Instructions to use hf-tiny-model-private/tiny-random-NezhaForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-NezhaForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-NezhaForTokenClassification")# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-NezhaForTokenClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9232c15d3ced62d551410ce924f32d376893ce238c69788a7515894fb308f7eb
- Size of remote file:
- 2.93 MB
- SHA256:
- 51454b85b185ab918eee87b6ea496540733b857b5f8c1df6d333687d57889b05
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