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library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: BingoGuard-bert-base-pt
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. -->
# BingoGuard-bert-base-pt
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: 0.1359
- Accuracy: 0.9438
- F1: 0.6724
## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4101 | 1.0 | 456 | 0.1396 | 0.9428 | 0.6398 |
| 0.3366 | 2.0 | 912 | 0.1291 | 0.9393 | 0.6516 |
| 0.2788 | 3.0 | 1368 | 0.1209 | 0.9462 | 0.6766 |
| 0.2695 | 4.0 | 1824 | 0.1321 | 0.9413 | 0.6648 |
| 0.236 | 5.0 | 2280 | 0.1359 | 0.9438 | 0.6724 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4
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