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:
- 739ee568a211aa7872a5cb6b77280b876e7741e34a9dbdc72c66b4a09ac6a956
- Size of remote file:
- 6.57 MB
- SHA256:
- 5cba329575d016727fa2f9cd580426b8613c9c4e70b2475807745490886b29af
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