---
library_name: transformers
license: mit
base_model: facebook/mbart-large-50
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart50-es-zapv2
  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. -->

# mbart50-es-zapv2

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2902
- Bleu: 18.7613
- Ter: 69.5611
- Meteor: 0.4206

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Ter     | Meteor |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|
| 5.9172        | 1.0   | 179  | 0.4674          | 3.6424  | 91.5076 | 0.2051 |
| 0.6639        | 2.0   | 358  | 0.7822          | 0.0     | 100.0   | 0.0    |
| 0.4960        | 3.0   | 537  | 0.2733          | 8.1370  | 84.6374 | 0.2895 |
| 0.1756        | 4.0   | 716  | 0.2302          | 16.6049 | 77.2901 | 0.4079 |
| 0.1186        | 5.0   | 895  | 0.2245          | 18.6545 | 69.3702 | 0.4340 |
| 0.0656        | 6.0   | 1074 | 0.2474          | 20.0563 | 68.9885 | 0.4478 |
| 0.0496        | 7.0   | 1253 | 0.3485          | 14.4966 | 76.7176 | 0.3331 |
| 0.0469        | 8.0   | 1432 | 0.2902          | 18.7613 | 69.5611 | 0.4206 |


### Framework versions

- Transformers 5.4.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2