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t5-small-indosum-1
mrizalf7/t5-small-indosum-1
t5-small-indosum-1 is a machine learning model from mrizalf7. 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
- Aug 2, 2023
- Updated
- Aug 6, 2023
- SHA
- 3d1e7364
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-indosum-1
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4057
- Rouge1: 15.2719
- Rouge2: 11.8877
- Rougel: 15.0297
- Rougelsum: 15.1504
- Gen Len: 19.0
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: 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 |
|---|---|---|---|---|---|---|---|---|
| 0.482 | 1.0 | 4460 | 0.4370 | 15.1965 | 11.8078 | 14.9509 | 15.0704 | 19.0 |
| 0.4464 | 2.0 | 8920 | 0.4110 | 15.2567 | 11.8679 | 15.0112 | 15.132 | 19.0 |
| 0.4421 | 3.0 | 13380 | 0.4057 | 15.2719 | 11.8877 | 15.0297 | 15.1504 | 19.0 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Aug 2, 2023
- Updated
- Aug 6, 2023