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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from textattack/bert-base-uncased-RTE: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/textattack/bert-base-uncased-RTE/resolve/main/training_args.bin
- Command line
-
hf download hf://textattack/bert-base-uncased-RTE/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/textattack/bert-base-uncased-RTE/resolve/main/training_args.bin
1.05 kB
- Xet hash:
- 3a58d1d716462d58449d2c8258dd6d0558521d1e4ff55d7f1e940792fc900668
- Size of remote file:
- 1.05 kB
- SHA256:
- 24b2bb6c082efcc9b5a8c1dbf0f62d065ab5c72c4847cb8ebf42edd5efca94e7
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