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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/mbart-large-50 |
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tags: |
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- simplification |
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- generated_from_trainer |
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metrics: |
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- BLEU |
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datasets: |
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- hackathon-pln-es/neutral-es |
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language: |
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- es |
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--- |
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# mbart-neutralization |
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This model is a fine-tuned version of facebook/mbart-large-50 on the hackathon-pln-es/neutral-es dataset. |
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It learns to paraphrase gender-marked expressions into an inclusive style. For example, "La enfermera me curó" → "El personal sanitario me curó", |
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thereby promoting more inclusive language. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0118 |
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- BLEU: 63.5448 |
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- Generation length: 36.7604 |
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## Model description |
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mBART-50 is a pretrained multilingual encoder–decoder (sequence-to-sequence) model that supports 50 languages. |
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It was designed to show that, instead of fine-tuning a separate model for each language pair, a single pre-trained model can be fine-tuned simultaneously on multiple |
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translation directions. |
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Building on the original mBART, it extends coverage by adding 25 more languages (for a total of 50), delivering a truly multilingual solution. |
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During pre-training, mBART-50 employs a denoising autoencoding objective: |
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monolingual sentences are “noised” by randomly shuffling their order and span-masking a portion of tokens, and the model learns to reconstruct the original text. |
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## Intended uses |
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- Reducing gender bias in Spanish texts via monolingual style transfer. |
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- Preprocessing step in NLP pipelines (e.g. for editorial tools or inclusive content generation). |
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- As a basis for further fine-tuning on related sequence-to-sequence tasks (summarization, paraphrasing). |
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## Limitations |
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- Only neutralizes gendered expressions in Spanish; it does not translate between languages. |
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- Quality may degrade on domain-specific or very technical texts outside the training distribution. |
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- May occasionally produce ungrammatical or awkward phrasing when forced to alter rare word combinations. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5.6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| |
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| No log | 1.0 | 440 | 0.0151 | 88.2841 | 34.8125 | |
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| 0.2281 | 2.0 | 880 | 0.0118 | 63.5448 | 36.7604 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
