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:
- 3a58d1d716462d58449d2c8258dd6d0558521d1e4ff55d7f1e940792fc900668
- Size of remote file:
- 1.05 kB
- SHA256:
- 24b2bb6c082efcc9b5a8c1dbf0f62d065ab5c72c4847cb8ebf42edd5efca94e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.