metadata
library_name: transformers
license: mit
base_model: belisards/congretimbau
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: MyDrive
results: []
MyDrive
This model is a fine-tuned version of belisards/congretimbau on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1336
- Accuracy: 0.8776
- F1: 0.8115
- Recall: 0.7919
- Precision: 0.8389
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: 14
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.1343 | 2.8333 | 51 | 0.1396 | 0.7679 | 0.5492 | 0.5629 | 0.7832 |
| 0.1057 | 5.6667 | 102 | 0.1280 | 0.8036 | 0.6777 | 0.6543 | 0.7887 |
| 0.053 | 8.5 | 153 | 0.1457 | 0.8482 | 0.7899 | 0.7742 | 0.8125 |
| 0.0159 | 11.3333 | 204 | 0.2345 | 0.8482 | 0.7952 | 0.7854 | 0.8072 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0