Instructions to use textattack/bert-base-uncased-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-RTE") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 321854041189539cac961919dd3358c1136c5d71d193a5926affe8a1e1ecde5a
- Size of remote file:
- 438 MB
- SHA256:
- b0e9f179bc2d61c419efdd19aa7d03cc99c2cbc82cc2e206d6460b350d56dc91
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