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:
- b065d5bfb6f93c838b9a80a94ffa0838aadda3e5c872b401ef12af003f224dd8
- Size of remote file:
- 1.42 GB
- SHA256:
- 4bb882437edc87eac1b49b52c484d21fa60e3df555805400bd998c66085b2830
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