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: 0.1877
- Accuracy: 0.7891
- F1: 0.7273
- Recall: 0.7564
- Precision: 0.7128
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: 120
- num_epochs: 18
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.29 | 1.0 | 35 | 0.2717 | 0.5625 | 0.5209 | 0.5478 | 0.5371 |
| 0.2615 | 2.0 | 70 | 0.2353 | 0.5357 | 0.5344 | 0.6643 | 0.6468 |
| 0.2189 | 3.0 | 105 | 0.1945 | 0.8036 | 0.7637 | 0.7889 | 0.7506 |
| 0.1579 | 4.0 | 140 | 0.1931 | 0.7857 | 0.7375 | 0.7545 | 0.7273 |
| 0.1078 | 5.0 | 175 | 0.2402 | 0.8036 | 0.7496 | 0.7553 | 0.7447 |
| 0.0596 | 6.0 | 210 | 0.2657 | 0.7946 | 0.7591 | 0.7941 | 0.7458 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0