| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: eng_spa_seq2seq |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # eng_spa_seq2seq |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0656 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 1 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:-----:|:---------------:| |
| | 0.2281 | 0.032 | 500 | 0.2171 | |
| | 0.194 | 0.064 | 1000 | 0.1832 | |
| | 0.1684 | 0.096 | 1500 | 0.1612 | |
| | 0.1583 | 0.128 | 2000 | 0.1476 | |
| | 0.1451 | 0.16 | 2500 | 0.1344 | |
| | 0.1371 | 0.192 | 3000 | 0.1238 | |
| | 0.1286 | 0.224 | 3500 | 0.1164 | |
| | 0.1231 | 0.256 | 4000 | 0.1099 | |
| | 0.1191 | 0.288 | 4500 | 0.1048 | |
| | 0.1119 | 0.32 | 5000 | 0.0997 | |
| | 0.1072 | 0.352 | 5500 | 0.0956 | |
| | 0.1073 | 0.384 | 6000 | 0.0917 | |
| | 0.0961 | 0.416 | 6500 | 0.0887 | |
| | 0.0983 | 0.448 | 7000 | 0.0865 | |
| | 0.0942 | 0.48 | 7500 | 0.0834 | |
| | 0.0921 | 0.512 | 8000 | 0.0814 | |
| | 0.0901 | 0.544 | 8500 | 0.0792 | |
| | 0.0853 | 0.576 | 9000 | 0.0771 | |
| | 0.0846 | 0.608 | 9500 | 0.0761 | |
| | 0.0823 | 0.64 | 10000 | 0.0739 | |
| | 0.0823 | 0.672 | 10500 | 0.0727 | |
| | 0.0824 | 0.704 | 11000 | 0.0717 | |
| | 0.081 | 0.736 | 11500 | 0.0709 | |
| | 0.079 | 0.768 | 12000 | 0.0695 | |
| | 0.0777 | 0.8 | 12500 | 0.0686 | |
| | 0.0759 | 0.832 | 13000 | 0.0676 | |
| | 0.0769 | 0.864 | 13500 | 0.0672 | |
| | 0.0781 | 0.896 | 14000 | 0.0666 | |
| | 0.0747 | 0.928 | 14500 | 0.0662 | |
| | 0.0757 | 0.96 | 15000 | 0.0658 | |
| | 0.0783 | 0.992 | 15500 | 0.0656 | |
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| ### Framework versions |
|
|
| - Transformers 4.46.3 |
| - Pytorch 2.5.1+cu121 |
| - Tokenizers 0.20.3 |
|
|
