Instructions to use hf-internal-testing/tiny-random-OPTForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-OPTForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-OPTForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-OPTForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-OPTForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d8a40ea173ce77ddc755da55809b9ee4ddfa14929fbd6da96c2655a433b5c34
- Size of remote file:
- 3.26 MB
- SHA256:
- bb13e4991ce469601d090935d113dd363597ccf6677042d632064d96745dc93a
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