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t5-summarizer-fast
ncncomplete/t5-summarizer-fast
t5-summarizer-fast is a machine learning model from ncncomplete. 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 19, 2026
- Updated
- Apr 19, 2026
- SHA
- af52e28d
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-summarizer-fast
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0230
- Rouge1: 0.0369
- Rouge2: 0.0176
- Rougel: 0.031
- Rougelsum: 0.0312
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: 8
- eval_batch_size: 8
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 225 | 1.0963 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 450 | 1.0365 | 0.0116 | 0.0044 | 0.0092 | 0.0095 |
| 1.9999 | 3.0 | 675 | 1.0230 | 0.0369 | 0.0176 | 0.031 | 0.0312 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Apr 19, 2026
- Updated
- Apr 19, 2026