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:
- 2421caebb26d7c7225b0321e4e8236d6ea2a88871e92b0f8bd59ae0a5a28fefb
- Size of remote file:
- 1.06 kB
- SHA256:
- 0380407c55c600ff701999785128b6e741534d432a81df03fe59d233992c6687
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