Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
body_part
organ
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Anatomy-Tiny-60M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Anatomy-Tiny-60M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Anatomy-Tiny-60M") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-ZeroShot-NER-Anatomy-Tiny-60M: direct link, hf CLI and curl.
- Browser
- Download file 207 Bytes
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Anatomy-Tiny-60M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Anatomy-Tiny-60M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Anatomy-Tiny-60M/resolve/main/test_results.json
207 Bytes
| { | |
| "eval_loss": 230.90248107910156, | |
| "seqeval_accuracy": 0.916925259331501, | |
| "seqeval_f1": 0.5613611195006544, | |
| "seqeval_precision": 0.5243558397592627, | |
| "seqeval_recall": 0.6039861351819757 | |
| } |