Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
species-recognition
taxonomy
organism-identification
biology
species
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Species-Multi-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Species-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Species-Multi-209M") 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/OpenMed-ZeroShot-NER-Species-Multi-209M: direct link, hf CLI and curl.
- Browser
- Download file 1.16 GB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Species-Multi-209M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Species-Multi-209M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Species-Multi-209M/resolve/main/pytorch_model.bin
1.16 GB
- Xet hash:
- ea98df174471de72ada43332e8359b556d21566a3545b6664aea938cbc2c78be
- Size of remote file:
- 1.16 GB
- SHA256:
- 001be2e7038aac2221c79f749ef39a624ad2df77a1f29f2cd89f87b74c66e0db
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