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t5-small-textsum-indosum
mrizalf7/t5-small-textsum-indosum
t5-small-textsum-indosum 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
- Jun 23, 2023
- Updated
- Jun 23, 2023
- SHA
- dbcb6191
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-textsum-indosum
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1918
- Rouge1: 31.4955
- Rouge2: 27.4003
- Rougel: 30.7387
- Rougelsum: 30.7506
- 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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|---|
| 1.3319 | 1.0 | 2230 | 1.2512 | 31.4524 | 27.342 | 30.6812 | 30.6953 | 19.0 |
| 1.2648 | 2.0 | 4460 | 1.1995 | 31.485 | 27.3937 | 30.7257 | 30.7409 | 19.0 |
| 1.2355 | 3.0 | 6690 | 1.1918 | 31.4955 | 27.4003 | 30.7387 | 30.7506 | 19.0 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Jun 23, 2023
- Updated
- Jun 23, 2023