Instructions to use hf-tiny-model-private/tiny-random-SqueezeBertModel 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-SqueezeBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-SqueezeBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62df19c788552727e9c1b8d4540ac1e819c591cb97c573bb3f0bc91a0461dc97
- Size of remote file:
- 327 kB
- SHA256:
- ba5a9840bb732f562f4056aa43a4f6bc1512d97ff32dd5766f23a52b0143aa92
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