Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chemical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Pharma-XLarge-770M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Pharma-XLarge-770M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pharma-XLarge-770M") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OpenMed/OpenMed-ZeroShot-NER-Pharma-XLarge-770M: direct link, hf CLI and curl.
- Browser
- Download file 2.43 GB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Pharma-XLarge-770M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Pharma-XLarge-770M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Pharma-XLarge-770M/resolve/main/pytorch_model.bin
2.43 GB
- Xet hash:
- 2a7a6f8b296eee91b3e2234a8a3c3649c61d41e35e897afb8f5c394951a31eb2
- Size of remote file:
- 2.43 GB
- SHA256:
- 95d9bd8e7eaffab65eb0304d4d1b0b6bc102486b6522bca7260fb97c3d0ea0fe
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