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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f977b915ce881a27b549f248ad575ba52364d8944f6e8f324ae0ffc293807584
- Size of remote file:
- 5.75 MB
- SHA256:
- d89676713d3a0da1430761794a2092b003fe488ddf7d772cfd7bf303a20fefa2
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