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mbart-finetuned-ES-ZAP-v3
josueu/mbart-finetuned-ES-ZAP-v3
mbart-finetuned-ES-ZAP-v3 is a machine learning model from josueu. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- mbart
- Access
- Public
- Created
- Mar 10, 2025
- Updated
- Mar 10, 2025
- SHA
- e695fb42
Base models
Model card
From the Hugging Face model README
<!-- 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-finetuned-ES-ZAP-v3
This model is a fine-tuned version of facebook/mbart-large-50 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5532
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: 2e-06
- train_batch_size: 4
- eval_batch_size: 4
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 10.5931 | 1.0 | 204 | 8.5621 |
| 7.8199 | 2.0 | 408 | 6.4131 |
| 5.8611 | 3.0 | 612 | 4.6442 |
| 4.2181 | 4.0 | 816 | 3.1848 |
| 2.89 | 5.0 | 1020 | 2.1098 |
| 1.944 | 6.0 | 1224 | 1.4628 |
| 1.3832 | 7.0 | 1428 | 1.1352 |
| 1.0273 | 8.0 | 1632 | 0.6296 |
| 0.6365 | 9.0 | 1836 | 0.5687 |
| 0.5704 | 10.0 | 2040 | 0.5532 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
- Library
- transformers
- Type
- mbart
- License
- mit
- Created
- Mar 10, 2025
- Updated
- Mar 10, 2025