Instructions to use EmergentMethods/gliner_medium_news-v2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use EmergentMethods/gliner_medium_news-v2.1 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("EmergentMethods/gliner_medium_news-v2.1") 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 EmergentMethods/gliner_medium_news-v2.1: direct link, hf CLI and curl.
- Browser
- Download file 781 MB
-
https://huggingface.co/EmergentMethods/gliner_medium_news-v2.1/resolve/refs%2Fpr%2F3/pytorch_model.bin
- Command line
-
hf download hf://EmergentMethods/gliner_medium_news-v2.1@refs/pr/3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EmergentMethods/gliner_medium_news-v2.1/resolve/refs%2Fpr%2F3/pytorch_model.bin
781 MB
- Xet hash:
- 7b135140e9aac1629d0f806e28ec52e207fe9ef1e0af3e9c87d43b46ee18c373
- Size of remote file:
- 781 MB
- SHA256:
- 046d387879654a97f4af6df707a18ce9d1196b021f94a17cc6fc0858f7bac665
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.