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:
- 1fb636e9297bc6be9a3a18665044a5ce10d2805b4cbe111b987533cf14ec85b4
- Size of remote file:
- 6.59 MB
- SHA256:
- b955a06ffaf87c921b90d6cb1d5efd25090fe6b41718b4ba8d1eb7617a15f1b4
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