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t5-translation-base-v7
JohnLei/t5-translation-base-v7
t5-translation-base-v7 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 14, 2025
- Updated
- Apr 14, 2025
- SHA
- 9500f8ed
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. -->
t5-translation-base-v7
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7566
- Bleu: 10.3783
- Gen Len: 10.5374
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-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 0.8291 | 1.0 | 33789 | 0.7836 | 9.4438 | 10.3441 |
| 0.8059 | 2.0 | 67578 | 0.7664 | 10.1297 | 10.429 |
| 0.7978 | 3.0 | 101367 | 0.7589 | 10.357 | 10.563 |
| 0.7952 | 4.0 | 135156 | 0.7566 | 10.3783 | 10.5374 |
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 14, 2025
- Updated
- Apr 14, 2025