Instructions to use shivangi/MRPC_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shivangi/MRPC_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shivangi/MRPC_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shivangi/MRPC_output") model = AutoModelForSequenceClassification.from_pretrained("shivangi/MRPC_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2dd65500e8e36b072e9c961baa64fa2978cfde8dd3911a6c13884f908c96495
- Size of remote file:
- 433 MB
- SHA256:
- 77a64fa5b98fe066b730081693ff536f1d0743042e522447febfdeb5c286e628
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