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---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
model-index:
- name: bbau-semeval25_fold4
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. -->
# bbau-semeval25_fold4
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: 0.4458
- Precision Samples: 1.0
- Recall Samples: 0.0
- F1 Samples: 0.0
- Precision Macro: 1.0
- Recall Macro: 0.4091
- F1 Macro: 0.4091
- Precision Micro: 1.0
- Recall Micro: 0.0
- F1 Micro: 0.0
- Precision Weighted: 1.0
- Recall Weighted: 0.0
- F1 Weighted: 0.0
## 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: 32
- seed: 42
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| No log | 1.0 | 5 | 0.6306 | 0.0699 | 0.2911 | 0.1095 | 0.5166 | 0.6006 | 0.2907 | 0.0704 | 0.3585 | 0.1176 | 0.4119 | 0.3585 | 0.1190 |
| 0.6408 | 2.0 | 10 | 0.5798 | 0.0481 | 0.1398 | 0.0666 | 0.7414 | 0.4794 | 0.3825 | 0.0505 | 0.1321 | 0.0731 | 0.5897 | 0.1321 | 0.0513 |
| 0.6408 | 3.0 | 15 | 0.5419 | 0.05 | 0.0175 | 0.0243 | 0.8951 | 0.4394 | 0.3810 | 0.0326 | 0.0283 | 0.0303 | 0.8880 | 0.0283 | 0.0023 |
| 0.5512 | 4.0 | 20 | 0.5123 | 0.0625 | 0.0092 | 0.0143 | 0.9705 | 0.4242 | 0.4106 | 0.0392 | 0.0189 | 0.0255 | 0.9727 | 0.0189 | 0.0018 |
| 0.5512 | 5.0 | 25 | 0.4900 | 0.975 | 0.005 | 0.0083 | 0.9924 | 0.4167 | 0.4167 | 0.5 | 0.0094 | 0.0185 | 0.9906 | 0.0094 | 0.0094 |
| 0.5002 | 6.0 | 30 | 0.4730 | 1.0 | 0.0 | 0.0 | 1.0 | 0.4091 | 0.4091 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 0.5002 | 7.0 | 35 | 0.4612 | 1.0 | 0.0 | 0.0 | 1.0 | 0.4091 | 0.4091 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 0.4705 | 8.0 | 40 | 0.4529 | 1.0 | 0.0 | 0.0 | 1.0 | 0.4091 | 0.4091 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 0.4705 | 9.0 | 45 | 0.4477 | 1.0 | 0.0 | 0.0 | 1.0 | 0.4091 | 0.4091 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 0.4562 | 10.0 | 50 | 0.4458 | 1.0 | 0.0 | 0.0 | 1.0 | 0.4091 | 0.4091 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.20.1
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