Instructions to use jamesdborin/Roberta-Large-MRPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesdborin/Roberta-Large-MRPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jamesdborin/Roberta-Large-MRPC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jamesdborin/Roberta-Large-MRPC") model = AutoModelForSequenceClassification.from_pretrained("jamesdborin/Roberta-Large-MRPC") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b6c1a7c68065efa9bc2627d5ebd100c2129f47199c802cc8cc3db277a008dff
- Size of remote file:
- 1.42 GB
- SHA256:
- c2d66ce14204b40e1f357b53f7f399e1563792103005fd488cba7e0ef8237014
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