pytorch tensorboard bert text-classification dataset:glue transformers generated_from_trainer license:apache-2.0
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model_index:
- name: finetuned-bert-mrpc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: mrpc
    metric:
      name: F1
      type: f1
      value: 0.8791946308724832
---

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# finetuned-bert-mrpc

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4917
- Accuracy: 0.8235
- F1: 0.8792

## 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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5382        | 1.0   | 230  | 0.4008          | 0.8456   | 0.8893 |
| 0.3208        | 2.0   | 460  | 0.4182          | 0.8309   | 0.8844 |
| 0.1587        | 3.0   | 690  | 0.4917          | 0.8235   | 0.8792 |


### Framework versions

- Transformers 4.9.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 1.8.1.dev0
- Tokenizers 0.10.1
