You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

BioCLIP

BioCLIP is a biomedical vision--language model with a ViT-L/14 image encoder and a BioClinical ModernBERT text encoder. It supports up to 512 text tokens.

Repository layout

BioClinicalModernBERT-large-ViT-L-14-pretrained.json
load_model.py
checkpoints/   five released training variants
evaluation/
|-- data/       BioMedFlickr and the unified 46-task validation data
|-- code/       two inference entry points and metric aggregation
`-- results/    released checkpoint results

Within the unified classification set, all benchmark files are under evaluation/data/zero_shot_classification/datasets/: shared payloads live in payloads/, while domain/dataset folders contain task definitions and any dataset-specific files.

The model JSON is required by OpenCLIP to construct the architecture. The model used by load_model.py and both evaluation runners is the recommended checkpoint:

checkpoints/openclip_init_10epoch_best.pt

Evaluation

Both commands download the checkpoint and the required validation data from this repository. A local data directory can also be supplied to the 46-task runner with --data-root.

pip install -r evaluation/requirements.txt

python evaluation/code/evaluate_biomedflickr.py \
  --output evaluation/results/biomedflickr_reproduced.json

python evaluation/code/evaluate_zero_shot_classification.py \
  --output evaluation/results/unified46_reproduced.json

Results

Complete checkpoint results and public-model comparisons are reported in evaluation/RESULTS.md. Machine-readable outputs are stored in evaluation/results/.

Intended use

BioCLIP is intended for research and is not a clinical device.

Downloads last month: -