Token Classification
GLiNER
PyTorch
Catalan
Spanish
NER
Catalan
NLP
television transcriptions
manual annotation
GLiNER
Instructions to use Ugiat/NERCat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use Ugiat/NERCat with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("Ugiat/NERCat") 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 Ugiat/NERCat: direct link, hf CLI and curl.
- Browser
- Download file 2.28 GB
-
https://huggingface.co/Ugiat/NERCat/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ugiat/NERCat/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ugiat/NERCat/resolve/main/pytorch_model.bin
2.28 GB
- Xet hash:
- d67349a2456240ed5a2e6282e974532a7df606b5742b2db312dc0b9f961154d4
- Size of remote file:
- 2.28 GB
- SHA256:
- 0c08bcad26647b3a97583553c9db83d487612972dfa7b18da3f326ec7af9bcf2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.