RoBERTadeploy

Forked from BERTdeploy

This is a small app I built using HuggingFace Transformers and FastAPI to perform text classification using the pre-trained DistilBERT model. I mostly relied on the excellent tutorial by Venelin to build this (ref 1). I made a few key changes to his approach:

• Used pre-trained model instead of fine-tuning
• Used requirement.txt for pip instead of using pipenv
• Did not use a lot of extra code style packages

How to use?

• install torch for your hardware
• pip install requirements.txt
• uvicorn DistilRoBERTa.api:app or bash bin/run_server

Then make your API call:

http POST http://127.0.0.1:8000/classify text="Pre-trained j-hartmann/emotion-english-distilroberta-base seems to work quite well!"

You'll get an output like:

{
    "probabilities": {
        "anger": 0.007748342119157314,
        "disgust": 0.0022821975871920586,
        "fear": 0.0021107119973748922,
        "joy": 0.27118009328842163,
        "neutral": 0.6292678713798523,
        "sadness": 0.005368099547922611,
        "surprise": 0.08204267174005508
    }
}

TO-DO

• docker
• ??

References

• BERTdeploy
• Deploy BERT for Sentiment Analysis as REST API using PyTorch, Transformers by Hugging Face and FastAPI
• Auto-deploy FastAPI App to Heroku via Git in these 5 Easy Steps