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")# pip install -U transformers accelerate # 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
Update eval_results_mrpc.txt
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eval_results_mrpc.txt
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eval_loss = 0.4044800681563524
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eval_acc = 0.8308823529411765
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eval_f1 = 0.8812392426850257
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eval_acc_and_f1 = 0.8560607978131012
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epoch = 3.0
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