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--- |
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license: apache-2.0 |
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datasets: |
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- nyu-mll/glue |
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- SetFit/mrpc |
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language: |
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- en |
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metrics: |
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- accuracy 0.8823529411764706 |
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- f1 0.9178082191780821 |
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library_name: transformers |
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pipeline_tag: text-classification |
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--- |
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--- |
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# MRPC-bert |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- num_epochs: 3 |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.0 |
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#Running model with Python |
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``` |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="brianhuster/MRPC-bert") |
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classifier( |
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"Sentence 1. Sentence 2." |
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) |
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``` |
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Replace "Sentence 1" and "Sentence 2" with your actual input sentence. Each sentence should end with a fullstop, even if they are questions. The model will return LABEL_1 if they are are equivalent in meaning, LABEL_1 otherwise. |
