Instructions to use textattack/roberta-base-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/roberta-base-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/roberta-base-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-RTE") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b913ed98062d87b95549184abeb209af735ea5920dfa852760697e43f7992a6
- Size of remote file:
- 499 MB
- SHA256:
- 09d1fcfbfd193659d777dfe5f6c55e7a2955bd96c14c340a87f5025997b3374d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.