---
license: apache-2.0
base_model: vgaraujov/bart-base-spanish
tags:
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: esp-to-lsm-barto-model
  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-barto-model

This model is a fine-tuned version of [vgaraujov/bart-base-spanish](https://huggingface.co/vgaraujov/bart-base-spanish) on Spanish-Mexican Sign Language (MSL) glosses dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0118
- Bleu: 82.2615
- Rouge: {'rouge1': 0.9459411340293693, 'rouge2': 0.8725612535612537, 'rougeL': 0.9409690603514131, 'rougeLsum': 0.9414154570919278}
- Ter: 7.9703

## 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: 1.5e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:|:-------:|
| 0.0862        | 1.0   | 85   | 0.0441          | 48.0832 | {'rouge1': 0.7799851292498354, 'rouge2': 0.6094051319051321, 'rougeL': 0.7634691389764923, 'rougeLsum': 0.7637001348324879} | 34.4764 |
| 0.0323        | 2.0   | 170  | 0.0214          | 72.9928 | {'rouge1': 0.884157046828874, 'rouge2': 0.7702001337442517, 'rougeL': 0.8722237432043938, 'rougeLsum': 0.8720308750417114}  | 16.6821 |
| 0.0198        | 3.0   | 255  | 0.0161          | 78.5669 | {'rouge1': 0.9199390239390244, 'rouge2': 0.82409139009139, 'rougeL': 0.911472143869203, 'rougeLsum': 0.9116302602626135}    | 12.0482 |
| 0.0162        | 4.0   | 340  | 0.0143          | 79.2390 | {'rouge1': 0.9243402735608619, 'rouge2': 0.8460535205535207, 'rougeL': 0.9158039177598002, 'rougeLsum': 0.9159157335039689} | 10.7507 |
| 0.0137        | 5.0   | 425  | 0.0137          | 82.3938 | {'rouge1': 0.9334139504286565, 'rouge2': 0.8579696784696786, 'rougeL': 0.9274591149591148, 'rougeLsum': 0.927894385026738}  | 9.6386  |
| 0.0108        | 6.0   | 510  | 0.0128          | 84.1329 | {'rouge1': 0.9350887445887449, 'rouge2': 0.8754486161986161, 'rougeL': 0.9311620617944146, 'rougeLsum': 0.9313348612172142} | 9.0825  |
| 0.0098        | 7.0   | 595  | 0.0129          | 79.7416 | {'rouge1': 0.9399191766838828, 'rouge2': 0.8716096403596405, 'rougeL': 0.9330582073155609, 'rougeLsum': 0.933733249865603}  | 9.4532  |
| 0.009         | 8.0   | 680  | 0.0125          | 82.9321 | {'rouge1': 0.9443956476530007, 'rouge2': 0.8689144281644281, 'rougeL': 0.9390896358543419, 'rougeLsum': 0.9394144809438929} | 8.8971  |
| 0.0084        | 9.0   | 765  | 0.0122          | 81.9698 | {'rouge1': 0.946071417961124, 'rouge2': 0.8742369759869761, 'rougeL': 0.9409199134199135, 'rougeLsum': 0.9414803284950346}  | 9.0825  |
| 0.0068        | 10.0  | 850  | 0.0121          | 81.9526 | {'rouge1': 0.9484588107970461, 'rouge2': 0.8778730158730159, 'rougeL': 0.9433170783464899, 'rougeLsum': 0.9437279305661661} | 8.4337  |
| 0.0078        | 11.0  | 935  | 0.0118          | 82.4911 | {'rouge1': 0.9460536750830865, 'rouge2': 0.8745218762718765, 'rougeL': 0.9401823225793814, 'rougeLsum': 0.9404524821583646} | 8.8044  |
| 0.0063        | 12.0  | 1020 | 0.0120          | 81.7252 | {'rouge1': 0.9465396825396828, 'rouge2': 0.8755185185185186, 'rougeL': 0.9404898777692895, 'rougeLsum': 0.941089275103981}  | 8.8044  |
| 0.0069        | 13.0  | 1105 | 0.0121          | 81.7348 | {'rouge1': 0.9456640068308027, 'rouge2': 0.8716636381048146, 'rougeL': 0.940350419274568, 'rougeLsum': 0.941292909747631}   | 8.5264  |
| 0.0059        | 14.0  | 1190 | 0.0120          | 82.7243 | {'rouge1': 0.9473343307019777, 'rouge2': 0.8731392958892958, 'rougeL': 0.9422385620915033, 'rougeLsum': 0.9425819221628045} | 8.4337  |
| 0.006         | 15.0  | 1275 | 0.0118          | 81.2037 | {'rouge1': 0.9470927234530175, 'rouge2': 0.8718730158730159, 'rougeL': 0.942004562431033, 'rougeLsum': 0.9425554706731177}  | 8.3411  |
| 0.0055        | 16.0  | 1360 | 0.0119          | 82.1601 | {'rouge1': 0.9435703038791275, 'rouge2': 0.8706992266992267, 'rougeL': 0.938448826500297, 'rougeLsum': 0.9388509252185724}  | 8.1557  |
| 0.0055        | 17.0  | 1445 | 0.0119          | 82.0465 | {'rouge1': 0.9453517120564336, 'rouge2': 0.8718517740429506, 'rougeL': 0.9403101408825094, 'rougeLsum': 0.940731923391366}  | 8.0630  |
| 0.0051        | 18.0  | 1530 | 0.0118          | 82.1849 | {'rouge1': 0.9452478036669215, 'rouge2': 0.8716373626373629, 'rougeL': 0.9402017337237925, 'rougeLsum': 0.9406384008148714} | 8.0630  |
| 0.0055        | 19.0  | 1615 | 0.0118          | 82.0985 | {'rouge1': 0.9452005559799677, 'rouge2': 0.8723565323565323, 'rougeL': 0.9399868644427471, 'rougeLsum': 0.9405265469824293} | 8.0630  |
| 0.0052        | 20.0  | 1700 | 0.0118          | 82.2615 | {'rouge1': 0.9459411340293693, 'rouge2': 0.8725612535612537, 'rougeL': 0.9409690603514131, 'rougeLsum': 0.9414154570919278} | 7.9703  |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1