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mt5-small-finetuned-mlsum_es_with_xsum
slwk/mt5-small-finetuned-mlsum_es_with_xsum
mt5-small-finetuned-mlsum_es_with_xsum is a machine learning model from slwk. 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 apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- mt5
- Access
- Public
- Created
- Jul 9, 2025
- Updated
- Jul 10, 2025
- SHA
- 0893fbb6
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. -->
mt5-small-finetuned-mlsum_es_with_xsum
This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.7201
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: 5.6e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 313 | 3.1156 |
| 5.8634 | 2.0 | 626 | 2.8568 |
| 5.8634 | 3.0 | 939 | 2.7807 |
| 3.626 | 4.0 | 1252 | 2.7417 |
| 3.626 | 5.0 | 1565 | 2.7271 |
| 3.4691 | 6.0 | 1878 | 2.7201 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Jul 9, 2025
- Updated
- Jul 10, 2025