Instructions to use hf-tiny-model-private/tiny-random-OpenAIGPTForSequenceClassification 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-OpenAIGPTForSequenceClassification 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-OpenAIGPTForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-OpenAIGPTForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-OpenAIGPTForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74904ee841f0d422027697099707973a2bb9d1eee3629ed65bd95a9023229c1e
- Size of remote file:
- 5.75 MB
- SHA256:
- 69ac2574971632e3b7c16a4a4c7390a5ea56c9342c54213632b0bc0eb552e43e
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