---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: esp-to-lsm-model-split
  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. -->

# esp-to-lsm-model-split

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co./Helsinki-NLP/opus-mt-es-es) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5118
- Bleu: 85.2207
- Ter: 8.9524
- Rouge1: 0.9459
- Rouge2: 0.8835

## 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: 0.00015
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Ter     | Rouge1 | Rouge2 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:------:|
| 0.997         | 1.0   | 75   | 0.7490          | 39.0819 | 32.0952 | 0.8972 | 0.8107 |
| 0.4353        | 2.0   | 150  | 0.5363          | 78.0900 | 11.9048 | 0.9174 | 0.8421 |
| 0.2602        | 3.0   | 225  | 0.5100          | 47.9363 | 15.3333 | 0.9230 | 0.8407 |
| 0.2316        | 4.0   | 300  | 0.4974          | 79.0734 | 11.5238 | 0.9214 | 0.8392 |
| 0.1203        | 5.0   | 375  | 0.4946          | 80.3010 | 10.7619 | 0.9343 | 0.8695 |
| 0.1216        | 6.0   | 450  | 0.5038          | 80.6212 | 10.6667 | 0.9313 | 0.8571 |
| 0.0754        | 7.0   | 525  | 0.4650          | 69.5096 | 9.1429  | 0.9366 | 0.8773 |
| 0.0848        | 8.0   | 600  | 0.5086          | 83.6847 | 9.6190  | 0.9340 | 0.8599 |
| 0.0504        | 9.0   | 675  | 0.4904          | 82.1068 | 11.2381 | 0.9389 | 0.8777 |
| 0.0367        | 10.0  | 750  | 0.5040          | 84.9459 | 9.4286  | 0.9425 | 0.8787 |
| 0.0386        | 11.0  | 825  | 0.4964          | 84.3063 | 9.6190  | 0.9417 | 0.8770 |
| 0.0377        | 12.0  | 900  | 0.4992          | 84.2254 | 9.2381  | 0.9437 | 0.8809 |
| 0.0244        | 13.0  | 975  | 0.5052          | 84.7862 | 9.4286  | 0.9407 | 0.8762 |
| 0.0237        | 14.0  | 1050 | 0.5037          | 82.0504 | 9.8095  | 0.9396 | 0.8706 |
| 0.0173        | 15.0  | 1125 | 0.5105          | 85.2564 | 9.0476  | 0.9453 | 0.8821 |
| 0.0193        | 16.0  | 1200 | 0.5129          | 85.2911 | 8.9524  | 0.9460 | 0.8854 |
| 0.0184        | 17.0  | 1275 | 0.5121          | 84.8249 | 9.2381  | 0.9444 | 0.8808 |
| 0.0142        | 18.0  | 1350 | 0.5088          | 85.2207 | 8.9524  | 0.9459 | 0.8835 |
| 0.0131        | 19.0  | 1425 | 0.5127          | 85.2207 | 8.9524  | 0.9459 | 0.8835 |
| 0.0122        | 20.0  | 1500 | 0.5118          | 85.2207 | 8.9524  | 0.9459 | 0.8835 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.13.3