Instructions to use hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification 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-SqueezeBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ed7f19c41005f90d258af81a302198e4f45c33065711e3a80916dc3e8033389
- Size of remote file:
- 347 kB
- SHA256:
- cc5b8d6151adeb30439ef8de1284428447e6b3a78287cd73657f73472ba67fa0
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