---
library_name: transformers
tags:
- generated_from_trainer
- transformers
- bert
- text-classification
model-index:
- name: ModernBERT-base-2-contract-sections-classification-v4-50-max
results: []
license: apache-2.0
datasets:
- marcelovidigal/contract-sections-with-labels-for-text-classification-v4
language:
- pt
base_model:
- answerdotai/ModernBERT-base
---
[
](https://wandb.ai/mvgdr/classificacao-secoes-contratos-v4-modernbert-base/runs/oph1v3zp)
[](https://wandb.ai/mvgdr/classificacao-secoes-contratos-v4-modernbert-base/runs/yh87i0fl)
# ModernBERT-base-2-contract-sections-classification-v4-50-max
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4092
- Accuracy Evaluate: 0.9244
- Precision Evaluate: 0.9280
- Recall Evaluate: 0.9266
- F1 Evaluate: 0.9265
- Accuracy Sklearn: 0.9244
- Precision Sklearn: 0.9252
- Recall Sklearn: 0.9244
- F1 Sklearn: 0.9239
- Acuracia Rotulo Objeto: 0.9563
- Acuracia Rotulo Obrigacoes: 0.9496
- Acuracia Rotulo Valor: 0.8311
- Acuracia Rotulo Vigencia: 0.9792
- Acuracia Rotulo Rescisao: 0.9441
- Acuracia Rotulo Foro: 0.9048
- Acuracia Rotulo Reajuste: 0.8922
- Acuracia Rotulo Fiscalizacao: 0.8485
- Acuracia Rotulo Publicacao: 0.9885
- Acuracia Rotulo Pagamento: 0.8829
- Acuracia Rotulo Casos Omissos: 0.9103
- Acuracia Rotulo Sancoes: 0.9722
- Acuracia Rotulo Dotacao Orcamentaria: 0.9863
## 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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:------------------:|:---------------:|:-----------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------------:|:--------------------------:|:---------------------:|:------------------------:|:------------------------:|:--------------------:|:------------------------:|:----------------------------:|:--------------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------------------------:|
| No log | 1.0 | 250 | 0.4141 | 0.9231 | 0.9252 | 0.9261 | 0.9254 | 0.9231 | 0.9231 | 0.9231 | 0.9229 | 0.9454 | 0.9419 | 0.8514 | 0.9792 | 0.9371 | 0.9048 | 0.8824 | 0.8409 | 1.0 | 0.9009 | 0.8974 | 0.9722 | 0.9863 |
| 0.357 | 2.0 | 500 | 0.4528 | 0.9194 | 0.9218 | 0.9253 | 0.9228 | 0.9194 | 0.9207 | 0.9194 | 0.9192 | 0.9508 | 0.9147 | 0.8311 | 0.9792 | 0.9371 | 0.9048 | 0.9020 | 0.8485 | 1.0 | 0.8919 | 0.9103 | 0.9722 | 0.9863 |
| 0.357 | 3.0 | 750 | 0.4269 | 0.925 | 0.9312 | 0.9270 | 0.9282 | 0.925 | 0.9262 | 0.925 | 0.9246 | 0.9617 | 0.9574 | 0.8243 | 0.9653 | 0.9441 | 0.9238 | 0.8824 | 0.8409 | 1.0 | 0.8829 | 0.9103 | 0.9722 | 0.9863 |
| 0.2319 | 4.0 | 1000 | 0.4197 | 0.9244 | 0.9283 | 0.9269 | 0.9267 | 0.9244 | 0.9252 | 0.9244 | 0.9238 | 0.9672 | 0.9457 | 0.8311 | 0.9792 | 0.9371 | 0.9048 | 0.8922 | 0.8409 | 1.0 | 0.8829 | 0.9103 | 0.9722 | 0.9863 |
| 0.2319 | 5.0 | 1250 | 0.4375 | 0.92 | 0.9191 | 0.9246 | 0.9209 | 0.92 | 0.9213 | 0.92 | 0.9198 | 0.9617 | 0.9225 | 0.8311 | 0.9722 | 0.9301 | 0.9048 | 0.8922 | 0.8561 | 0.9885 | 0.8919 | 0.9103 | 0.9722 | 0.9863 |
| 0.1568 | 6.0 | 1500 | 0.4203 | 0.9225 | 0.9232 | 0.9259 | 0.9239 | 0.9225 | 0.9232 | 0.9225 | 0.9222 | 0.9563 | 0.9380 | 0.8311 | 0.9792 | 0.9371 | 0.9048 | 0.8922 | 0.8485 | 0.9885 | 0.8919 | 0.9103 | 0.9722 | 0.9863 |
| 0.1568 | 7.0 | 1750 | 0.4056 | 0.9275 | 0.9294 | 0.9300 | 0.9289 | 0.9275 | 0.9280 | 0.9275 | 0.9270 | 0.9563 | 0.9419 | 0.8446 | 0.9861 | 0.9650 | 0.9048 | 0.8922 | 0.8409 | 0.9885 | 0.9009 | 0.9103 | 0.9722 | 0.9863 |
| 0.1107 | 8.0 | 2000 | 0.4097 | 0.9263 | 0.9289 | 0.9282 | 0.9278 | 0.9263 | 0.9267 | 0.9263 | 0.9257 | 0.9563 | 0.9535 | 0.8378 | 0.9861 | 0.9371 | 0.9143 | 0.8824 | 0.8409 | 0.9885 | 0.9009 | 0.9103 | 0.9722 | 0.9863 |
| 0.1107 | 9.0 | 2250 | 0.4176 | 0.9237 | 0.9266 | 0.9265 | 0.9259 | 0.9237 | 0.9244 | 0.9237 | 0.9233 | 0.9563 | 0.9457 | 0.8311 | 0.9792 | 0.9371 | 0.9048 | 0.9020 | 0.8485 | 0.9885 | 0.8829 | 0.9103 | 0.9722 | 0.9863 |
| 0.0923 | 10.0 | 2500 | 0.4092 | 0.9244 | 0.9280 | 0.9266 | 0.9265 | 0.9244 | 0.9252 | 0.9244 | 0.9239 | 0.9563 | 0.9496 | 0.8311 | 0.9792 | 0.9441 | 0.9048 | 0.8922 | 0.8485 | 0.9885 | 0.8829 | 0.9103 | 0.9722 | 0.9863 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0