Instructions to use EmergentMethods/gliner_large_news-v2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use EmergentMethods/gliner_large_news-v2.1 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("EmergentMethods/gliner_large_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_large_news-v2.1: direct link, hf CLI and curl.
- Browser
- Download file 1.78 GB
-
https://huggingface.co/EmergentMethods/gliner_large_news-v2.1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EmergentMethods/gliner_large_news-v2.1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EmergentMethods/gliner_large_news-v2.1/resolve/main/pytorch_model.bin
1.78 GB
- Xet hash:
- c284016273cfada4de71e0524a193595db1edc19cb69fa05db56d64a36b07786
- Size of remote file:
- 1.78 GB
- SHA256:
- 9aa8388917daf4ccbb36c939a551dda099820ef5c34f84c1ad3bf9420f0662db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.