Instructions to use textattack/distilbert-base-uncased-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-uncased-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-uncased-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-uncased-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-uncased-RTE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f453a2be245d7788e4f2313aa591bc6c0aaf42a7f688830b5f27e756bf49098
- Size of remote file:
- 268 MB
- SHA256:
- 8e622eb812a2527e8f5f424c7b55f52fcdfc61c4beedad069b7762655015a4d4
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