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 tokenizer.json from dwmit/ja_classification_brl: direct link, hf CLI and curl.
- Browser
- Download file 3.54 MB
-
https://huggingface.co/dwmit/ja_classification_brl/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://dwmit/ja_classification_brl@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/dwmit/ja_classification_brl/resolve/refs%2Fpr%2F1/tokenizer.json
3.54 MB
- Xet hash:
- f7a852eccfda94cf536ba5daa6cf5169d901d3c31aeef5a85322e6f5c2539a2a
- Size of remote file:
- 3.54 MB
- SHA256:
- 1efb44684871d45b60c0726aa4ab4f51b5e4415da78e7aad02fe78a0863ffbfc
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