metadata

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 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.