Modelpublic

AgentPublic/chatrag-deberta

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

Chatrag-Deberta is a small lightweight LLM to predict whether a question should retrieve additional information with RAG or not.

Chatrag-Deberta is based on Deberta-v3-large, a 304M encoder-decoder. Its initial version was fine-tuned on 20,000 examples of questions annotated by Mistral 7B.

Use

A typical example of inference with Chatrag-Deberta is provided in the Google Colab demo or with inference_chatrag.py

For every submitted text, Chatrag-Deberta will output a range of probabilities to require RAG or not.

This makes it possible to adjust a threshold of activation depending on whether more or less RAG is desirable in the system.

Query	Prob	Result
Comment puis-je renouveler un passeport ?	0.988455	RAG
Combien font deux et deux ?	0.041475	No-RAG
Écris un début de lettre de recommandation pour la Dinum	0.103086	No-RAG