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t5-small-translation_v3
JohnLei/t5-small-translation_v3
t5-small-translation_v3 is a machine learning model from JohnLei. 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
- t5
- Access
- Public
- Created
- Apr 13, 2025
- Updated
- Apr 13, 2025
- SHA
- 6390871e
Base models
Datasets
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. -->
t5-small-translation_v3
This model is a fine-tuned version of google-t5/t5-small on the covost2 dataset. It achieves the following results on the evaluation set:
- Loss: 4.0331
- Bleu: 0.1366
- Gen Len: 20.0
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 4.539 | 1.0 | 746 | 4.1366 | 0.0502 | 20.0 |
| 4.3657 | 2.0 | 1492 | 4.0781 | 0.0826 | 19.9982 |
| 4.2651 | 3.0 | 2238 | 4.0510 | 0.0589 | 19.9998 |
| 4.2638 | 4.0 | 2984 | 4.0376 | 0.1365 | 20.0 |
| 4.227 | 5.0 | 3730 | 4.0331 | 0.1366 | 20.0 |
Framework versions
- Transformers 4.51.2
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.21.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Apr 13, 2025
- Updated
- Apr 13, 2025