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")# 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
- Xet hash:
- 428f1640aaea5eebc67254ffd5ed7838e41c9c78b27922dd554df9b509e7b00a
- Size of remote file:
- 3.71 kB
- SHA256:
- 11cbdec0a509cc9b5ff291ebf78a7c6366dd134daee00f8ac255c2bd86939da8
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