Instructions to use dwmit/ja_classification_brl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwmit/ja_classification_brl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dwmit/ja_classification_brl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dwmit/ja_classification_brl") model = AutoModelForTokenClassification.from_pretrained("dwmit/ja_classification_brl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dwmit/ja_classification_brl: direct link, hf CLI and curl.
- Browser
- Download file 735 MB
-
https://huggingface.co/dwmit/ja_classification_brl/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://dwmit/ja_classification_brl@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dwmit/ja_classification_brl/resolve/refs%2Fpr%2F1/pytorch_model.bin
735 MB
- Xet hash:
- fe195f384ca4f1937fec28e879fcd7cd99b2c1eb6a4f873bd1ae04759c9fc477
- Size of remote file:
- 735 MB
- SHA256:
- b2b177ef2f9b2e0abd67334c2e4cb4abb3cb35b970ffc0757c21a359f71e1b94
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