| --- |
| language: |
| - pt |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - brazilian_court_decisions |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: bert-base-multilingual-cased-finetuned-lener_br-finetuned-lener-br |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: lener_br |
| type: lener_br |
| config: lener_br |
| split: train |
| args: lener_br |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0 |
| - name: Recall |
| type: recall |
| value: 0 |
| - name: F1 |
| type: f1 |
| value: 0 |
| - name: Accuracy |
| type: accuracy |
| value: 0 |
| --- |
| |
| <!-- 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. --> |
|
|
| # bert-base-multilingual-cased-finetuned-lener_br-finetuned-lener-br |
| |
| This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the lener_br dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: nan (To update) |
| - Precision: 0.9122 (To update) |
| - Recall: 0.9163 (To update) |
| - F1: 0.9142 (To update) |
| - Accuracy: 0.9826 (To update) |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters (To update) |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results (To update) |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.068 | 1.0 | 3914 | nan | 0.6196 | 0.8604 | 0.7204 | 0.9568 | |
| | 0.0767 | 2.0 | 7828 | nan | 0.8270 | 0.8710 | 0.8484 | 0.9693 | |
| | 0.0257 | 3.0 | 11742 | nan | 0.7243 | 0.9005 | 0.8029 | 0.9639 | |
| | 0.0193 | 4.0 | 15656 | nan | 0.9010 | 0.8984 | 0.8997 | 0.9821 | |
| | 0.0156 | 5.0 | 19570 | nan | 0.7150 | 0.9121 | 0.8016 | 0.9641 | |
| | 0.0165 | 6.0 | 23484 | nan | 0.7640 | 0.8796 | 0.8177 | 0.9691 | |
| | 0.0225 | 7.0 | 27398 | nan | 0.8851 | 0.9098 | 0.8973 | 0.9803 | |
| | 0.016 | 8.0 | 31312 | nan | 0.9081 | 0.9015 | 0.9048 | 0.9792 | |
| | 0.0078 | 9.0 | 35226 | nan | 0.8941 | 0.8863 | 0.8902 | 0.9788 | |
| | 0.0061 | 10.0 | 39140 | nan | 0.9026 | 0.9002 | 0.9014 | 0.9804 | |
| | 0.0057 | 11.0 | 43054 | nan | 0.8793 | 0.9018 | 0.8904 | 0.9769 | |
| | 0.0044 | 12.0 | 46968 | nan | 0.8790 | 0.9033 | 0.8910 | 0.9785 | |
| | 0.0043 | 13.0 | 50882 | nan | 0.9122 | 0.9163 | 0.9142 | 0.9826 | |
| | 0.0003 | 14.0 | 54796 | nan | 0.9032 | 0.9070 | 0.9051 | 0.9807 | |
| | 0.0025 | 15.0 | 58710 | nan | 0.8903 | 0.9085 | 0.8993 | 0.9798 | |
|
|
|
|
| ### Framework versions (To update) |
|
|
| - Transformers 4.23.1 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
|
|
