metadata
library_name: transformers
license: mit
base_model: belisards/congretimbau
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: belisards/congretimbau
results: []
belisards/congretimbau
This model is a fine-tuned version of belisards/congretimbau on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1076
- Accuracy: 0.8503
- F1: 0.7896
- Recall: 0.7959
- Precision: 0.7839
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: 32
- eval_batch_size: 32
- 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: 200
- num_epochs: 18
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.1548 | 1.0 | 35 | 0.1456 | 0.7411 | 0.4571 | 0.5112 | 0.6227 |
| 0.1572 | 2.0 | 70 | 0.1354 | 0.7411 | 0.6588 | 0.6570 | 0.6607 |
| 0.1305 | 3.0 | 105 | 0.1212 | 0.7768 | 0.6402 | 0.6251 | 0.7194 |
| 0.1069 | 4.0 | 140 | 0.1155 | 0.8393 | 0.7857 | 0.7794 | 0.7930 |
| 0.0937 | 5.0 | 175 | 0.1216 | 0.8304 | 0.7764 | 0.7734 | 0.7798 |
| 0.0639 | 6.0 | 210 | 0.1257 | 0.8482 | 0.7899 | 0.7742 | 0.8125 |
| 0.0437 | 7.0 | 245 | 0.1610 | 0.8393 | 0.7614 | 0.7345 | 0.8195 |
| 0.0254 | 8.0 | 280 | 0.2101 | 0.8482 | 0.7842 | 0.7630 | 0.8197 |
| 0.0067 | 9.0 | 315 | 0.2555 | 0.8482 | 0.7899 | 0.7742 | 0.8125 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0