Instructions to use hf-tiny-model-private/tiny-random-OPTForSequenceClassification 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-OPTForSequenceClassification 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-OPTForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-OPTForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-OPTForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c393110f96215256d51a371a1d23af7271d749eb5a6d3b85d858dee8846f7072
- Size of remote file:
- 125 kB
- SHA256:
- 8a0d1bac2b4d37d814e4e9369834e64cabe8fb01d92abc2e9c3add03d30e8bfe
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