Instructions to use hf-tiny-model-private/tiny-random-RoFormerForSequenceClassification 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-RoFormerForSequenceClassification 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-RoFormerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RoFormerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-RoFormerForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7396c22fb9135431e9087d450bcc3ba3edb05009f3501110194b49c39bfbf879
- Size of remote file:
- 6.69 MB
- SHA256:
- eb36656f95a45b41b12ee4f95d37113c156d2d4e45f679aa5980f402d656016c
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