Instructions to use genloop/FHIR_QnA_Relevance_Classification_Mistral-NeMo-Instruct-FT_T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genloop/FHIR_QnA_Relevance_Classification_Mistral-NeMo-Instruct-FT_T1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="genloop/FHIR_QnA_Relevance_Classification_Mistral-NeMo-Instruct-FT_T1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("genloop/FHIR_QnA_Relevance_Classification_Mistral-NeMo-Instruct-FT_T1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add metadata (#1)
Browse files- Add metadata (a9b53e8e3a0e9f8560362e189809d5a597c8baa0)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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# Model Card for Model ID
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This repository contains the model introduced in the paper [Question Answering on Patient Medical Records with Private Fine-Tuned LLMs](https://huggingface.co/papers/2501.13687).
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license: apache-2.0
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library_name: transformers
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pipeline_tag: question-answering
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---
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# Model Card for Model ID
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This repository contains the model introduced in the paper [Question Answering on Patient Medical Records with Private Fine-Tuned LLMs](https://huggingface.co/papers/2501.13687).
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