datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gliner-biomed-pre-training
GLiNER-BioMed pre-training dataset
This dataset, used for the pre-training stage of the GLiNER-BioMed models, was introduced in the paper GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition.
Citation
If you use the GLiNER-BioMed models or datasets in your work, please cite:
@article{yazdani2026gliner,
author = {Yazdani, Anthony and Stepanov, Ihor and Teodoro, Douglas},
title = {{GLiNER-BioMed}: a suite of efficient… See the full description on the dataset page: https://huggingface.co/datasets/anthonyyazdaniml/gliner-biomed-pre-training.chinese-biomedicine-and-genomics-open-intelligence
🔬 Chinese Biomedicine, Cell Therapy & Genomics Open Intelligence Dataset
Curated open intelligence dataset providing English briefs, clinical trial benchmarks, verified abstracts, and DOIs of frontier Chinese research in Cellular Therapeutics, Gene Editing, ADCs, and NMPA Clinical Approvals.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core quantitative takeaways, author… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-biomedicine-and-genomics-open-intelligence.gliner-biomed-post-training
GLiNER-BioMed post-training dataset
This dataset, used for the post-training stage of the GLiNER-BioMed models, was introduced in the paper GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition.
Citation
If you use the GLiNER-BioMed models or datasets in your work, please cite:
@article{yazdani2026gliner,
author = {Yazdani, Anthony and Stepanov, Ihor and Teodoro, Douglas},
title = {{GLiNER-BioMed}: a suite of efficient… See the full description on the dataset page: https://huggingface.co/datasets/anthonyyazdaniml/gliner-biomed-post-training.biomed_NER
Biomed NER
This dataset consists of 4,840 manually annotated text records drawn from PubMed abstracts, drug descriptions from the FDA, and patent abstracts. All entities are continuous, and there are no nested entities.
Dataset composition
The dataset contains 4,840 annotated text records distributed across three sources:
Source
Approx. records
Purpose
PubMed abstracts
~4,300
Core biomedical content
FDA drug descriptions
~430
Pharmaceutical text with dense… See the full description on the dataset page: https://huggingface.co/datasets/knowledgator/biomed_NER.biomed-visual-instructions
Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)
This repos contains the biomedicine visual instructions for post-training MLLMs in our paper: On Domain-Specific Post-Training for Multimodal Large Language Models.
The main project page is: Adapt-MLLM-to-Domains
Data Information
Using our visual instruction synthesizer, we generate visual instruction tasks based on the image-caption pairs from PubMedVision (referred to as PMC_refined… See the full description on the dataset page: https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions.biomedical-qa-dpo-geminiALIA-es-biomedical-synthetic-instructions
Dataset Introduction
The ALIA Spanish Biomedical Synthetic Instructions Corpus is a synthetic instruction-tuning resource in Spanish created under the ALIA project using the Magpie methodology. It was designed to train and evaluate language models in biomedical and healthcare tasks with controlled formats and large-scale supervision.
It contains:
639,456 instances
961,073,205 tokens
14 task modalities (clinical diagnosis, patient education, ethical reasoning, document… See the full description on the dataset page: https://huggingface.co/datasets/SINAI/ALIA-es-biomedical-synthetic-instructions.BioMed-VITAL-instructions
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Authors: Hejie Cui*, Lingjun Mao*, Xin Liang, Jieyu Zhang, Hui Ren, Quanzheng Li, Xiang Li, Carl Yang
BioMed-VITAL Instructions Files
Data file name
File Size
Sample Size
BioMed-VITAL-instructions-60K.json
127 MB
60K
BioMed-VITAL-instructions-80K.json
156 MB
80K
BioMed-VITAL-instructions-150K.json
309 MB
60K + 10K + 80K
BioMed-VITAL-instructions-210K.json
463 MB
80K + 10K + 60K +… See the full description on the dataset page: https://huggingface.co/datasets/mao1207/BioMed-VITAL-instructions.ALIA-es-biomedical-pairs
Dataset Introduction
The ALIA Spanish Biomedical Retrieval Pairs Corpus contains tabular instances designed to train and evaluate retrieval-oriented models (e.g., dense retrievers / embedding encoders) using passage-grounded query data produced with a Qwen3-style prompting workflow integrated in the ALIA encoders pipeline.
It preserves provenance to the original document and chunk while exposing controls such as question type and difficulty (ranging from high_school to phd level).… See the full description on the dataset page: https://huggingface.co/datasets/SINAI/ALIA-es-biomedical-pairs.gliner2-fr-biomed-v3e-trainset
gliner2-fr-biomed-v3e-trainset
Données d'entraînement synthétiques (distillation Qwen3-235B) du modèle rntc/gliner2-fr-biomed-v3e — GLiNER2 FR biomédical (primitives génériques + role-binding). Aucun gold de benchmark (préserve la généralisation zero-shot).
Format JSONL, une ligne = un exemple :
{"input": "<texte>", "output": {"entities": {"<label>": ["<mention>", ...]},
"classifications": {...}, "json_structures": [{"<section>": {"<champ>": "<valeur>"}}], "relations": [...]}}… See the full description on the dataset page: https://huggingface.co/datasets/rntc/gliner2-fr-biomed-v3e-trainset.biomed-fr-v4-enriched-fullben-entities
BEN Entities
Full BEN entity extraction results exported from MongoDB as Hub-native
jsonl.gz shards.
Each row contains only document_id and entities. Scores are filtered with
threshold 0.6 and rounded to two decimals.
Configs
pubmed from Mongo collection pubmed_ncbi
pmc from Mongo collection pmc_xml
uspto from Mongo collection patent_uspto
clinical_trial from Mongo collection clinical_trial_gov
Usage
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/BioMedBigDataCenter/ben-entities.biomed_retrieval_datasetbiomed-fr-v4-enrichedALIA-es-biomedical-triplets
Dataset Introduction
The dataset ALIA Spanish Biomedical Hard Negatives Corpus contains hard negatives for dense retrieval training
generated from <query, passage> pairs contained in SINAI/ALIA-es-biomedical-pairs.The dataset was created as part of the ALIA project to improve the
training of embedding models and dense retrievers specialized in Spanish
biomedical language.
Hard negatives are passages that are semantically similar to a query
but not correct answers, making them… See the full description on the dataset page: https://huggingface.co/datasets/SINAI/ALIA-es-biomedical-triplets.biomed_squad_es_v2
Dataset Card for biomed_squad_es_v2
This Dataset was created as part of the "Extractive QA Biomedicine" project developed during the 2022 Hackathon organized by SOMOS NLP.
Dataset Summary
This is a subset of the dev squad_es (v2) dataset (automatic translation of the Stanford Question Answering Dataset v2 into Spanish) containing questions related to the biomedical domain.
License, distribution and usage conditions of the original Squad_es Dataset apply.
Languages… See the full description on the dataset page: https://huggingface.co/datasets/somosnlp-hackathon-2022/biomed_squad_es_v2.gliner2-fr-biomed-v3b-trainset
gliner2-fr-biomed-v3b-trainset
Données d'entraînement synthétiques (distillation Qwen3-235B) du modèle rntc/gliner2-fr-biomed-v3b — GLiNER2 FR biomédical (généraliste (UMLS-steered, multi-task)). Aucun gold de benchmark (préserve la généralisation zero-shot).
Format JSONL, une ligne = un exemple :
{"input": "<texte>", "output": {"entities": {"<label>": ["<mention>", ...]},
"classifications": {...}, "json_structures": [{"<section>": {"<champ>": "<valeur>"}}], "relations":… See the full description on the dataset page: https://huggingface.co/datasets/rntc/gliner2-fr-biomed-v3b-trainset.biomedicine-deep-research
Biomedicine Deep Research
Complete materialized data for the 13-benchmark biomedicine deep research track. The files preserve the host materializer's directory layout.
public/development/: labeled fit and tune examples.
public/verifier/: unlabeled evaluation inputs.
public/reference/: audited biomedical source allowlist, RiskCalcs, and its notice.
private/: evaluation labels, mounted only into the separate grader during benchmark runs.
The complete tree is downloadable from… See the full description on the dataset page: https://huggingface.co/datasets/zifeng-ai/biomedicine-deep-research.autobench-biomedical-verification
AutoBench Biomedical Verification
Frozen evaluation rows for AutoBench.
gliner2-fr-biomed-v3f-trainset
gliner2-fr-biomed-v3f-trainset
Données d'entraînement synthétiques (distillation Qwen3-235B) du modèle rntc/gliner2-fr-biomed-v3f — GLiNER2 FR biomédical (densité stratifiée par hash (100k chunks annotés)). Aucun gold de benchmark (préserve la généralisation zero-shot).
Format JSONL, une ligne = un exemple :
{"input": "<texte>", "output": {"entities": {"<label>": ["<mention>", ...]},
"classifications": {...}, "json_structures": [{"<section>": {"<champ>": "<valeur>"}}]… See the full description on the dataset page: https://huggingface.co/datasets/rntc/gliner2-fr-biomed-v3f-trainset.gliner2-fr-biomed-v3d
GLiNER2 FR Biomédical — données d'entraînement multi-tâches (v3d)
Données synthétiques d'entraînement pour un GLiNER2 français biomédical généraliste open-vocabulary, dans la lignée GLiNER / NuNER / GLiNER-biomed (distillation depuis un LLM, pas d'entraînement sur du gold de benchmark).
Langue : français
Domaine : biomédical / clinique
Tâches : NER open-vocab · classifications · extraction structurée (json_structures) · relations
Annotateur : Qwen3-235B-A22B-Instruct-2507 (FP8)… See the full description on the dataset page: https://huggingface.co/datasets/rntc/gliner2-fr-biomed-v3d.gliner2-fr-biomed-v3c-trainset
gliner2-fr-biomed-v3c-trainset
Données d'entraînement synthétiques (distillation Qwen3-235B) du modèle rntc/gliner2-fr-biomed-v3c — GLiNER2 FR biomédical (densité bimodale + relations). Aucun gold de benchmark (préserve la généralisation zero-shot).
Format JSONL, une ligne = un exemple :
{"input": "<texte>", "output": {"entities": {"<label>": ["<mention>", ...]},
"classifications": {...}, "json_structures": [{"<section>": {"<champ>": "<valeur>"}}], "relations": [...]}}… See the full description on the dataset page: https://huggingface.co/datasets/rntc/gliner2-fr-biomed-v3c-trainset.biomedical-evidence-support-gpt-oss-sftbiomed-fr-pipeline-testbiomedical-multi-source-finetunebiomed_summ_fullbiomed_NER
BioMed_general_NER
This dataset consists of manually annotated biomedical abstracts from PubMed, drug descriptions from FDA and abstracts from patents.
It was extracted 24 different entity types, including those specific to medicine and biology and general such as location and organization as well.
This is one of the biggest datasets of such kind, which consists of 4840 annotated abstracts.
Classes
Here's a description for each of the labels:
CHEMICALS - Represents… See the full description on the dataset page: https://huggingface.co/datasets/Sriramgopal12/biomed_NER.bio-med-arxivbiomed-nerATLAS_nnUNetv2