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:
- 62d537a53aac94bd9cb62b48b36e9e4a67ff712534d3f6d7cd18d3ba1d7cfdfe
- Size of remote file:
- 1.09 kB
- SHA256:
- 887cb924bb2595548dd76697a6527ad35ec5231fe519a7fe741925d1ca234ae4
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