Instructions to use jamesdborin/Electra-Large-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesdborin/Electra-Large-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jamesdborin/Electra-Large-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jamesdborin/Electra-Large-RTE") model = AutoModelForSequenceClassification.from_pretrained("jamesdborin/Electra-Large-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f132be4531d6bc7c3d772b56229ed326eba90b42e584bf93a3a16e0f4e5f36b8
- Size of remote file:
- 1.34 GB
- SHA256:
- 5ea622031f3941b7d08edd6d42e63b2841fccc08464fec80cd7c597f8800d667
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