---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-mrpc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8774509803921569
    - name: F1
      type: f1
      value: 0.9137931034482758
  - task:
      type: natural-language-inference
      name: Natural Language Inference
    dataset:
      name: glue
      type: glue
      config: mrpc
      split: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.979825517993457
      verified: true
    - name: Precision
      type: precision
      value: 0.9842615012106537
      verified: true
    - name: Recall
      type: recall
      value: 0.9858528698464026
      verified: true
    - name: AUC
      type: auc
      value: 0.9958293217637636
      verified: true
    - name: F1
      type: f1
      value: 0.9850565428109854
      verified: true
    - name: loss
      type: loss
      value: 0.08004990220069885
      verified: true
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-base-mrpc

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5565
- Accuracy: 0.8775
- F1: 0.9138
- Combined Score: 0.8956

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6