Instructions to use jamesdborin/Roberta-Large-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesdborin/Roberta-Large-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jamesdborin/Roberta-Large-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jamesdborin/Roberta-Large-RTE") model = AutoModelForSequenceClassification.from_pretrained("jamesdborin/Roberta-Large-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 067f60d7a8b5f92fe15a6687ab2d646177d872333b43b7ffd984aa5d4fbe3a89
- Size of remote file:
- 1.42 GB
- SHA256:
- 6d6ba03e261199bee0857a3254ff4a840593d0e5630e24fa207d4398e3fb063a
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