Instructions to use textattack/facebook-bart-large-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/facebook-bart-large-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/facebook-bart-large-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/facebook-bart-large-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/facebook-bart-large-RTE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e7a245e937a6d4408758e85225116c10d397db024fbba486f29667996669397
- Size of remote file:
- 1.05 kB
- SHA256:
- 14a913b2b63cf8227ebef289ca82ed73b0a93fb8fbee10eb9ddf09b842e509af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.