Instructions to use hf-internal-testing/tiny-random-RoFormerForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RoFormerForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-RoFormerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RoFormerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-RoFormerForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52db5dba7860fd758fcea17c88dc3a0b1006a815dbb16e7918341559a612b834
- Size of remote file:
- 6.59 MB
- SHA256:
- 90abd1c3e8a6b20fd2e86ea2b1c60e4cd209ad77aa11703b47b6006bd5197e47
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