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---
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
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