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--- |
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license: mit |
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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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model-index: |
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- name: bertimbau-base-finetuned-lener-br-finetuned-brazilian_court_decisions |
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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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# bertimbau-base-finetuned-lener-br-finetuned-brazilian_court_decisions |
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This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8017 |
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- Accuracy: 0.7698 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 405 | 0.7790 | 0.6535 | |
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| 0.8276 | 2.0 | 810 | 0.6739 | 0.7277 | |
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| 0.5818 | 3.0 | 1215 | 0.8767 | 0.7302 | |
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| 0.4147 | 4.0 | 1620 | 0.8229 | 0.7896 | |
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| 0.287 | 5.0 | 2025 | 0.9874 | 0.7921 | |
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| 0.287 | 6.0 | 2430 | 1.2301 | 0.7772 | |
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| 0.1727 | 7.0 | 2835 | 1.2864 | 0.7946 | |
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| 0.1179 | 8.0 | 3240 | 1.5097 | 0.7772 | |
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| 0.0709 | 9.0 | 3645 | 1.4772 | 0.7921 | |
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| 0.0437 | 10.0 | 4050 | 1.5581 | 0.7797 | |
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| 0.0437 | 11.0 | 4455 | 1.6317 | 0.7896 | |
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| 0.0318 | 12.0 | 4860 | 1.7295 | 0.7822 | |
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| 0.0158 | 13.0 | 5265 | 1.7333 | 0.7797 | |
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| 0.0108 | 14.0 | 5670 | 1.8008 | 0.7772 | |
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| 0.0137 | 15.0 | 6075 | 1.8017 | 0.7698 | |
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### Framework versions |
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- Transformers 4.22.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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