---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- precision
model-index:
- name: clasificador-ser-estar-window-3-bert-base
  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. -->

# clasificador-ser-estar-window-3-bert-base

This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4416
- F1 Score: 0.8648
- Recall: 0.9578
- Precision: 0.7882
- Roc Auc: 0.8802

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Score | Recall | Precision | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 25   | 0.6602          | 0.7465   | 1.0    | 0.5955    | 0.6476  |
| No log        | 2.0   | 50   | 0.5149          | 0.8342   | 0.9873 | 0.7222    | 0.8281  |
| No log        | 3.0   | 75   | 0.4397          | 0.8561   | 0.9916 | 0.7532    | 0.8710  |
| No log        | 4.0   | 100  | 0.4293          | 0.8566   | 0.9325 | 0.7921    | 0.8897  |
| No log        | 5.0   | 125  | 0.4076          | 0.8566   | 0.9325 | 0.7921    | 0.8882  |
| No log        | 6.0   | 150  | 0.4416          | 0.8648   | 0.9578 | 0.7882    | 0.8802  |
| No log        | 7.0   | 175  | 0.4894          | 0.8499   | 0.9198 | 0.7899    | 0.8800  |
| No log        | 8.0   | 200  | 0.5129          | 0.8577   | 0.9283 | 0.7971    | 0.8907  |
| No log        | 9.0   | 225  | 0.5474          | 0.8532   | 0.9198 | 0.7956    | 0.8817  |
| No log        | 10.0  | 250  | 0.6858          | 0.8377   | 0.8819 | 0.7977    | 0.8750  |
| No log        | 11.0  | 275  | 0.6811          | 0.8465   | 0.9072 | 0.7934    | 0.8740  |
| No log        | 12.0  | 300  | 0.7265          | 0.8538   | 0.9367 | 0.7845    | 0.8761  |
| No log        | 13.0  | 325  | 0.7422          | 0.8532   | 0.9198 | 0.7956    | 0.8825  |
| No log        | 14.0  | 350  | 0.8648          | 0.8409   | 0.9030 | 0.7868    | 0.8743  |
| No log        | 15.0  | 375  | 0.8326          | 0.8498   | 0.9072 | 0.7993    | 0.8816  |
| No log        | 16.0  | 400  | 0.8516          | 0.8504   | 0.9114 | 0.7970    | 0.8776  |
| No log        | 17.0  | 425  | 0.8633          | 0.8487   | 0.9114 | 0.7941    | 0.8862  |
| No log        | 18.0  | 450  | 0.9064          | 0.8475   | 0.9030 | 0.7985    | 0.8856  |
| No log        | 19.0  | 475  | 0.9145          | 0.8475   | 0.9030 | 0.7985    | 0.8856  |
| 0.2549        | 20.0  | 500  | 0.9146          | 0.8475   | 0.9030 | 0.7985    | 0.8860  |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0