Instructions to use hf-internal-testing/tiny-random-NezhaForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NezhaForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-NezhaForTokenClassification")# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-NezhaForTokenClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d557ba648d404c98608ff74323e68cc245593c43d8f2cd77c3760a331695bba8
- Size of remote file:
- 2.91 MB
- SHA256:
- 9690c53617c0d5740334a92ab704dadd44f0415793d3c59559905e7a69e63f78
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