---
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: 0.5690
- Bleu: 83.5807
- Rouge: {'rouge1': 0.9265753592812418, 'rouge2': 0.8656694324194325, 'rougeL': 0.9238164847135437, 'rougeLsum': 0.9238003663003664}
- Ter Score: 10.0090

## 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: 64
- 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    | Rouge                                                                                                                       | Ter Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:|:---------:|
| 0.997         | 1.0   | 75   | 0.7578          | 74.2121 | {'rouge1': 0.8930136077372922, 'rouge2': 0.8132252290193469, 'rougeL': 0.8868313923778324, 'rougeLsum': 0.8866414102466736} | 16.3210   |
| 0.4353        | 2.0   | 150  | 0.5659          | 50.7443 | {'rouge1': 0.9142509364274071, 'rouge2': 0.83197113997114, 'rougeL': 0.9055773276287983, 'rougeLsum': 0.9062817797670736}   | 12.8043   |
| 0.2602        | 3.0   | 225  | 0.5444          | 72.0122 | {'rouge1': 0.9183889862860454, 'rouge2': 0.8433486969005839, 'rougeL': 0.9132635343958876, 'rougeLsum': 0.913651539908893}  | 15.9603   |
| 0.2316        | 4.0   | 300  | 0.5503          | 50.9502 | {'rouge1': 0.9147289323852568, 'rouge2': 0.8403040453698347, 'rougeL': 0.9084138578656601, 'rougeLsum': 0.9084760810455303} | 13.0748   |
| 0.1203        | 5.0   | 375  | 0.5211          | 58.7666 | {'rouge1': 0.9278827629661555, 'rouge2': 0.8655444837508406, 'rougeL': 0.922415336132431, 'rougeLsum': 0.9224576705147474}  | 29.6664   |
| 0.1216        | 6.0   | 450  | 0.5491          | 81.6262 | {'rouge1': 0.9206053007450066, 'rouge2': 0.8534470899470898, 'rougeL': 0.9171148252618841, 'rougeLsum': 0.9168772093919156} | 11.0911   |
| 0.0754        | 7.0   | 525  | 0.5095          | 83.4616 | {'rouge1': 0.9305456776339132, 'rouge2': 0.8778395262145262, 'rougeL': 0.9280110015257075, 'rougeLsum': 0.9281936805025043} | 10.0090   |
| 0.0848        | 8.0   | 600  | 0.5538          | 81.8681 | {'rouge1': 0.9248025063172123, 'rouge2': 0.8648207579457581, 'rougeL': 0.9219360612154733, 'rougeLsum': 0.921904937654938}  | 10.4599   |
| 0.0504        | 9.0   | 675  | 0.5390          | 80.8118 | {'rouge1': 0.9217618560633272, 'rouge2': 0.8611767121767122, 'rougeL': 0.9194047336106163, 'rougeLsum': 0.9196579346579348} | 12.3535   |
| 0.0367        | 10.0  | 750  | 0.5632          | 82.2896 | {'rouge1': 0.9241220549602904, 'rouge2': 0.8623059255559258, 'rougeL': 0.921636625901332, 'rougeLsum': 0.9214262796027506}  | 10.8206   |
| 0.0386        | 11.0  | 825  | 0.5325          | 83.7819 | {'rouge1': 0.9264862667289138, 'rouge2': 0.8665701058201061, 'rougeL': 0.924734155278273, 'rougeLsum': 0.9247572857425799}  | 10.2795   |
| 0.0377        | 12.0  | 900  | 0.5540          | 83.6969 | {'rouge1': 0.9270570480717542, 'rouge2': 0.8649807692307694, 'rougeL': 0.9248777127012422, 'rougeLsum': 0.9247459680842035} | 10.0090   |
| 0.0244        | 13.0  | 975  | 0.5462          | 83.4825 | {'rouge1': 0.9284353783471431, 'rouge2': 0.8673707311207314, 'rougeL': 0.9249773075508372, 'rougeLsum': 0.924672456084221}  | 9.9188    |
| 0.0237        | 14.0  | 1050 | 0.5468          | 83.3820 | {'rouge1': 0.9267599383187618, 'rouge2': 0.8631084656084658, 'rougeL': 0.9244043657867187, 'rougeLsum': 0.9240160215601393} | 10.0992   |
| 0.0173        | 15.0  | 1125 | 0.5604          | 82.7936 | {'rouge1': 0.9260569985569987, 'rouge2': 0.8652394179894183, 'rougeL': 0.923313301078007, 'rougeLsum': 0.9233026695526696}  | 10.1894   |
| 0.0193        | 16.0  | 1200 | 0.5689          | 85.1028 | {'rouge1': 0.9298936104744928, 'rouge2': 0.874325396825397, 'rougeL': 0.9280833015024192, 'rougeLsum': 0.9275536633845459}  | 9.6483    |
| 0.0184        | 17.0  | 1275 | 0.5695          | 83.7781 | {'rouge1': 0.9266896553881849, 'rouge2': 0.8650757020757022, 'rougeL': 0.924688972247796, 'rougeLsum': 0.9245597692068284}  | 10.2795   |
| 0.0142        | 18.0  | 1350 | 0.5655          | 83.6649 | {'rouge1': 0.925748337718926, 'rouge2': 0.8645625300625305, 'rougeL': 0.9233836055012529, 'rougeLsum': 0.9233253614577146}  | 10.0090   |
| 0.0131        | 19.0  | 1425 | 0.5701          | 83.6843 | {'rouge1': 0.9268515199397553, 'rouge2': 0.8660478595478597, 'rougeL': 0.9242069248833956, 'rougeLsum': 0.9242629070276129} | 9.9188    |
| 0.0122        | 20.0  | 1500 | 0.5690          | 83.5807 | {'rouge1': 0.9265753592812418, 'rouge2': 0.8656694324194325, 'rougeL': 0.9238164847135437, 'rougeLsum': 0.9238003663003664} | 10.0090   |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.13.3