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---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: neuralmind/bert-base-portuguese-cased
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. -->
# neuralmind/bert-base-portuguese-cased
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: 0.0460
- Accuracy: 0.8367
- F1: 0.7871
- Recall: 0.8194
- Precision: 0.7687
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 5151
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 19
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0801 | 1.0 | 18 | 0.0769 | 0.7411 | 0.4256 | 0.5 | 0.3705 |
| 0.0691 | 2.0 | 36 | 0.0709 | 0.75 | 0.4612 | 0.5172 | 0.8739 |
| 0.0647 | 3.0 | 54 | 0.0661 | 0.75 | 0.4612 | 0.5172 | 0.8739 |
| 0.0644 | 4.0 | 72 | 0.0648 | 0.6518 | 0.5774 | 0.5856 | 0.5753 |
| 0.0621 | 5.0 | 90 | 0.0632 | 0.7054 | 0.6424 | 0.6554 | 0.6367 |
| 0.0621 | 6.0 | 108 | 0.0627 | 0.7232 | 0.6265 | 0.6226 | 0.6319 |
| 0.0586 | 7.0 | 126 | 0.0595 | 0.75 | 0.6937 | 0.7079 | 0.6857 |
| 0.0547 | 8.0 | 144 | 0.0582 | 0.7768 | 0.7338 | 0.7597 | 0.7223 |
| 0.0509 | 9.0 | 162 | 0.0554 | 0.7768 | 0.7338 | 0.7597 | 0.7223 |
| 0.0462 | 10.0 | 180 | 0.0557 | 0.75 | 0.7091 | 0.7416 | 0.6998 |
| 0.0437 | 11.0 | 198 | 0.0532 | 0.7768 | 0.7382 | 0.7709 | 0.7264 |
| 0.0415 | 12.0 | 216 | 0.0515 | 0.7857 | 0.7466 | 0.7769 | 0.7341 |
| 0.0356 | 13.0 | 234 | 0.0545 | 0.8036 | 0.7547 | 0.7665 | 0.7461 |
| 0.0301 | 14.0 | 252 | 0.0543 | 0.8214 | 0.7770 | 0.7898 | 0.7675 |
| 0.0262 | 15.0 | 270 | 0.0541 | 0.8036 | 0.7594 | 0.7777 | 0.7481 |
| 0.0248 | 16.0 | 288 | 0.0583 | 0.8125 | 0.7584 | 0.7613 | 0.7557 |
| 0.0232 | 17.0 | 306 | 0.0593 | 0.8125 | 0.7635 | 0.7725 | 0.7562 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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