Token Classification
Transformers
PyTorch
Graphcore
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use jimypbr/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimypbr/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jimypbr/test-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jimypbr/test-ner") model = AutoModelForTokenClassification.from_pretrained("jimypbr/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jimypbr/test-ner: direct link, hf CLI and curl.
- Browser
- Download file 248 MB
-
https://huggingface.co/jimypbr/test-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jimypbr/test-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jimypbr/test-ner/resolve/main/pytorch_model.bin
248 MB
- Xet hash:
- 3db85677f8b4c693ee58c86a16ad80d8269495fc3ec109d2e58b328f162ee148
- Size of remote file:
- 248 MB
- SHA256:
- 571c37e02f1038e53a9d36e0e2f22dbad43f24f2cacf9aed48499066230f2fe2
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