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--- |
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license: mit |
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base_model: PORTULAN/albertina-900m-portuguese-ptpt-encoder |
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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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- precision |
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- recall |
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model-index: |
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- name: LVI_albertina-900m-portuguese-ptpt-encoder |
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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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# LVI_albertina-900m-portuguese-ptpt-encoder |
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This model is a fine-tuned version of [PORTULAN/albertina-900m-portuguese-ptpt-encoder](https://huggingface.co/PORTULAN/albertina-900m-portuguese-ptpt-encoder) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1651 |
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- Accuracy: 0.9832 |
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- F1: 0.9833 |
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- Precision: 0.9779 |
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- Recall: 0.9889 |
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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: 5e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1273 | 1.0 | 12866 | 0.1718 | 0.9676 | 0.9668 | 0.9914 | 0.9434 | |
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| 0.1034 | 2.0 | 25732 | 0.1283 | 0.9814 | 0.9813 | 0.9844 | 0.9782 | |
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| 0.0292 | 3.0 | 38598 | 0.1219 | 0.9850 | 0.9850 | 0.9828 | 0.9872 | |
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| 0.0251 | 4.0 | 51464 | 0.1203 | 0.9857 | 0.9856 | 0.9901 | 0.9812 | |
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| 0.013 | 5.0 | 64330 | 0.1240 | 0.9837 | 0.9836 | 0.9896 | 0.9777 | |
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| 0.0237 | 6.0 | 77196 | 0.1294 | 0.9848 | 0.9849 | 0.9809 | 0.9889 | |
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| 0.0153 | 7.0 | 90062 | 0.1651 | 0.9832 | 0.9833 | 0.9779 | 0.9889 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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