Word Embeddings with NLU	electra, sentiment pos en.embed.electra emotion		Electra-Paper, T-SNE, T-SNE-Electra, Electra_Embedding
ELMO Word Embeddings with NLU	elmo, sentiment pos elmo emotion		ELMO-Paper, Elmo-TensorFlow, T-SNE, T-SNE-Elmo, Elmo-Embedding
GLOVE Word Embeddings with NLU	glove, sentiment pos glove emotion		Glove-Paper, T-SNE, T-SNE-Glove , Glove_Embedding
XLNET Word Embeddings with NLU	xlnet, sentiment pos xlnet emotion		XLNet-Paper, Bert Github, T-SNE, T-SNE-XLNet, Xlnet_Embedding
Multiple Word-Embeddings and Part of Speech in 1 Line of code	bert electra elmo glove xlnet albert pos		Bert-Paper, Albert-Paper, ELMO-Paper, Electra-Paper, XLNet-Paper, Glove-Paper
Normalzing with NLU	norm		-
Detect sentences with NLU	sentence_detector.deep, sentence_detector.pragmatic, xx.sentence_detector		Sentence Detector
Spellchecking with NLU	n.a.	n.a.	-
Stemming with NLU	en.stem, de.stem		-
Stopwords removal with NLU	stopwords		Stopwords
Tokenization with NLU	tokenize		-
Normalization of Documents	norm_document		-
Open and Closed book question answering with Google's T5	en.t5 , answer_question		T5-Paper, T5-Model
Overview of every task available with T5	en.t5.base		T5-Paper, T5-Model
Translate between more than 200 Languages in 1 line of code with Marian Models	tr.translate_to.fr, en.translate_to.fr ,fr.translate_to.he , en.translate_to.de		Marian-Papers, Translation-Pipeline (En to Fr), Translation-Pipeline (En to Ger)
BERT Sentence Embeddings with NLU	embed_sentence.bert, pos sentiment embed_sentence.bert		Bert-Paper, Bert Github, Bert-Sentence_Embedding
ELECTRA Sentence Embeddings with NLU	embed_sentence.electra, pos sentiment embed_sentence.electra		Electra Paper, Sentence-Electra-Embedding
USE Sentence Embeddings with NLU	use, pos sentiment use emotion		Universal Sentence Encoder, USE-TensorFlow, Sentence-USE-Embedding
Sentence similarity with NLU using BERT embeddings	embed_sentence.bert, use en.embed_sentence.electra embed_sentence.bert		Bert-Paper, Bert Github, Bert-Sentence_Embedding
Part of Speech tagging with NLU	pos		Part of Speech
NER Aspect Airline ATIS	en.ner.aspect.airline		NER Airline Model, Atis intent Dataset
NLU-NER_CONLL_2003_5class_example	ner		NER-Piple
Named-entity recognition with Deep Learning ONTO NOTES	ner.onto		NER_Onto
Aspect based NER-Sentiment-Restaurants	en.ner.aspect_sentiment		-
Detect Named Entities (NER), Part of Speech Tags (POS) and Tokenize in Chinese	zh.segment_words, zh.pos, zh.ner, zh.translate_to.en		Translation-Pipeline (Zh to En)
Detect Named Entities (NER), Part of Speech Tags (POS) and Tokenize in Japanese	ja.segment_words, ja.pos, ja.ner, ja.translate_to.en		Translation-Pipeline (Ja to En)
Detect Named Entities (NER), Part of Speech Tags (POS) and Tokenize in Korean	ko.segment_words, ko.pos, ko.ner.kmou.glove_840B_300d, ko.translate_to.en		-
Date Matching	match.datetime		-
Typed Dependency Parsing with NLU	dep		Dependency Parsing
Untyped Dependency Parsing with NLU	dep.untyped		-
E2E Classification with NLU	e2e		e2e-Model
Language Classification with NLU	lang		-
Cyberbullying Classification with NLU	classify.cyberbullying		Cyberbullying-Classifier
Sentiment Classification with NLU for Twitter	emotion		Emotion detection
Fake News Classification with NLU	en.classify.fakenews		Fakenews-Classifier
Intent Classification with NLU	en.classify.intent.airline		Airline-Intention classifier, Atis-Dataset
Question classification based on the TREC dataset	en.classify.questions		Question-Classifier
Sarcasm Classification with NLU	en.classify.sarcasm		Sarcasm-Classifier
Sentiment Classification with NLU for Twitter	en.sentiment.twitter		Sentiment_Twitter-Classifier
Sentiment Classification with NLU for Movies	en.sentiment.imdb		Sentiment_imdb-Classifier
Spam Classification with NLU	en.classify.spam		Spam-Classifier
Toxic text classification with NLU	en.classify.toxic		Toxic-Classifier
Unsupervised keyword extraction with NLU using the YAKE algorithm	yake		-
Grammatical Chunk Matching with NLU	match.chunks		-
Getting n-Grams with NLU	ngram		-
Assertion	en.med_ner.clinical en.assert, en.med_ner.clinical.biobert en.assert.biobert, ...		Healthcare-NER, NER_Clinical-Classifier, Toxic-Classifier
De-Identification Model overview	med_ner.jsl.wip.clinical en.de_identify, med_ner.jsl.wip.clinical en.de_identify.clinical, ...		NER-Clinical
Drug Normalization	norm_drugs		-
Entity Resolution	med_ner.jsl.wip.clinical en.resolve_chunk.cpt_clinical, med_ner.jsl.wip.clinical en.resolve.icd10cm, ...		NER-Clinical, Entity-Resolver clinical
Medical Named Entity Recognition	en.med_ner.ade.clinical, en.med_ner.ade.clinical_bert, en.med_ner.anatomy,en.med_ner.anatomy.biobert, ...		-
Relation Extraction	en.med_ner.jsl.wip.clinical.greedy en.relation, en.med_ner.jsl.wip.clinical.greedy en.relation.bodypart.problem, ...		-
Visualization of NLP-Models with Spark-NLP and NLU	ner, dep.typed, med_ner.jsl.wip.clinical resolve_chunk.rxnorm.in, med_ner.jsl.wip.clinical resolve.icd10cm		NER-Piple, Dependency Parsing, NER-Clinical, Entity-Resolver (Chunks) clinical
NLU Covid-19 Emotion Showcase	emotion		Emotion detection
NLU Covid-19 Sentiment Showcase	sentiment		Sentiment classification
NLU Airline Emotion Demo	emotion		Emotion detection
NLU Airline Sentiment Demo	sentiment		Sentiment classification
Bengali NER Hindi Embeddings for 30 Models	bn.ner, bn.lemma, ja.lemma, am.lemma, bh.lemma, en.ner.onto.bert.small_l2_128,..		Bengali-NER, Bengali-Lemmatizer, Japanese-Lemmatizer, Amharic-Lemmatizer
Entity Resolution	med_ner.jsl.wip.clinical en.resolve.umls, med_ner.jsl.wip.clinical en.resolve.loinc, med_ner.jsl.wip.clinical en.resolve.loinc.biobert		-
NLU 20 Minutes Crashcourse - the fast Data Science route	spell, sentiment, pos, ner, yake, en.t5, emotion, answer_question, en.t5.base ...		T5-Model, Part of Speech, NER-Piple, Emotion detection , Spellchecker, Sentiment classification
Chapter 0: Intro: 1-liners	sentiment, pos, ner, bert, elmo, embed_sentence.bert		Part of Speech, NER-Piple, Sentiment classification, Elmo-Embedding, Bert-Sentence_Embedding
Chapter 1: NLU base-features with some classifiers on testdata	emotion, yake, stem		Emotion detection
Chapter 2: Translation between 300+ languages with Marian	tr.translate_to.en, en.translate_to.fr, en.translate_to.he		Translation-Pipeline (En to Fr), Translation (En to He)
Chapter 3: Answer questions and summarize Texts with T5	answer_question, en.t5, en.t5.base		T5-Model
Chapter 4: Overview of T5-Tasks	en.t5.base		T5-Model
Graph NLU 20 Minutes Crashcourse - State of the Art Text Mining for Graphs	spell, sentiment, pos, ner, yake, emotion, med_ner.jsl.wip.clinical, ...		Part of Speech, NER-Piple, Emotion detection, Spellchecker, Sentiment classification
Healthcare with NLU	med_ner.human_phenotype.gene_biobert, med_ner.ade_biobert, med_ner.anatomy, med_ner.bacterial_species,...		-
Part 0: Intro: 1-liners	spell, sentiment, pos, ner, bert, elmo, embed_sentence.bert		Bert-Paper, Bert Github, T-SNE, T-SNE-Bert , Part of Speech, NER-Piple, Spellchecker, Sentiment classification, Elmo-Embedding , Bert-Sentence_Embedding
Part 1: NLU base-features with some classifiers on Testdata	yake, stem, ner, emotion		NER-Piple, Emotion detection
Part 2: Translate between 200+ Languages in 1 line of code with Marian-Models	en.translate_to.de, en.translate_to.fr, en.translate_to.he		Translation-Pipeline (En to Fr), Translation-Pipeline (En to Ger), Translation (En to He)
Part 3: More Multilingual NLP-translations for Asian Languages with Marian	en.translate_to.hi, en.translate_to.ru, en.translate_to.zh		Translation (En to Hi), Translation (En to Ru), Translation (En to Zh)
Part 4: Unsupervise Chinese Keyword Extraction, NER and Translation from chinese news	zh.translate_to.en, zh.segment_words, yake, zh.lemma, zh.ner		Translation-Pipeline (Zh to En), Zh-Lemmatizer
Part 5: Multilingual sentiment classifier training for 100+ languages	train.sentiment, xx.embed_sentence.labse train.sentiment	n.a.	Sentence_Embedding.Labse
Part 6: Question-answering and Text-summarization with T5-Modell	answer_question, en.t5, en.t5.base		T5-Paper
Part 7: Overview of all tasks available with T5	en.t5.base		T5-Paper
Part 8: Overview of some of the Multilingual modes with State Of the Art accuracy (1-liner)	bn.lemma, ja.lemma, am.lemma, bh.lemma, zh.segment_words, ...		Bengali-Lemmatizer, Japanese-Lemmatizer , Amharic-Lemmatizer
Overview of some Multilingual modes avaiable with State Of the Art accuracy (1-liner)	bn.ner.cc_300d, ja.ner, zh.ner, th.ner.lst20.glove_840B_300D, ar.ner		Bengali-NER
NLU 20 Minutes Crashcourse - the fast Data Science route	-		-

Need help?

• Ping us on Slack
• Post an issue on Github

Simple NLU Demos

• NLU different output levels Demo

Features in NLU Overview

• 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
• 1000 pre-trained models in +200 languages!
• Multi-lingual NER models: Arabic, Chinese, Danish, Dutch, English, Finnish, French, German, Hewbrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Urdu and more
• Natural Language inference
• Coreference resolution
• Sentence Completion
• Word sense disambiguation
• Clinical entity recognition
• Clinical Entity Linking
• Entity normalization
• Assertion Status Detection
• De-identification
• Relation Extraction
• Clinical Entity Resolution

Citation

We have published a paper that you can cite for the NLU 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/S2665963821000063},
    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.}
    }
}