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mT5_tai-lo_to_chinese_ver7.0.a
Curiousfox/mT5_tai-lo_to_chinese_ver7.0.a
mT5_tai-lo_to_chinese_ver7.0.a is a machine learning model from Curiousfox. 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
- Apr 6, 2026
- Updated
- Apr 6, 2026
- SHA
- 350be265
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. -->
outputs
This model is a fine-tuned version of google/mt5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1711
- Chrf: 0.0
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 1
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|---|---|---|---|---|
| 16.3755 | 0.4244 | 1000 | 7.5487 | 0.0 |
| 7.1060 | 0.8489 | 2000 | 3.5109 | 0.0 |
| 4.3106 | 1.2733 | 3000 | 3.2070 | 0.2841 |
| 3.9613 | 1.6978 | 4000 | 3.1724 | 0.0 |
| 3.7818 | 2.1222 | 5000 | 3.1711 | 0.0 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Apr 6, 2026
- Updated
- Apr 6, 2026