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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f609197be0ce5a39d1be4e09802e5263fdbc88d33cab9bb3b363413badbf6d0a
- Size of remote file:
- 433 MB
- SHA256:
- 0fae9e9beee8b45d0d51808593d7e8229a56b1c07e1c706a3a0cb606b8f40ced
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