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--- |
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language: |
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- pt |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lener_br |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model_index: |
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- name: bertimbau-base-lener_br |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: lener_br |
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type: lener_br |
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args: lener_br |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9692504609383333 |
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model-index: |
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- name: Luciano/bertimbau-base-lener_br |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: lener_br |
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type: lener_br |
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config: lener_br |
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split: test |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9824282794418222 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9877557596262284 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.9870401674313772 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9873978338768773 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.11542011797428131 |
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verified: true |
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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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# bertimbau-base-lener_br |
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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 lener_br dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2298 |
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- Precision: 0.8501 |
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- Recall: 0.9138 |
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- F1: 0.8808 |
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- Accuracy: 0.9693 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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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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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0686 | 1.0 | 1957 | 0.1399 | 0.7759 | 0.8669 | 0.8189 | 0.9641 | |
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| 0.0437 | 2.0 | 3914 | 0.1457 | 0.7997 | 0.8938 | 0.8441 | 0.9623 | |
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| 0.0313 | 3.0 | 5871 | 0.1675 | 0.8466 | 0.8744 | 0.8603 | 0.9651 | |
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| 0.0201 | 4.0 | 7828 | 0.1621 | 0.8713 | 0.8839 | 0.8775 | 0.9718 | |
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| 0.0137 | 5.0 | 9785 | 0.1811 | 0.7783 | 0.9159 | 0.8415 | 0.9645 | |
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| 0.0105 | 6.0 | 11742 | 0.1836 | 0.8568 | 0.9009 | 0.8783 | 0.9692 | |
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| 0.0105 | 7.0 | 13699 | 0.1649 | 0.8339 | 0.9125 | 0.8714 | 0.9725 | |
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| 0.0059 | 8.0 | 15656 | 0.2298 | 0.8501 | 0.9138 | 0.8808 | 0.9693 | |
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| 0.0051 | 9.0 | 17613 | 0.2210 | 0.8437 | 0.9045 | 0.8731 | 0.9693 | |
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| 0.0061 | 10.0 | 19570 | 0.2499 | 0.8627 | 0.8946 | 0.8784 | 0.9681 | |
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| 0.0041 | 11.0 | 21527 | 0.1985 | 0.8560 | 0.9052 | 0.8799 | 0.9720 | |
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| 0.003 | 12.0 | 23484 | 0.2204 | 0.8498 | 0.9065 | 0.8772 | 0.9699 | |
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| 0.0014 | 13.0 | 25441 | 0.2152 | 0.8425 | 0.9067 | 0.8734 | 0.9709 | |
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| 0.0005 | 14.0 | 27398 | 0.2317 | 0.8553 | 0.8987 | 0.8765 | 0.9705 | |
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| 0.0015 | 15.0 | 29355 | 0.2436 | 0.8543 | 0.8989 | 0.8760 | 0.9700 | |
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### Framework versions |
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- Transformers 4.8.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.9.0 |
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- Tokenizers 0.10.3 |
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