|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: belisards/congretimbau |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- recall |
|
- precision |
|
model-index: |
|
- name: modelos |
|
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. --> |
|
|
|
# modelos |
|
|
|
This model is a fine-tuned version of [belisards/congretimbau](https://huggingface.co/belisards/congretimbau) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5239 |
|
- Accuracy: 0.8254 |
|
- F1: 0.7442 |
|
- Recall: 0.7267 |
|
- Precision: 0.7727 |
|
|
|
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 100 |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
|
| 0.5681 | 1.0323 | 32 | 0.5508 | 0.75 | 0.4286 | 0.5 | 0.375 | |
|
| 0.5233 | 2.0645 | 64 | 0.5138 | 0.7381 | 0.5146 | 0.5317 | 0.5897 | |
|
| 0.4339 | 3.0968 | 96 | 0.4529 | 0.7917 | 0.6875 | 0.6706 | 0.7240 | |
|
| 0.3907 | 4.1290 | 128 | 0.4087 | 0.8393 | 0.7683 | 0.75 | 0.7970 | |
|
| 0.2166 | 5.1613 | 160 | 0.4054 | 0.8452 | 0.7867 | 0.7778 | 0.7976 | |
|
| 0.14 | 6.1935 | 192 | 0.4474 | 0.8274 | 0.7716 | 0.7738 | 0.7696 | |
|
| 0.0673 | 7.2258 | 224 | 0.5118 | 0.8393 | 0.7726 | 0.7579 | 0.7932 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.45.2 |
|
- Pytorch 2.4.1+cu121 |
|
- Datasets 3.0.1 |
|
- Tokenizers 0.20.1 |
|
|
