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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dde632ab0167a395a95bc0b808e1184f5143d3517b89269887771b1abd53987f
- Size of remote file:
- 2.93 MB
- SHA256:
- ff26e0855cc206d239d887d47658e3ebd7d8fbad7c26d7e491395ec4c2596dd6
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