Instructions to use hf-internal-testing/tiny-random-SqueezeBertForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SqueezeBertForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-SqueezeBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22b95356adb0bb76056c6128d76c92f0f332cc6b48bcbcaa3e2cf6395a772b07
- Size of remote file:
- 347 kB
- SHA256:
- ae49590dd7a6d88c3a7794453a634a1d41806f4c76f55f6f149d266ef71466e9
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