Token Classification
PyTorch
GLiFormer
GLiNER
English
openmed
medical
biomedical
clinical-nlp
named-entity-recognition
disease
Eval Results (legacy)
Instructions to use OpenMed/GLiFormer-BioNER-Disease-Large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiFormer
How to use OpenMed/GLiFormer-BioNER-Disease-Large-v1 with GLiFormer:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- GLiNER
How to use OpenMed/GLiFormer-BioNER-Disease-Large-v1 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/GLiFormer-BioNER-Disease-Large-v1") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OpenMed/GLiFormer-BioNER-Disease-Large-v1: direct link, hf CLI and curl.
- Browser
- Download file 2.3 GB
-
https://huggingface.co/OpenMed/GLiFormer-BioNER-Disease-Large-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/GLiFormer-BioNER-Disease-Large-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/GLiFormer-BioNER-Disease-Large-v1/resolve/main/pytorch_model.bin
2.3 GB
- Xet hash:
- f3b50aa9a3dfa8adc3ce3a66a2fd683aa427707d6ccc660ee81c1161e76bf83c
- Size of remote file:
- 2.3 GB
- SHA256:
- 25994e6887134b5923861763cec033d80675240b97088944991698acd28819a1
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