---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- glue-ptpt
metrics:
- accuracy
- f1
model-index:
- name: paraphrase-bert-portuguese
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue-ptpt
      type: glue-ptpt
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8725490196078431
    - name: F1
      type: f1
      value: 0.9106529209621993
---

<!-- 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. -->

# paraphrase-bert-portuguese

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the glue-ptpt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6398
- Accuracy: 0.8725
- F1: 0.9107

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4894        | 1.09  | 500  | 0.3384          | 0.8578   | 0.8945 |
| 0.2603        | 2.18  | 1000 | 0.5077          | 0.8799   | 0.9130 |
| 0.1316        | 3.27  | 1500 | 0.6398          | 0.8725   | 0.9107 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3