---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: finetuned-bert-categories-estimation
  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. -->

# finetuned-bert-categories-estimation

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3765
- F1: 0.8829
- Accuracy: 0.9185

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 4.1819        | 0.13  | 100  | 3.4072          | 0.1568 | 0.4170   |
| 3.0332        | 0.27  | 200  | 2.4751          | 0.3097 | 0.5559   |
| 2.3356        | 0.4   | 300  | 1.9235          | 0.4379 | 0.6605   |
| 1.8533        | 0.53  | 400  | 1.5348          | 0.5528 | 0.7300   |
| 1.5404        | 0.66  | 500  | 1.2910          | 0.6279 | 0.7834   |
| 1.3375        | 0.8   | 600  | 1.0888          | 0.6428 | 0.7987   |
| 1.09          | 0.93  | 700  | 0.9613          | 0.6828 | 0.8233   |
| 0.9577        | 1.06  | 800  | 0.8399          | 0.7188 | 0.8387   |
| 0.7999        | 1.2   | 900  | 0.7625          | 0.7396 | 0.8510   |
| 0.7067        | 1.33  | 1000 | 0.7112          | 0.7534 | 0.8537   |
| 0.6479        | 1.46  | 1100 | 0.6395          | 0.7807 | 0.8695   |
| 0.6           | 1.6   | 1200 | 0.6111          | 0.8015 | 0.8781   |
| 0.5168        | 1.73  | 1300 | 0.5787          | 0.8070 | 0.8783   |
| 0.5635        | 1.86  | 1400 | 0.5333          | 0.8167 | 0.8873   |
| 0.5094        | 1.99  | 1500 | 0.5283          | 0.8217 | 0.8868   |
| 0.3862        | 2.13  | 1600 | 0.4973          | 0.8257 | 0.8908   |
| 0.3663        | 2.26  | 1700 | 0.4879          | 0.8281 | 0.8889   |
| 0.3584        | 2.39  | 1800 | 0.4619          | 0.8406 | 0.8973   |
| 0.3427        | 2.53  | 1900 | 0.4460          | 0.8555 | 0.9044   |
| 0.3334        | 2.66  | 2000 | 0.4386          | 0.8600 | 0.9056   |
| 0.3267        | 2.79  | 2100 | 0.4274          | 0.8638 | 0.9064   |
| 0.3045        | 2.93  | 2200 | 0.4154          | 0.8704 | 0.9094   |
| 0.3048        | 3.06  | 2300 | 0.4156          | 0.8703 | 0.9106   |
| 0.2329        | 3.19  | 2400 | 0.4068          | 0.8640 | 0.9097   |
| 0.2393        | 3.32  | 2500 | 0.3957          | 0.8766 | 0.9122   |
| 0.2335        | 3.46  | 2600 | 0.3923          | 0.8776 | 0.9159   |
| 0.201         | 3.59  | 2700 | 0.3840          | 0.8810 | 0.9175   |
| 0.2156        | 3.72  | 2800 | 0.3849          | 0.8817 | 0.9174   |
| 0.2135        | 3.86  | 2900 | 0.3777          | 0.8833 | 0.9190   |
| 0.2164        | 3.99  | 3000 | 0.3765          | 0.8829 | 0.9185   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0