Instructions to use DataSign/gliner-ja-pii-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use DataSign/gliner-ja-pii-v1 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("DataSign/gliner-ja-pii-v1") 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 DataSign/gliner-ja-pii-v1: direct link, hf CLI and curl.
- Browser
- Download file 1.16 GB
-
https://huggingface.co/DataSign/gliner-ja-pii-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DataSign/gliner-ja-pii-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DataSign/gliner-ja-pii-v1/resolve/main/pytorch_model.bin
1.16 GB
- Xet hash:
- 4693cd12e7d95474db185ff76d6beb4331b5a1c8ccaabf15f2dab5477fcbf8a5
- Size of remote file:
- 1.16 GB
- SHA256:
- 723c6f30621bc42f3beb1ca910bdb35896349fee8b8060e6878ec4553e1e3144
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