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t5-fra-cor-small
zenocode-org/t5-fra-cor-small
t5-fra-cor-small is a machine learning model from zenocode-org. 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
- Mar 8, 2026
- Updated
- Mar 8, 2026
- SHA
- 5bdb38f8
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-fra-cor-small
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: 3.1998
- Bleu: 1.4953
- Gen Len: 68.0984
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 16.2996 | 1.0 | 290 | 3.4781 | 1.3942 | 76.5738 |
| 14.3797 | 2.0 | 580 | 3.2525 | 1.4423 | 68.4918 |
| 13.9806 | 3.0 | 870 | 3.1998 | 1.4953 | 68.0984 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Mar 8, 2026
- Updated
- Mar 8, 2026