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--- |
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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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datasets: |
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- glue-ptpt |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: paraphrase-bert-portuguese |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: glue-ptpt |
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type: glue-ptpt |
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config: mrpc |
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split: validation |
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args: mrpc |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8676470588235294 |
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- name: F1 |
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type: f1 |
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value: 0.9028776978417268 |
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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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# paraphrase-bert-portuguese |
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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 glue-ptpt dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2267 |
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- Accuracy: 0.8676 |
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- F1: 0.9029 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 1.0 | 459 | 0.7241 | 0.8603 | 0.9012 | |
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| 0.0658 | 2.0 | 918 | 0.7902 | 0.8725 | 0.9071 | |
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| 0.1499 | 3.0 | 1377 | 0.7895 | 0.8676 | 0.9022 | |
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| 0.0654 | 4.0 | 1836 | 0.9841 | 0.8676 | 0.9036 | |
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| 0.018 | 5.0 | 2295 | 1.0520 | 0.8627 | 0.8989 | |
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| 0.0144 | 6.0 | 2754 | 1.1002 | 0.8725 | 0.9081 | |
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| 0.007 | 7.0 | 3213 | 1.1303 | 0.8652 | 0.9005 | |
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| 0.0056 | 8.0 | 3672 | 1.2298 | 0.8725 | 0.9081 | |
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| 0.0019 | 9.0 | 4131 | 1.2353 | 0.8701 | 0.9038 | |
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| 0.0001 | 10.0 | 4590 | 1.2267 | 0.8676 | 0.9029 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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