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---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-2-contract-sections-classification-v2
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-v2/runs/y6ofo8lp)
[<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-v2/runs/y6ofo8lp)
[<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-v2/runs/y6ofo8lp)
# distilbert-base-multilingual-cased-2-contract-sections-classification-v2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3290
- Accuracy: 0.964
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.0275 | 1.0 | 1000 | 0.1685 | 0.964 |
| 0.0281 | 2.0 | 2000 | 0.1838 | 0.9625 |
| 0.0262 | 3.0 | 3000 | 0.1768 | 0.9675 |
| 0.025 | 4.0 | 4000 | 0.1766 | 0.9643 |
| 0.0229 | 5.0 | 5000 | 0.2149 | 0.958 |
| 0.0125 | 6.0 | 6000 | 0.2050 | 0.9593 |
| 0.0157 | 7.0 | 7000 | 0.2108 | 0.9593 |
| 0.0168 | 8.0 | 8000 | 0.2029 | 0.9625 |
| 0.0118 | 9.0 | 9000 | 0.2118 | 0.9617 |
| 0.011 | 10.0 | 10000 | 0.2319 | 0.9593 |
| 0.0103 | 11.0 | 11000 | 0.2175 | 0.9615 |
| 0.0097 | 12.0 | 12000 | 0.2288 | 0.9625 |
| 0.0114 | 13.0 | 13000 | 0.2267 | 0.9617 |
| 0.0064 | 14.0 | 14000 | 0.2401 | 0.9605 |
| 0.0055 | 15.0 | 15000 | 0.2361 | 0.9607 |
| 0.0042 | 16.0 | 16000 | 0.2279 | 0.9633 |
| 0.005 | 17.0 | 17000 | 0.2537 | 0.96 |
| 0.0033 | 18.0 | 18000 | 0.2518 | 0.9613 |
| 0.0052 | 19.0 | 19000 | 0.2680 | 0.9583 |
| 0.0034 | 20.0 | 20000 | 0.2836 | 0.959 |
| 0.0027 | 21.0 | 21000 | 0.2599 | 0.961 |
| 0.0025 | 22.0 | 22000 | 0.2695 | 0.9587 |
| 0.0018 | 23.0 | 23000 | 0.2758 | 0.959 |
| 0.0029 | 24.0 | 24000 | 0.2826 | 0.9597 |
| 0.0032 | 25.0 | 25000 | 0.2645 | 0.9617 |
| 0.002 | 26.0 | 26000 | 0.2856 | 0.9597 |
| 0.0033 | 27.0 | 27000 | 0.2750 | 0.9595 |
| 0.0031 | 28.0 | 28000 | 0.2653 | 0.9607 |
| 0.0021 | 29.0 | 29000 | 0.2687 | 0.9623 |
| 0.004 | 30.0 | 30000 | 0.2878 | 0.9613 |
| 0.0027 | 31.0 | 31000 | 0.2778 | 0.9625 |
| 0.004 | 32.0 | 32000 | 0.2672 | 0.965 |
| 0.005 | 33.0 | 33000 | 0.2771 | 0.9647 |
| 0.0024 | 34.0 | 34000 | 0.2746 | 0.9663 |
| 0.0022 | 35.0 | 35000 | 0.3088 | 0.9595 |
| 0.0001 | 36.0 | 36000 | 0.2909 | 0.9615 |
| 0.0016 | 37.0 | 37000 | 0.2744 | 0.9645 |
| 0.0025 | 38.0 | 38000 | 0.3005 | 0.9607 |
| 0.0006 | 39.0 | 39000 | 0.3034 | 0.9607 |
| 0.0021 | 40.0 | 40000 | 0.3198 | 0.9607 |
| 0.0002 | 41.0 | 41000 | 0.3039 | 0.9607 |
| 0.0005 | 42.0 | 42000 | 0.3338 | 0.9585 |
| 0.001 | 43.0 | 43000 | 0.3179 | 0.96 |
| 0.0016 | 44.0 | 44000 | 0.2949 | 0.9633 |
| 0.0022 | 45.0 | 45000 | 0.3167 | 0.9597 |
| 0.0008 | 46.0 | 46000 | 0.3077 | 0.9605 |
| 0.0028 | 47.0 | 47000 | 0.3055 | 0.9615 |
| 0.0025 | 48.0 | 48000 | 0.2892 | 0.9643 |
| 0.0018 | 49.0 | 49000 | 0.3142 | 0.9597 |
| 0.0013 | 50.0 | 50000 | 0.3204 | 0.9617 |
| 0.003 | 51.0 | 51000 | 0.3505 | 0.9597 |
| 0.0003 | 52.0 | 52000 | 0.3168 | 0.963 |
| 0.0026 | 53.0 | 53000 | 0.3503 | 0.959 |
| 0.0019 | 54.0 | 54000 | 0.3374 | 0.9633 |
| 0.0006 | 55.0 | 55000 | 0.3449 | 0.96 |
| 0.0001 | 56.0 | 56000 | 0.3348 | 0.9627 |
| 0.0027 | 57.0 | 57000 | 0.3310 | 0.9613 |
| 0.0021 | 58.0 | 58000 | 0.3310 | 0.961 |
| 0.0005 | 59.0 | 59000 | 0.3136 | 0.963 |
| 0.0006 | 60.0 | 60000 | 0.3118 | 0.9637 |
| 0.0006 | 61.0 | 61000 | 0.3133 | 0.9613 |
| 0.0013 | 62.0 | 62000 | 0.3058 | 0.9643 |
| 0.0 | 63.0 | 63000 | 0.3053 | 0.964 |
| 0.0008 | 64.0 | 64000 | 0.3016 | 0.965 |
| 0.0008 | 65.0 | 65000 | 0.3109 | 0.9655 |
| 0.0011 | 66.0 | 66000 | 0.3061 | 0.9647 |
| 0.0 | 67.0 | 67000 | 0.3009 | 0.9665 |
| 0.0009 | 68.0 | 68000 | 0.3140 | 0.9643 |
| 0.0006 | 69.0 | 69000 | 0.3105 | 0.965 |
| 0.0007 | 70.0 | 70000 | 0.3120 | 0.9655 |
| 0.0 | 71.0 | 71000 | 0.3334 | 0.962 |
| 0.0018 | 72.0 | 72000 | 0.3361 | 0.9617 |
| 0.0011 | 73.0 | 73000 | 0.3240 | 0.963 |
| 0.0006 | 74.0 | 74000 | 0.3196 | 0.9637 |
| 0.002 | 75.0 | 75000 | 0.3077 | 0.966 |
| 0.0017 | 76.0 | 76000 | 0.3153 | 0.9633 |
| 0.001 | 77.0 | 77000 | 0.3217 | 0.963 |
| 0.001 | 78.0 | 78000 | 0.3192 | 0.965 |
| 0.0 | 79.0 | 79000 | 0.3188 | 0.9657 |
| 0.001 | 80.0 | 80000 | 0.3278 | 0.9627 |
| 0.0016 | 81.0 | 81000 | 0.3189 | 0.9625 |
| 0.0017 | 82.0 | 82000 | 0.3237 | 0.9625 |
| 0.001 | 83.0 | 83000 | 0.3195 | 0.9635 |
| 0.0014 | 84.0 | 84000 | 0.3301 | 0.9625 |
| 0.0 | 85.0 | 85000 | 0.3235 | 0.9635 |
| 0.0017 | 86.0 | 86000 | 0.3313 | 0.9627 |
| 0.0005 | 87.0 | 87000 | 0.3300 | 0.9625 |
| 0.0 | 88.0 | 88000 | 0.3241 | 0.964 |
| 0.0005 | 89.0 | 89000 | 0.3260 | 0.9637 |
| 0.0016 | 90.0 | 90000 | 0.3296 | 0.9643 |
| 0.0006 | 91.0 | 91000 | 0.3302 | 0.9637 |
| 0.0 | 92.0 | 92000 | 0.3283 | 0.964 |
| 0.0016 | 93.0 | 93000 | 0.3250 | 0.965 |
| 0.0007 | 94.0 | 94000 | 0.3260 | 0.9647 |
| 0.0006 | 95.0 | 95000 | 0.3294 | 0.9637 |
| 0.0009 | 96.0 | 96000 | 0.3278 | 0.9645 |
| 0.0016 | 97.0 | 97000 | 0.3277 | 0.9645 |
| 0.0006 | 98.0 | 98000 | 0.3287 | 0.964 |
| 0.0 | 99.0 | 99000 | 0.3290 | 0.964 |
| 0.0005 | 100.0 | 100000 | 0.3290 | 0.964 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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