pytorch tensorboard bert text-classification dataset:glue transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-mrpc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8578431372549019
    - name: F1
      type: f1
      value: 0.9003436426116839
---

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# bert-base-uncased-finetuned-mrpc

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4520
- Accuracy: 0.8578
- F1: 0.9003

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 230  | 0.4169          | 0.8039   | 0.8639 |
| No log        | 2.0   | 460  | 0.4299          | 0.8137   | 0.875  |
| 0.4242        | 3.0   | 690  | 0.4520          | 0.8578   | 0.9003 |
| 0.4242        | 4.0   | 920  | 0.6323          | 0.8431   | 0.8926 |
| 0.1103        | 5.0   | 1150 | 0.6163          | 0.8578   | 0.8997 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
