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ViTay-insert-original
FiveC/ViTay-insert-original
ViTay-insert-original is a machine learning model from FiveC. 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 mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- mbart
- Access
- Public
- Created
- Dec 16, 2025
- Updated
- Dec 16, 2025
- SHA
- 60d3040d
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. -->
ViTay-insert-original
This model is a fine-tuned version of FiveC/BartTay on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1409
- Sacrebleu: 19.4815
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: 16
- eval_batch_size: 16
- seed: 42
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Sacrebleu |
|---|---|---|---|---|
| 0.1869 | 1.0 | 2570 | 0.1633 | 15.3004 |
| 0.135 | 2.0 | 5140 | 0.1438 | 18.6032 |
| 0.1203 | 3.0 | 7710 | 0.1409 | 19.4815 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Library
- transformers
- Type
- mbart
- License
- mit
- Created
- Dec 16, 2025
- Updated
- Dec 16, 2025