---
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.