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t5-small_finetuned
nnguyen96/t5-small_finetuned
t5-small_finetuned is a machine learning model from nnguyen96. 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
- Nov 9, 2024
- Updated
- Nov 10, 2024
- SHA
- 00e5df62
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-small_finetuned
This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.5066
- Rouge1: 0.1851
- Rouge2: 0.0284
- Rougel: 0.148
- Rougelsum: 0.1477
- Gen Len: 18.845
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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 3.8941 | 1.0 | 100 | 3.5902 | 0.1785 | 0.0256 | 0.1421 | 0.1424 | 18.765 |
| 3.6658 | 2.0 | 200 | 3.5176 | 0.1849 | 0.0287 | 0.1481 | 0.1477 | 18.805 |
| 3.6187 | 3.0 | 300 | 3.5066 | 0.1851 | 0.0284 | 0.148 | 0.1477 | 18.845 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Nov 9, 2024
- Updated
- Nov 10, 2024