---
library_name: transformers
license: mit
base_model: facebook/mbart-large-50
tags:
- simplification
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-neutralization
  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. -->

# mbart-neutralization

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the dataset hackathon-pln-es/neutral-es (https://huggingface.co/datasets/somosnlp-hackathon-2022/neutral-es).
It achieves the following results on the evaluation set:
- Loss: 0.0138
- Bleu: 98.5147
- Gen Len: 18.4583

## Model description

This model was delivered as part of a class project, in which we used different NLP libraries and resources.

## Intended uses & limitations

The aim of this model is detecting gendered-specific structures in Spanish and neutralize them to get a more inclusive text.

Given the computing limitations, there is room for improvement and obtenining more neutral texts.


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log        | 1.0   | 440  | 0.0165          | 98.4773 | 18.6042 |
| 0.223         | 2.0   | 880  | 0.0138          | 98.5147 | 18.4583 |


### Framework versions

- Transformers 4.51.2
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4