yefengjie / happy-transformer

A package built on top of Hugging Face's transformers library that makes it easy to utilize state-of-the-art NLP models

Happy Transformer

Documentation and news: happytransformer.com

New Course: Create a text generation web app. Also learn how to fine-tune GPT-Neo link

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Happy Transformer is an package built on top of Hugging Face's transformer library that makes it easy to utilize state-of-the-art NLP models.

Features

Public Methods	Basic Usage	Training
Text Generation	✔	✔
Text Classification	✔	✔
Word Prediction	✔	✔
Question Answering	✔	✔
Text-to-Text	✔	✔
Next Sentence Prediction	✔
Token Classification	✔

Quick Start

pip install happytransformer

from happytransformer import HappyWordPrediction
#--------------------------------------#
happy_wp = HappyWordPrediction()  # default uses distilbert-base-uncased
result = happy_wp.predict_mask("I think therefore I [MASK]")
print(result)  # [WordPredictionResult(token='am', score=0.10172799974679947)]
print(result[0].token)  # am

Maintainers

• Eric Fillion Lead Maintainer
• Ted Brownlow Maintainer

Tutorials

Text generation with training (GPT-Neo)

Text classification (training)

Text classification (hate speech detection)

Text classification (sentiment analysis)

Word prediction with training (DistilBERT, RoBERTa)

Top T5 Models

Grammar Correction

Fine-tune a Grammar Correction Model