Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M") 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
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Pathology-Large-459M
86258d7 verified Download special_tokens_map.json from OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M: direct link, hf CLI and curl.
- Browser
- Download file 286 Bytes
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M/resolve/main/special_tokens_map.json
286 Bytes
| { | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": { | |
| "content": "[UNK]", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |