Instructions to use abrei/m1-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abrei/m1-tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="abrei/m1-tagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("abrei/m1-tagger") model = AutoModelForTokenClassification.from_pretrained("abrei/m1-tagger") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2f23e92590bcc6846e266eef8526023ac2c6f8df37d0484a59e127867b0a2d1
- Size of remote file:
- 436 MB
- SHA256:
- abe100487f15754d0671bc031f35196bb72528fa50bb5539b0d2c723c96980a7
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