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:
- 4f920805cd28380ada4aa9ac816ad7c5442852a07e83bf79946b412b4a86bf95
- Size of remote file:
- 6.72 MB
- SHA256:
- 01604133ee5dc57cb4ce87548e171ace46188aa43accf1b1b8bec12a8024602d
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