Instructions to use genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete: direct link, hf CLI and curl.
- Browser
- Download file 267 Bytes
-
https://huggingface.co/genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete/resolve/e0b6402d2caa1a982f5741e10535c2e041f7e65d/README.md
- Command line
-
hf download hf://genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete@e0b6402d2caa1a982f5741e10535c2e041f7e65d/README.md
-
curl -L -o README.md https://huggingface.co/genloop/FHIR_QnA_Query-Based_Resource_Relevance_Classification_T1_complete/resolve/e0b6402d2caa1a982f5741e10535c2e041f7e65d/README.md
267 Bytes
metadata
license: apache-2.0
library_name: transformers
pipeline_tag: question-answering
This repository contains the model introduced in the paper Question Answering on Patient Medical Records with Private Fine-Tuned LLMs.