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--- |
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library_name: transformers |
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license: mit |
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base_model: belisards/congretimbau |
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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: belisards/congretimbau |
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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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# belisards/congretimbau |
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This model is a fine-tuned version of [belisards/congretimbau](https://huggingface.co/belisards/congretimbau) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1076 |
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- Accuracy: 0.8503 |
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- F1: 0.7896 |
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- Recall: 0.7959 |
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- Precision: 0.7839 |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 5151 |
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- optimizer: Use 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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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 18 |
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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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| 0.1548 | 1.0 | 35 | 0.1456 | 0.7411 | 0.4571 | 0.5112 | 0.6227 | |
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| 0.1572 | 2.0 | 70 | 0.1354 | 0.7411 | 0.6588 | 0.6570 | 0.6607 | |
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| 0.1305 | 3.0 | 105 | 0.1212 | 0.7768 | 0.6402 | 0.6251 | 0.7194 | |
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| 0.1069 | 4.0 | 140 | 0.1155 | 0.8393 | 0.7857 | 0.7794 | 0.7930 | |
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| 0.0937 | 5.0 | 175 | 0.1216 | 0.8304 | 0.7764 | 0.7734 | 0.7798 | |
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| 0.0639 | 6.0 | 210 | 0.1257 | 0.8482 | 0.7899 | 0.7742 | 0.8125 | |
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| 0.0437 | 7.0 | 245 | 0.1610 | 0.8393 | 0.7614 | 0.7345 | 0.8195 | |
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| 0.0254 | 8.0 | 280 | 0.2101 | 0.8482 | 0.7842 | 0.7630 | 0.8197 | |
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| 0.0067 | 9.0 | 315 | 0.2555 | 0.8482 | 0.7899 | 0.7742 | 0.8125 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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