Token Classification
GLiNER
PyTorch
multilingual
NER
GLiNER
information extraction
encoder
entity recognition
Instructions to use knowledgator/gliner-llama-1.3B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use knowledgator/gliner-llama-1.3B-v1.0 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("knowledgator/gliner-llama-1.3B-v1.0") 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 knowledgator/gliner-llama-1.3B-v1.0: direct link, hf CLI and curl.
- Browser
- Download file 5.82 GB
-
https://huggingface.co/knowledgator/gliner-llama-1.3B-v1.0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://knowledgator/gliner-llama-1.3B-v1.0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/knowledgator/gliner-llama-1.3B-v1.0/resolve/main/pytorch_model.bin
5.82 GB
- Xet hash:
- 907378602e95c9a151d0f51f6bd59f4251c8af24bbf714a0f6a25ff43ac9bfc8
- Size of remote file:
- 5.82 GB
- SHA256:
- 6e7ed28c6280ca0f67b6fd706451fae9ed8ec7feef033b7ffc1d4c598fce714e
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