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 hundreds of pretrained models in tens of languages - all production-grade, scalable, and trainable.
Take a look at our official Spark NLU page: https://nlu.johnsnowlabs.com/ for user documentation and examples
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 nluimport nlu
nlu.load('sentiment').predict('I love NLU! <3') 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') 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!
- 350 + 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
- 50+ Classifiers (NER, POS, Emotion, Sarcasm, Questions, Spam)
- 40+ Supported Languages
- Labeled and Unlabeled Dependency parsing
- Various Text Cleaning and Pre-Processing methods like Stemming, Lemmatizing, Normalizing, Filtering, Cleaning pipelines and more
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.
Working with text data can sometimes be quite a dirty Job. NLU helps you keep your hands clean by providing lots of components that take away data engineering intensive tasks.
- Datetime Matcher
- Pattern Matcher
- Chunk Matcher
- Phrases Matcher
- Stopword Cleaners
- Pattern Cleaners
- Slang Cleaner
Checkout the NLU Namespace for everything that NLU has to offer!
- Pandas DataFrame and Series
- Spark DataFrames
- Modin with Ray backend
- Modin with Dask backend
- Numpy arrays
- Strings and lists of strings
Checkout the following notebooks for examples on how to work with NLU.
- Kaggle Twitter Airline Sentiment Analysis NLU demo
- Kaggle Twitter Airline Emotion Analysis NLU demo
- Kaggle Twitter COVID Sentiment Analysis NLU demo
- Kaggle Twitter COVID Emotion Analysis nlu demo
The following are Collab examples which showcase each NLU component and some applications.
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- BERT Word Embeddings and T-SNE plotting
- ALBERT Word Embeddings and T-SNE plotting
- ELMO Word Embeddings and T-SNE plotting
- XLNET Word Embeddings and T-SNE plotting
- ELECTRA Word Embeddings and T-SNE plotting
- COVIDBERT Word Embeddings and T-SNE plotting
- BIOBERT Word Embeddings and T-SNE plotting
- GLOVE Word Embeddings and T-SNE plotting