Instructions to use hf-internal-testing/tiny-random-OpenAIGPTForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-OpenAIGPTForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-OpenAIGPTForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-OpenAIGPTForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-OpenAIGPTForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af52f0d8c41d06ac83bd6c8ae3068253e54f02492d72fd418159d1a8add9bd65
- Size of remote file:
- 584 kB
- SHA256:
- 9c8bc88a97d1533871d9a1f38a3c0c27d2107abfbf93bb0ab71cd383b9ae9983
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