---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
- recall
model-index:
- name: newsdata-bertimbal
  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. -->

# newsdata-bertimbal

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: 0.2924
- Accuracy: 0.9183
- Precision: 0.9118
- F1: 0.9144
- Recall: 0.9183

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | F1     | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7154        | 0.1024 | 1000 | 0.5830          | 0.856    | 0.8352    | 0.8399 | 0.856  |
| 0.5232        | 0.2048 | 2000 | 0.4769          | 0.874    | 0.8647    | 0.8633 | 0.874  |
| 0.4342        | 0.3071 | 3000 | 0.3966          | 0.891    | 0.8800    | 0.8826 | 0.891  |
| 0.3969        | 0.4095 | 4000 | 0.3509          | 0.9023   | 0.8900    | 0.8949 | 0.9023 |
| 0.3719        | 0.5119 | 5000 | 0.3263          | 0.9102   | 0.9055    | 0.9054 | 0.9102 |
| 0.3638        | 0.6143 | 6000 | 0.3209          | 0.909    | 0.9017    | 0.9035 | 0.909  |
| 0.3217        | 0.7166 | 7000 | 0.3131          | 0.9068   | 0.9025    | 0.9034 | 0.9068 |
| 0.3169        | 0.8190 | 8000 | 0.2952          | 0.9167   | 0.9101    | 0.9125 | 0.9167 |
| 0.3147        | 0.9214 | 9000 | 0.2924          | 0.9183   | 0.9118    | 0.9144 | 0.9183 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1