Instructions to use hf-tiny-model-private/tiny-random-MvpForSequenceClassification 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-MvpForSequenceClassification 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-MvpForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MvpForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-MvpForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ee0111f16a32416de52fd6916c655d34adb45c84362fae9ed8e6f1ff3f959c8
- Size of remote file:
- 140 kB
- SHA256:
- 5dc5b88f3db05a674fde86284315db608b8de40b80ca261eae596b1fa689e227
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