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:
- 1528c4cc67b728d0160e30b1129e76a369d9c35c59c8afb036b408e8d4e9309d
- Size of remote file:
- 107 kB
- SHA256:
- 71168cf7ce8e0dd198dbde1fc60c10843b159853d36633f012a9f18381ad6c74
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