github.com/JohnSnowLabs/nlu @5.4.1
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README
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](https://medium.com/s |