| --- |
| license: apache-2.0 |
| datasets: |
| - nyu-mll/glue |
| - SetFit/mrpc |
| language: |
| - en |
| metrics: |
| - accuracy 0.8823529411764706 |
| - f1 0.9178082191780821 |
| library_name: transformers |
| pipeline_tag: text-classification |
| --- |
| --- |
|
|
| # MRPC-bert |
|
|
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. |
|
|
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - num_epochs: 3 |
| |
| ### Framework versions |
| |
| - Transformers 4.38.0.dev0 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.0 |
| |
| #Running model with Python |
| ``` |
| from transformers import pipeline |
| |
| classifier = pipeline("text-classification", model="brianhuster/MRPC-bert") |
| classifier( |
| "Sentence 1. Sentence 2." |
| ) |
| ``` |
| 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. |
