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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/bert-base-uncased-RTE: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-RTE/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://textattack/bert-base-uncased-RTE@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/bert-base-uncased-RTE/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- f07365a2598965977ed68b80c049e7b0808912a0fea42e51105c8fac2af9ae4b
- Size of remote file:
- 438 MB
- SHA256:
- 63733fb269828a14b20e371ee6ca3da62028da2831aaeaea00fe09df62e53b3a
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