Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
protein-interactions
molecular-biology
biochemistry
systems-biology
protein
protein_complex
protein_family
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Protein-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-Protein-XLarge-770M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Protein-XLarge-770M") 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 special_tokens_map.json from OpenMed/OpenMed-ZeroShot-NER-Protein-XLarge-770M: direct link, hf CLI and curl.
- Browser
- Download file 416 Bytes
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-XLarge-770M/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Protein-XLarge-770M/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-XLarge-770M/resolve/main/special_tokens_map.json
416 Bytes
| { | |
| "eos_token": { | |
| "content": "</s>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "<pad>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |