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:
- 58c3808fca01a54a7d0af8509f39739c509d5249dba711eb6814d528f1b43b17
- Size of remote file:
- 328 kB
- SHA256:
- 0ae0ed58eacf03533a7b380460efd830f8a684f6b1c903a58b61e89462da77b9
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