Instructions to use jurabi/bert-ner-japanese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jurabi/bert-ner-japanese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jurabi/bert-ner-japanese")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jurabi/bert-ner-japanese") model = AutoModelForTokenClassification.from_pretrained("jurabi/bert-ner-japanese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jurabi/bert-ner-japanese: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/jurabi/bert-ner-japanese/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jurabi/bert-ner-japanese/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jurabi/bert-ner-japanese/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- a7197cf1667bac216f269fe0054b4188bc666dbae2428efd3ede1f80b4745618
- Size of remote file:
- 443 MB
- SHA256:
- e3b7146238ac2e2a45a7f90ae665b1513c7a764cbd2ef18332bbe9a215f9c907
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