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---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
model-index:
- name: albert-base-v2-2-contract-sections-classification-v4-10
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mvgdr/classificacao-secoes-contratos-v4-albert-base/runs/1ngfioq6)
# albert-base-v2-2-contract-sections-classification-v4-10
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8591
- Accuracy Evaluate: 0.7843
- Precision Evaluate: 0.8045
- Recall Evaluate: 0.7910
- F1 Evaluate: 0.7930
- Accuracy Sklearn: 0.7843
- Precision Sklearn: 0.7973
- Recall Sklearn: 0.7843
- F1 Sklearn: 0.7854
- Acuracia Rotulo Objeto: 0.8698
- Acuracia Rotulo Obrigacoes: 0.8670
- Acuracia Rotulo Valor: 0.6046
- Acuracia Rotulo Vigencia: 0.5984
- Acuracia Rotulo Rescisao: 0.7839
- Acuracia Rotulo Foro: 0.9
- Acuracia Rotulo Reajuste: 0.8185
- Acuracia Rotulo Fiscalizacao: 0.6656
- Acuracia Rotulo Publicacao: 0.8227
- Acuracia Rotulo Pagamento: 0.7717
- Acuracia Rotulo Casos Omissos: 0.8522
- Acuracia Rotulo Sancoes: 0.8716
- Acuracia Rotulo Dotacao Orcamentaria: 0.8571
## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Evaluate | Precision Evaluate | Recall Evaluate | F1 Evaluate | Accuracy Sklearn | Precision Sklearn | Recall Sklearn | F1 Sklearn | Acuracia Rotulo Objeto | Acuracia Rotulo Obrigacoes | Acuracia Rotulo Valor | Acuracia Rotulo Vigencia | Acuracia Rotulo Rescisao | Acuracia Rotulo Foro | Acuracia Rotulo Reajuste | Acuracia Rotulo Fiscalizacao | Acuracia Rotulo Publicacao | Acuracia Rotulo Pagamento | Acuracia Rotulo Casos Omissos | Acuracia Rotulo Sancoes | Acuracia Rotulo Dotacao Orcamentaria |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:------------------:|:---------------:|:-----------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------------:|:--------------------------:|:---------------------:|:------------------------:|:------------------------:|:--------------------:|:------------------------:|:----------------------------:|:--------------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------------------------:|
| 1.7695 | 1.0 | 1000 | 1.9528 | 0.4447 | 0.6693 | 0.3931 | 0.3947 | 0.4447 | 0.6282 | 0.4447 | 0.4028 | 0.9029 | 0.8148 | 0.4069 | 0.0577 | 0.2521 | 0.8692 | 0.2064 | 0.0789 | 0.5025 | 0.2283 | 0.4335 | 0.3303 | 0.0275 |
| 1.2261 | 2.0 | 2000 | 1.6110 | 0.5755 | 0.6694 | 0.5569 | 0.5614 | 0.5755 | 0.6584 | 0.5755 | 0.5645 | 0.8244 | 0.8013 | 0.4097 | 0.3963 | 0.3601 | 0.9077 | 0.6299 | 0.1956 | 0.6059 | 0.6051 | 0.5714 | 0.6514 | 0.2802 |
| 0.9134 | 3.0 | 3000 | 1.3841 | 0.6538 | 0.7003 | 0.6524 | 0.6478 | 0.6538 | 0.6991 | 0.6538 | 0.6473 | 0.8161 | 0.8182 | 0.4585 | 0.4724 | 0.4377 | 0.9038 | 0.7794 | 0.3218 | 0.7438 | 0.7428 | 0.7635 | 0.7339 | 0.4890 |
| 0.7022 | 4.0 | 4000 | 1.2144 | 0.6935 | 0.7370 | 0.6945 | 0.6965 | 0.6935 | 0.7265 | 0.6935 | 0.6904 | 0.8244 | 0.8333 | 0.4470 | 0.5407 | 0.6842 | 0.8923 | 0.8043 | 0.3596 | 0.7488 | 0.7210 | 0.6453 | 0.8349 | 0.6923 |
| 0.5597 | 5.0 | 5000 | 1.0738 | 0.7288 | 0.7560 | 0.7406 | 0.7386 | 0.7288 | 0.7494 | 0.7288 | 0.7286 | 0.8574 | 0.7845 | 0.5358 | 0.5171 | 0.7147 | 0.9 | 0.8292 | 0.5110 | 0.7783 | 0.7355 | 0.8128 | 0.8716 | 0.7802 |
| 0.4429 | 6.0 | 6000 | 0.9868 | 0.7552 | 0.7772 | 0.7641 | 0.7626 | 0.7552 | 0.7731 | 0.7552 | 0.7556 | 0.8678 | 0.8350 | 0.5874 | 0.5328 | 0.7368 | 0.8846 | 0.8399 | 0.5584 | 0.8227 | 0.7790 | 0.8276 | 0.8807 | 0.7802 |
| 0.389 | 7.0 | 7000 | 0.9236 | 0.7615 | 0.7823 | 0.7701 | 0.7683 | 0.7615 | 0.7763 | 0.7615 | 0.7603 | 0.8616 | 0.8620 | 0.6017 | 0.5538 | 0.7479 | 0.8962 | 0.8185 | 0.5142 | 0.8227 | 0.7681 | 0.8473 | 0.8716 | 0.8462 |
| 0.3341 | 8.0 | 8000 | 0.8949 | 0.776 | 0.7961 | 0.7833 | 0.7845 | 0.776 | 0.7900 | 0.776 | 0.7771 | 0.8781 | 0.8519 | 0.6017 | 0.5722 | 0.7729 | 0.8962 | 0.8078 | 0.6656 | 0.8227 | 0.7536 | 0.8424 | 0.8716 | 0.8462 |
| 0.3099 | 9.0 | 9000 | 0.8650 | 0.7805 | 0.8039 | 0.7876 | 0.7905 | 0.7805 | 0.7956 | 0.7805 | 0.7819 | 0.8822 | 0.8552 | 0.6160 | 0.5748 | 0.7756 | 0.8962 | 0.8114 | 0.6593 | 0.8227 | 0.7754 | 0.8522 | 0.8716 | 0.8462 |
| 0.3016 | 10.0 | 10000 | 0.8591 | 0.7843 | 0.8045 | 0.7910 | 0.7930 | 0.7843 | 0.7973 | 0.7843 | 0.7854 | 0.8698 | 0.8670 | 0.6046 | 0.5984 | 0.7839 | 0.9 | 0.8185 | 0.6656 | 0.8227 | 0.7717 | 0.8522 | 0.8716 | 0.8571 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0
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