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library_name: transformers
license: apache-2.0
base_model: vgaraujov/bart-base-spanish
tags:
- generated_from_trainer
model-index:
- name: barto_exp1_10partition_modelo_msl6000
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. -->
# barto_exp1_10partition_modelo_msl6000
This model is a fine-tuned version of [vgaraujov/bart-base-spanish](https://huggingface.co/vgaraujov/bart-base-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9532
- Model Preparation Time: 0.0034
- Bleu Msl: 0
- Bleu 1 Msl: 0.7639
- Bleu 2 Msl: 0.7149
- Bleu 3 Msl: 0.6525
- Bleu 4 Msl: 0.5443
- Ter Msl: 28.3847
- Bleu Asl: 0
- Bleu 1 Asl: 0.9293
- Bleu 2 Asl: 0.8946
- Bleu 3 Asl: 0.8600
- Bleu 4 Asl: 0.8197
- Ter Asl: 9.1654
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Bleu Msl | Bleu 1 Msl | Bleu 2 Msl | Bleu 3 Msl | Bleu 4 Msl | Ter Msl | Bleu Asl | Bleu 1 Asl | Bleu 2 Asl | Bleu 3 Asl | Bleu 4 Asl | Ter Asl |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|:----------:|:----------:|:----------:|:----------:|:-------:|:--------:|:----------:|:----------:|:----------:|:----------:|:-------:|
| No log | 1.0 | 150 | 1.2803 | 0.0034 | 0 | 0.6793 | 0.5456 | 0.3942 | 0.2008 | 47.0772 | 0 | 0.8508 | 0.7752 | 0.7072 | 0.6397 | 18.5776 |
| No log | 2.0 | 300 | 1.0222 | 0.0034 | 0 | 0.7352 | 0.6454 | 0.5466 | 0.4151 | 42.4843 | 0 | 0.8848 | 0.8238 | 0.7679 | 0.7111 | 14.2235 |
| No log | 3.0 | 450 | 0.9065 | 0.0034 | 0 | 0.7557 | 0.6691 | 0.5770 | 0.4421 | 37.6827 | 0 | 0.9055 | 0.8516 | 0.8027 | 0.7522 | 11.9013 |
| 1.7579 | 4.0 | 600 | 0.9370 | 0.0034 | 0 | 0.7106 | 0.6262 | 0.5307 | 0.3905 | 43.8413 | 0 | 0.9287 | 0.8872 | 0.8472 | 0.8047 | 9.0711 |
| 1.7579 | 5.0 | 750 | 0.8798 | 0.0034 | 0 | 0.744 | 0.6483 | 0.5455 | 0.3693 | 37.6827 | 0 | 0.9301 | 0.8896 | 0.8513 | 0.8104 | 8.8534 |
| 1.7579 | 6.0 | 900 | 0.8268 | 0.0034 | 0 | 0.7777 | 0.6941 | 0.5919 | 0.4367 | 32.2547 | 0 | 0.9348 | 0.8988 | 0.8631 | 0.8230 | 8.5631 |
| 0.2488 | 7.0 | 1050 | 0.8773 | 0.0034 | 0 | 0.7458 | 0.6463 | 0.5394 | 0.3955 | 39.7704 | 0 | 0.9305 | 0.8931 | 0.8572 | 0.8183 | 8.7808 |
| 0.2488 | 8.0 | 1200 | 0.8616 | 0.0034 | 0 | 0.7766 | 0.6871 | 0.5825 | 0.4373 | 33.9248 | 0 | 0.9417 | 0.9095 | 0.8787 | 0.8464 | 7.2569 |
| 0.2488 | 9.0 | 1350 | 0.8660 | 0.0034 | 0 | 0.7496 | 0.6521 | 0.5392 | 0.3802 | 39.3528 | 0 | 0.9429 | 0.9116 | 0.8811 | 0.8464 | 7.1118 |
| 0.1035 | 10.0 | 1500 | 0.9077 | 0.0034 | 0 | 0.7380 | 0.6450 | 0.5310 | 0.3551 | 39.8747 | 0 | 0.9427 | 0.9122 | 0.8840 | 0.8534 | 7.4746 |
| 0.1035 | 11.0 | 1650 | 0.9008 | 0.0034 | 0 | 0.6960 | 0.6035 | 0.5006 | 0.3589 | 40.7098 | 0 | 0.9427 | 0.9110 | 0.8808 | 0.8483 | 7.5472 |
| 0.1035 | 12.0 | 1800 | 0.9089 | 0.0034 | 0 | 0.7222 | 0.6306 | 0.5310 | 0.3713 | 40.7098 | 0 | 0.9422 | 0.9117 | 0.8820 | 0.8496 | 7.6197 |
| 0.1035 | 13.0 | 1950 | 0.8632 | 0.0034 | 0 | 0.7502 | 0.6575 | 0.5511 | 0.4091 | 38.5177 | 0 | 0.9416 | 0.9090 | 0.8776 | 0.8423 | 7.6923 |
| 0.0495 | 14.0 | 2100 | 0.9132 | 0.0034 | 0 | 0.6990 | 0.5972 | 0.4719 | 0.3275 | 45.6159 | 0 | 0.9326 | 0.9003 | 0.8683 | 0.8325 | 8.5631 |
| 0.0495 | 15.0 | 2250 | 0.8947 | 0.0034 | 0 | 0.6974 | 0.6036 | 0.4808 | 0.3461 | 44.1545 | 0 | 0.9409 | 0.9097 | 0.8803 | 0.8484 | 7.9826 |
| 0.0495 | 16.0 | 2400 | 0.9675 | 0.0034 | 0 | 0.6752 | 0.5754 | 0.4583 | 0.3035 | 47.7035 | 0 | 0.9398 | 0.9100 | 0.8812 | 0.8490 | 7.8374 |
| 0.0279 | 17.0 | 2550 | 0.8933 | 0.0034 | 0 | 0.6144 | 0.5142 | 0.4073 | 0.2809 | 52.0877 | 0 | 0.9386 | 0.9080 | 0.8788 | 0.8462 | 7.6923 |
| 0.0279 | 18.0 | 2700 | 0.9261 | 0.0034 | 0 | 0.6965 | 0.5974 | 0.4745 | 0.3043 | 45.3027 | 0 | 0.9356 | 0.9043 | 0.8748 | 0.8428 | 8.1277 |
| 0.0279 | 19.0 | 2850 | 0.9349 | 0.0034 | 0 | 0.6877 | 0.5828 | 0.4633 | 0.3246 | 47.2860 | 0 | 0.9422 | 0.9120 | 0.8831 | 0.8524 | 7.5472 |
| 0.0179 | 20.0 | 3000 | 0.9240 | 0.0034 | 0 | 0.7345 | 0.6479 | 0.5332 | 0.3636 | 40.6054 | 0 | 0.9418 | 0.9131 | 0.8855 | 0.8546 | 7.2569 |
| 0.0179 | 21.0 | 3150 | 0.9256 | 0.0034 | 0 | 0.7046 | 0.5958 | 0.4713 | 0.3168 | 44.0501 | 0 | 0.9434 | 0.9137 | 0.8849 | 0.8531 | 7.2569 |
| 0.0179 | 22.0 | 3300 | 0.9344 | 0.0034 | 0 | 0.7389 | 0.6511 | 0.5386 | 0.3806 | 37.8914 | 0 | 0.9371 | 0.9054 | 0.8743 | 0.8406 | 8.0552 |
| 0.0179 | 23.0 | 3450 | 0.9214 | 0.0034 | 0 | 0.7286 | 0.6367 | 0.5203 | 0.3562 | 42.5887 | 0 | 0.9389 | 0.9089 | 0.8807 | 0.8493 | 7.7649 |
| 0.0111 | 24.0 | 3600 | 0.9181 | 0.0034 | 0 | 0.7315 | 0.6346 | 0.5186 | 0.3594 | 40.3967 | 0 | 0.9400 | 0.9101 | 0.8810 | 0.8494 | 7.7649 |
| 0.0111 | 25.0 | 3750 | 0.8888 | 0.0034 | 0 | 0.7639 | 0.6738 | 0.5603 | 0.3930 | 38.9353 | 0 | 0.9477 | 0.9188 | 0.8904 | 0.8584 | 6.6763 |
| 0.0111 | 26.0 | 3900 | 0.9291 | 0.0034 | 0 | 0.6978 | 0.5936 | 0.4724 | 0.3117 | 45.0939 | 0 | 0.9370 | 0.9055 | 0.8757 | 0.8434 | 8.0552 |
| 0.0093 | 27.0 | 4050 | 0.9178 | 0.0034 | 0 | 0.7327 | 0.6379 | 0.5191 | 0.3538 | 41.3361 | 0 | 0.9405 | 0.9109 | 0.8833 | 0.8531 | 7.5472 |
| 0.0093 | 28.0 | 4200 | 0.9152 | 0.0034 | 0 | 0.7329 | 0.6373 | 0.5154 | 0.3502 | 41.7537 | 0 | 0.9458 | 0.9166 | 0.8893 | 0.8591 | 7.1843 |
| 0.0093 | 29.0 | 4350 | 0.9204 | 0.0034 | 0 | 0.7399 | 0.6449 | 0.5254 | 0.3587 | 41.1273 | 0 | 0.9418 | 0.9124 | 0.8844 | 0.8535 | 7.4746 |
| 0.0066 | 30.0 | 4500 | 0.9122 | 0.0034 | 0 | 0.7397 | 0.6463 | 0.5293 | 0.3629 | 41.0230 | 0 | 0.9465 | 0.9179 | 0.8902 | 0.8600 | 6.8940 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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