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:
- c511e5ddf880a348a090095d120b8ddc508a80e76092600b360a86743383a07a
- Size of remote file:
- 2.91 MB
- SHA256:
- de79435bc5bdccc93e14bd4afc31dba9617bc1362d51381a7ade316c9f9afeb1
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