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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb3a48b9ed9752ffa40a97cb26d821bedc2c6c985276064a3d583b35373aa3a8
- Size of remote file:
- 3.41 MB
- SHA256:
- 05e7534aa3a168254c496ad6076ee22a8bcae45ce0abe01faeff1d2583626ce6
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