---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
model-index:
- name: e3_lr2e-05
  results: []
---

<!-- 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. -->

# e3_lr2e-05

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5721

## 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: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.2771        | 0.0707 | 100  | 1.9875          |
| 2.0486        | 0.1414 | 200  | 1.8946          |
| 1.993         | 0.2121 | 300  | 1.8415          |
| 1.9532        | 0.2828 | 400  | 1.8133          |
| 1.9145        | 0.3535 | 500  | 1.7807          |
| 1.8872        | 0.4242 | 600  | 1.7534          |
| 1.8593        | 0.4949 | 700  | 1.7357          |
| 1.8447        | 0.5656 | 800  | 1.7173          |
| 1.8149        | 0.6363 | 900  | 1.7074          |
| 1.7966        | 0.7070 | 1000 | 1.7036          |
| 1.8034        | 0.7777 | 1100 | 1.6883          |
| 1.7854        | 0.8484 | 1200 | 1.6740          |
| 1.7779        | 0.9191 | 1300 | 1.6642          |
| 1.7706        | 0.9897 | 1400 | 1.6582          |
| 1.7723        | 1.0604 | 1500 | 1.6475          |
| 1.746         | 1.1311 | 1600 | 1.6463          |
| 1.7386        | 1.2018 | 1700 | 1.6399          |
| 1.7319        | 1.2725 | 1800 | 1.6385          |
| 1.7292        | 1.3432 | 1900 | 1.6230          |
| 1.7121        | 1.4139 | 2000 | 1.6204          |
| 1.7245        | 1.4846 | 2100 | 1.6152          |
| 1.7159        | 1.5553 | 2200 | 1.6103          |
| 1.7232        | 1.6260 | 2300 | 1.6114          |
| 1.6952        | 1.6967 | 2400 | 1.6099          |
| 1.6944        | 1.7674 | 2500 | 1.6012          |
| 1.6991        | 1.8381 | 2600 | 1.5970          |
| 1.6954        | 1.9088 | 2700 | 1.5933          |
| 1.698         | 1.9795 | 2800 | 1.5918          |
| 1.6857        | 2.0502 | 2900 | 1.5915          |
| 1.6783        | 2.1209 | 3000 | 1.5840          |
| 1.679         | 2.1916 | 3100 | 1.5817          |
| 1.6796        | 2.2623 | 3200 | 1.5835          |
| 1.6709        | 2.3330 | 3300 | 1.5769          |
| 1.6626        | 2.4037 | 3400 | 1.5819          |
| 1.6732        | 2.4744 | 3500 | 1.5824          |
| 1.6726        | 2.5458 | 3600 | 1.5720          |
| 1.6822        | 2.6165 | 3700 | 1.5758          |
| 1.6578        | 2.6872 | 3800 | 1.5739          |
| 1.6756        | 2.7579 | 3900 | 1.5743          |
| 1.6747        | 2.8286 | 4000 | 1.5695          |
| 1.659         | 2.8993 | 4100 | 1.5713          |
| 1.6587        | 2.9700 | 4200 | 1.5750          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1