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mbart-neutralization
This model is a fine-tuned version of facebook/mbart-large-50 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0132
- Bleu: 98.6021
- Gen Len: 18.5104
Intended uses & limitations
Translating Spanish sentences and texts into "neutral", "inclusive" language
Training and evaluation data
Training and evaluation dataset: Spanish Gender Neutralization dataset
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 | 0.0408 | 97.3967 | 18.7604 |
| 0.2255 | 2.0 | 880 | 0.0132 | 98.6021 | 18.5104 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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Model tree for Irisba/mbart-neutralization
Base model
facebook/mbart-large-50
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Irisba/mbart-neutralization") model = AutoModelForSeq2SeqLM.from_pretrained("Irisba/mbart-neutralization")