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

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	[![Open In