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--- |
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library_name: transformers |
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license: mit |
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base_model: neuralmind/bert-base-portuguese-cased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bbau-semeval25_fold2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bbau-semeval25_fold2 |
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This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4474 |
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- Precision Samples: 1.0 |
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- Recall Samples: 0.0 |
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- F1 Samples: 0.0 |
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- Precision Macro: 1.0 |
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- Recall Macro: 0.3636 |
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- F1 Macro: 0.3636 |
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- Precision Micro: 1.0 |
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- Recall Micro: 0.0 |
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- F1 Micro: 0.0 |
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- Precision Weighted: 1.0 |
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- Recall Weighted: 0.0 |
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- F1 Weighted: 0.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| |
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| No log | 1.0 | 5 | 0.6293 | 0.0783 | 0.3868 | 0.1220 | 0.4983 | 0.5865 | 0.3159 | 0.0751 | 0.375 | 0.1252 | 0.3532 | 0.375 | 0.1464 | |
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| 0.6408 | 2.0 | 10 | 0.5789 | 0.0787 | 0.2286 | 0.1079 | 0.7311 | 0.4717 | 0.3440 | 0.0839 | 0.2054 | 0.1192 | 0.5702 | 0.2054 | 0.0796 | |
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| 0.6408 | 3.0 | 15 | 0.5425 | 0.0708 | 0.0583 | 0.0554 | 0.9220 | 0.3953 | 0.3740 | 0.0706 | 0.0536 | 0.0609 | 0.8686 | 0.0536 | 0.0258 | |
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| 0.552 | 4.0 | 20 | 0.5135 | 0.1125 | 0.0271 | 0.0396 | 0.9759 | 0.3864 | 0.3719 | 0.0952 | 0.0357 | 0.0519 | 0.9634 | 0.0357 | 0.0110 | |
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| 0.552 | 5.0 | 25 | 0.4912 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3636 | 0.3636 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | |
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| 0.5007 | 6.0 | 30 | 0.4745 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3636 | 0.3636 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | |
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| 0.5007 | 7.0 | 35 | 0.4624 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3636 | 0.3636 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | |
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| 0.4713 | 8.0 | 40 | 0.4543 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3636 | 0.3636 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | |
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| 0.4713 | 9.0 | 45 | 0.4493 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3636 | 0.3636 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | |
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| 0.4567 | 10.0 | 50 | 0.4474 | 1.0 | 0.0 | 0.0 | 1.0 | 0.3636 | 0.3636 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.3.1 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.1 |
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