| --- |
| license: mit |
| base_model: neuralmind/bert-large-portuguese-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - recall |
| - precision |
| model-index: |
| - name: content |
| 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. --> |
|
|
| # content |
|
|
| This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4768 |
| - Accuracy: 0.7739 |
| - F1-score: 0.7823 |
| - Recall: 0.9002 |
| - Precision: 0.6917 |
|
|
| ## 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: 2.5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall | Precision | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:| |
| | 0.5016 | 0.3814 | 500 | 0.4686 | 0.7736 | 0.7865 | 0.9022 | 0.6971 | |
| | 0.4628 | 0.7628 | 1000 | 0.4437 | 0.7753 | 0.7769 | 0.8464 | 0.7180 | |
| | 0.4139 | 1.1442 | 1500 | 0.4633 | 0.7773 | 0.7573 | 0.7517 | 0.7630 | |
| | 0.3569 | 1.5256 | 2000 | 0.5019 | 0.7831 | 0.7930 | 0.8991 | 0.7093 | |
| | 0.357 | 1.9069 | 2500 | 0.4498 | 0.7839 | 0.7644 | 0.7585 | 0.7704 | |
| | 0.2612 | 2.2883 | 3000 | 0.6906 | 0.7665 | 0.7740 | 0.8650 | 0.7003 | |
| | 0.2292 | 2.6697 | 3500 | 0.6406 | 0.7624 | 0.7711 | 0.8656 | 0.6952 | |
| | 0.2345 | 3.0511 | 4000 | 0.8274 | 0.7687 | 0.7502 | 0.7511 | 0.7492 | |
| | 0.1527 | 3.4325 | 4500 | 0.8778 | 0.7602 | 0.7433 | 0.7511 | 0.7356 | |
| | 0.1613 | 3.8139 | 5000 | 0.8756 | 0.7564 | 0.7220 | 0.6842 | 0.7642 | |
| | 0.1188 | 4.1953 | 5500 | 1.2264 | 0.7567 | 0.7317 | 0.7176 | 0.7463 | |
| | 0.0992 | 4.5767 | 6000 | 1.2104 | 0.7636 | 0.7440 | 0.7430 | 0.7449 | |
| | 0.0938 | 4.9580 | 6500 | 1.1858 | 0.7616 | 0.7461 | 0.7579 | 0.7347 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.42.4 |
| - Pytorch 2.3.1+cu121 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
|
|
