datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
finemed-fr
FineMed-fr
🤗 Blog |
📄 Paper |
💻 Code |
🌐 FineMed |
🩺 DoctoBERT
📚 Introduction
FineMed-fr is a large, openly available corpus of French medical text for language-model pretraining: 21.1M documents and 19.2B words of real-world medical writing, annotated along several quality axes.
The corpus is drawn from three heterogeneous open-web sources (FineWeb-2,
FinePDFs, and
FineWiki), which together provide the scale, source
diversity, and stylistic range… See the full description on the dataset page: https://huggingface.co/datasets/doctolib-lab/finemed-fr.FineMed-SFT
Introduction
This dataset is constructed using Qwen2.5-72B-Instruct and QwQ-32B-Preview, and it serves as the foundation for fine-tuning both FineMedLM and FineMedLM-o1.
This repository contains the SFT data used during the training process, including all medical data, fine-grained medical data, and thinking data. To use the dataset, simply download the jsonl file from the folder.
For details, see our paper and GitHub repository.
Citation
If you find our data useful… See the full description on the dataset page: https://huggingface.co/datasets/hongzhouyu/FineMed-SFT.finemed-rephrased-fr
FineMed-rephrased-fr
🤗 Blog |
📄 Paper |
💻 Code |
🌐 FineMed |
🩺 DoctoBERT
📚 Introduction
FineMed-rephrased-fr is a signal-amplifying rephrasing of FineMed-fr: 13.6M documents and 4.5B words of LLM-rephrased French medical text. An LLM rewrites each source document into a faithful variant that raises medical-term density and broadens the co-occurrence context around each medical concept, using an adapted Massive Genre-Audience (MGA) reformulation.
As… See the full description on the dataset page: https://huggingface.co/datasets/doctolib-lab/finemed-rephrased-fr.FineMed-DPO
Introduction
This dataset is constructed using Qwen2.5-72B-Instruct and QwQ-32B-Preview, and it serves as the foundation for fine-tuning FineMedLM-o1.
This repository contains the DPO data used during the training process. To access the dataset, you can either use the load_dataset function or directly download the parquet files from the designated folder.
For details, see our paper and GitHub repository.
Citation
If you find our data useful, please consider citing our… See the full description on the dataset page: https://huggingface.co/datasets/hongzhouyu/FineMed-DPO.