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---
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-es-es
tags:
- generated_from_trainer
model-index:
- name: '5661'
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. -->
# 5661
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co./Helsinki-NLP/opus-mt-es-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5476
- Model Preparation Time: 0.0034
- Bleu Msl: 0.0
- Bleu 1 Msl: 0.6746
- Bleu 2 Msl: 0.0151
- Bleu 3 Msl: 0.0045
- Bleu 4 Msl: 0.0023
- Ter Msl: 100
- Bleu Asl: 0
- Bleu 1 Asl: 0
- Bleu 2 Asl: 0
- Bleu 3 Asl: 0
- Bleu 4 Asl: 0
- Ter Asl: 100
## 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 | 142 | 0.3764 | 0.0034 | 35.3553 | 0.5627 | 0.0104 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4152 | 0.0083 | 0.0024 | 0.0012 | 100 |
| No log | 2.0 | 284 | 0.3592 | 0.0034 | 37.9918 | 0.5665 | 0.0104 | 0.0029 | 0.0014 | 100 | 53.7285 | 0.3970 | 0.0081 | 0.0024 | 0.0012 | 100 |
| No log | 3.0 | 426 | 0.3690 | 0.0034 | 50.0000 | 0.5627 | 0.0104 | 0.0029 | 0.0014 | 100 | 32.1729 | 0.3427 | 0.0075 | 0.0022 | 0.0011 | 100 |
| 0.019 | 4.0 | 568 | 0.3660 | 0.0034 | 50.0000 | 0.5875 | 0.0106 | 0.0029 | 0.0014 | 100 | 37.9918 | 0.4135 | 0.0083 | 0.0024 | 0.0012 | 100 |
| 0.019 | 5.0 | 710 | 0.3824 | 0.0034 | 50.0000 | 0.6350 | 0.0110 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.4465 | 0.0086 | 0.0024 | 0.0012 | 100 |
| 0.019 | 6.0 | 852 | 0.3574 | 0.0034 | 50.0000 | 0.5019 | 0.0098 | 0.0028 | 0.0014 | 100 | 42.7287 | 0.4217 | 0.0083 | 0.0024 | 0.0012 | 100 |
| 0.019 | 7.0 | 994 | 0.3810 | 0.0034 | 0.0 | 0.5361 | 0.0101 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4300 | 0.0084 | 0.0024 | 0.0012 | 100 |
| 0.0188 | 8.0 | 1136 | 0.3659 | 0.0034 | 50.0000 | 0.5 | 0.0098 | 0.0028 | 0.0014 | 100 | 100.0000 | 0.3904 | 0.0080 | 0.0023 | 0.0012 | 100 |
| 0.0188 | 9.0 | 1278 | 0.3696 | 0.0034 | 50.0000 | 0.6236 | 0.0109 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.4778 | 0.0089 | 0.0025 | 0.0012 | 100 |
| 0.0188 | 10.0 | 1420 | 0.3749 | 0.0034 | 50.0000 | 0.5970 | 0.0107 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.4498 | 0.0086 | 0.0025 | 0.0012 | 100 |
| 0.0107 | 11.0 | 1562 | 0.3746 | 0.0034 | 50.0000 | 0.5228 | 0.0100 | 0.0028 | 0.0014 | 100 | 70.7107 | 0.4349 | 0.0085 | 0.0024 | 0.0012 | 100 |
| 0.0107 | 12.0 | 1704 | 0.3635 | 0.0034 | 50.0000 | 0.5627 | 0.0104 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4728 | 0.0088 | 0.0025 | 0.0012 | 100 |
| 0.0107 | 13.0 | 1846 | 0.3629 | 0.0034 | 50.0000 | 0.5856 | 0.0106 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4481 | 0.0086 | 0.0024 | 0.0012 | 100 |
| 0.0107 | 14.0 | 1988 | 0.3697 | 0.0034 | 50.0000 | 0.5760 | 0.0105 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4778 | 0.0089 | 0.0025 | 0.0012 | 100 |
| 0.0096 | 15.0 | 2130 | 0.3630 | 0.0034 | 50.0000 | 0.5570 | 0.0103 | 0.0029 | 0.0014 | 100 | 59.4604 | 0.4514 | 0.0086 | 0.0025 | 0.0012 | 100 |
| 0.0096 | 16.0 | 2272 | 0.3608 | 0.0034 | 50.0000 | 0.5304 | 0.0101 | 0.0028 | 0.0014 | 100 | 100.0000 | 0.4481 | 0.0086 | 0.0024 | 0.0012 | 100 |
| 0.0096 | 17.0 | 2414 | 0.3612 | 0.0034 | 50.0000 | 0.5646 | 0.0104 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4415 | 0.0085 | 0.0024 | 0.0012 | 100 |
| 0.0057 | 18.0 | 2556 | 0.3571 | 0.0034 | 50.0000 | 0.5646 | 0.0104 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4415 | 0.0085 | 0.0024 | 0.0012 | 100 |
| 0.0057 | 19.0 | 2698 | 0.3655 | 0.0034 | 50.0000 | 0.5817 | 0.0105 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4514 | 0.0086 | 0.0025 | 0.0012 | 100 |
| 0.0057 | 20.0 | 2840 | 0.3682 | 0.0034 | 50.0000 | 0.5760 | 0.0105 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4580 | 0.0087 | 0.0025 | 0.0012 | 100 |
| 0.0057 | 21.0 | 2982 | 0.3683 | 0.0034 | 50.0000 | 0.5722 | 0.0104 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4646 | 0.0088 | 0.0025 | 0.0012 | 100 |
| 0.0046 | 22.0 | 3124 | 0.3745 | 0.0034 | 50.0000 | 0.5798 | 0.0105 | 0.0029 | 0.0014 | 100 | 100.0000 | 0.4695 | 0.0088 | 0.0025 | 0.0012 | 100 |
| 0.0046 | 23.0 | 3266 | 0.3680 | 0.0034 | 50.0000 | 0.6198 | 0.0109 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.4811 | 0.0089 | 0.0025 | 0.0012 | 100 |
| 0.0046 | 24.0 | 3408 | 0.3702 | 0.0034 | 50.0000 | 0.5951 | 0.0106 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.4811 | 0.0089 | 0.0025 | 0.0012 | 100 |
| 0.0039 | 25.0 | 3550 | 0.3701 | 0.0034 | 50.0000 | 0.5951 | 0.0106 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.4959 | 0.0090 | 0.0025 | 0.0012 | 100 |
| 0.0039 | 26.0 | 3692 | 0.3689 | 0.0034 | 50.0000 | 0.6065 | 0.0107 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.5041 | 0.0091 | 0.0025 | 0.0012 | 100 |
| 0.0039 | 27.0 | 3834 | 0.3681 | 0.0034 | 50.0000 | 0.6084 | 0.0108 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.5008 | 0.0091 | 0.0025 | 0.0012 | 100 |
| 0.0039 | 28.0 | 3976 | 0.3684 | 0.0034 | 50.0000 | 0.6217 | 0.0109 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.5025 | 0.0091 | 0.0025 | 0.0012 | 100 |
| 0.0033 | 29.0 | 4118 | 0.3702 | 0.0034 | 50.0000 | 0.6122 | 0.0108 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.5008 | 0.0091 | 0.0025 | 0.0012 | 100 |
| 0.0033 | 30.0 | 4260 | 0.3700 | 0.0034 | 50.0000 | 0.6160 | 0.0108 | 0.0030 | 0.0014 | 100 | 100.0000 | 0.5008 | 0.0091 | 0.0025 | 0.0012 | 100 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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