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--- |
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license: mit |
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base_model: neuralmind/bert-large-portuguese-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: final |
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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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# final |
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This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3507 |
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- Accuracy: 0.8945 |
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- F1: 0.8863 |
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- Recall: 0.8760 |
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- Precision: 0.8968 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 5151 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.485 | 0.9756 | 80 | 0.3916 | 0.8182 | 0.7984 | 0.7674 | 0.8319 | |
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| 0.3395 | 1.9512 | 160 | 0.3039 | 0.8764 | 0.8547 | 0.7752 | 0.9524 | |
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| 0.2139 | 2.9268 | 240 | 0.3122 | 0.8691 | 0.8548 | 0.8217 | 0.8908 | |
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| 0.084 | 3.9024 | 320 | 0.3507 | 0.8945 | 0.8863 | 0.8760 | 0.8968 | |
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| 0.058 | 4.8780 | 400 | 0.5087 | 0.8727 | 0.8571 | 0.8140 | 0.9052 | |
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| 0.0389 | 5.8537 | 480 | 0.4579 | 0.8982 | 0.888 | 0.8605 | 0.9174 | |
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| 0.0264 | 6.8293 | 560 | 0.5052 | 0.8873 | 0.8765 | 0.8527 | 0.9016 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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