Instructions to use hf-tiny-model-private/tiny-random-SqueezeBertForTokenClassification 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-SqueezeBertForTokenClassification 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-SqueezeBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39ce889766d9a8e5b22edd850313ffc59517adad515cc9bf06d9dcb26b9f2490
- Size of remote file:
- 328 kB
- SHA256:
- 48a71f3b39ddb03c4c454bacac2cf4c92dce752db1392d21498f1d068c5f44ab
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