Instructions to use hf-internal-testing/tiny-random-SqueezeBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SqueezeBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SqueezeBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 780ac0126f660ca83e12cb25678c2dae65fe3b1e5bda6182b16ae5a6659a838e
- Size of remote file:
- 327 kB
- SHA256:
- 719a0e4b994c95bb00015a739514d6487b27d0959d1b36f188eec6ee1a73699f
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