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: 0.2121
- Accuracy: 0.7823
- F1: 0.7252
- Recall: 0.7628
- Precision: 0.7103
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 |
|---|---|---|---|---|---|---|---|
| 0.2495 | 1.0 | 35 | 0.3182 | 0.7143 | 0.5311 | 0.5380 | 0.5711 |
| 0.2596 | 2.0 | 70 | 0.2572 | 0.4911 | 0.4818 | 0.5557 | 0.5448 |
| 0.2321 | 3.0 | 105 | 0.2390 | 0.7232 | 0.6754 | 0.7011 | 0.6681 |
| 0.1769 | 4.0 | 140 | 0.2265 | 0.7054 | 0.6773 | 0.7339 | 0.6816 |
| 0.1614 | 5.0 | 175 | 0.2461 | 0.7054 | 0.6735 | 0.7227 | 0.6745 |
| 0.1027 | 6.0 | 210 | 0.2762 | 0.8125 | 0.7764 | 0.8062 | 0.7621 |
| 0.0832 | 7.0 | 245 | 0.3463 | 0.8036 | 0.7441 | 0.7441 | 0.7441 |
| 0.0354 | 8.0 | 280 | 0.6084 | 0.8214 | 0.7673 | 0.7673 | 0.7673 |
| 0.0068 | 9.0 | 315 | 0.6917 | 0.8214 | 0.7673 | 0.7673 | 0.7673 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0