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Happy Transformer makes it easy to fine-tune NLP Transformer models and use them for inference.

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Happy Transformer

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Happy Transformer makes it easy to fine-tune NLP Transformer models and use them for inference.


  1. Deepspeed for training
  2. Apple's MPS for training and inference
  3. WandB to track training runs
  4. Data supplied for training is automatically split into portions for training and evaluating
  5. Push models directly to Hugging Face's Model Hub

Read about the full 3.0.0 update including breaking changes here.


Tasks Inference Training
Text Generation
Text Classification
Word Prediction
Question Answering
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



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

Project details

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