--- 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.