Instructions to use hf-tiny-model-private/tiny-random-Data2VecTextForTokenClassification 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-Data2VecTextForTokenClassification 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-Data2VecTextForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c1c7801b8abf48952d5071d4f5c30a22719cc9b3d896ecb8806e519f5c2a176
- Size of remote file:
- 368 kB
- SHA256:
- 80a1d554256f445a17b57aca751365c92a9efc514602155d1f79057c7dadf15b
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