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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/roberta-base-RTE: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/textattack/roberta-base-RTE/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/roberta-base-RTE/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/roberta-base-RTE/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- ac6a8f181d7956ea903a1095cb94d7bfdc1143d2add234014d3cd11fd2afab0b
- Size of remote file:
- 501 MB
- SHA256:
- 490c7a300989f1822ace2b9d89d2fe6572b22a3916d22e53f59fa547dd458c33
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