Instructions to use shivangi/MRPC_64_128_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shivangi/MRPC_64_128_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shivangi/MRPC_64_128_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shivangi/MRPC_64_128_output") model = AutoModelForSequenceClassification.from_pretrained("shivangi/MRPC_64_128_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4557cccd67366bcd1c992f86f048a0cd51cf79f69e2ea5d0045c3b620c2347f6
- Size of remote file:
- 433 MB
- SHA256:
- 87c15143d2c858e60376a743c8d2744c571e2643c0a0ffc61e04943942024501
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