Instructions to use hz53/roberta-base-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hz53/roberta-base-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hz53/roberta-base-rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hz53/roberta-base-rte") model = AutoModelForSequenceClassification.from_pretrained("hz53/roberta-base-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hz53/roberta-base-rte: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/hz53/roberta-base-rte/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hz53/roberta-base-rte/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hz53/roberta-base-rte/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- d78f549b635d9c4765f006b8b0b3427ac5835ed0072025ca6204e442021de038
- Size of remote file:
- 499 MB
- SHA256:
- f11255255977f7cbe0b03df889a6cd5319c3e2b1c5a21389c05a115f1befc236
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