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mt5_small-silver-standard-lora
Firmansyah-Ibrahim/mt5_small-silver-standard-lora
mt5_small-silver-standard-lora is a machine learning model from Firmansyah-Ibrahim. 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 peft. 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
- peft
- License
- apache-2.0
- Access
- Public
- Created
- May 7, 2026
- Updated
- May 7, 2026
- SHA
- 39887ae4
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-silver-standard-lora
This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.9282
- Rouge1: 17.173
- Rouge2: 5.1482
- Rougel: 16.6617
- Rougelsum: 16.7675
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 54.2369 | 1.0 | 149 | 5.1842 | 6.182 | 1.2295 | 5.9424 | 5.8969 |
| 23.4568 | 2.0 | 298 | 4.0397 | 15.3558 | 4.4153 | 14.839 | 14.9532 |
| 20.8425 | 3.0 | 447 | 3.9282 | 17.173 | 5.1482 | 16.6617 | 16.7675 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
- Library
- peft
- License
- apache-2.0
- Created
- May 7, 2026
- Updated
- May 7, 2026