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fine_tuned_t5_small_model_sec_5_v10
miasetya/fine_tuned_t5_small_model_sec_5_v10
fine_tuned_t5_small_model_sec_5_v10 is a machine learning model from miasetya. 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
- Dec 5, 2024
- Updated
- Dec 5, 2024
- SHA
- b8186b6c
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. -->
fine_tuned_t5_small_model_sec_5_v10
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.9804
- Rouge1: 0.3974
- Rouge2: 0.1498
- Rougel: 0.2498
- Rougelsum: 0.2496
- Gen Len: 105.0526
- Bert F1: 0.8722
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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bert F1 |
|---|---|---|---|---|---|---|---|---|---|
| 3.7939 | 0.4211 | 40 | 3.4052 | 0.4026 | 0.1518 | 0.2501 | 0.2503 | 105.7158 | 0.8711 |
| 3.4088 | 0.8421 | 80 | 3.1841 | 0.4047 | 0.1523 | 0.25 | 0.2501 | 108.2895 | 0.8719 |
| 3.1447 | 1.2632 | 120 | 3.1051 | 0.4023 | 0.1526 | 0.246 | 0.246 | 108.6684 | 0.8719 |
| 3.2019 | 1.6842 | 160 | 3.0575 | 0.4025 | 0.1499 | 0.2451 | 0.2451 | 109.1053 | 0.8719 |
| 3.147 | 2.1053 | 200 | 3.0326 | 0.397 | 0.1468 | 0.2429 | 0.2428 | 108.3842 | 0.8713 |
| 3.0804 | 2.5263 | 240 | 3.0138 | 0.3974 | 0.1467 | 0.244 | 0.2443 | 106.7684 | 0.8714 |
| 3.1645 | 2.9474 | 280 | 3.0006 | 0.397 | 0.1492 | 0.246 | 0.2458 | 106.5947 | 0.8715 |
| 3.0244 | 3.3684 | 320 | 2.9930 | 0.3962 | 0.1465 | 0.2466 | 0.2466 | 105.5421 | 0.8712 |
| 3.1056 | 3.7895 | 360 | 2.9868 | 0.3941 | 0.1466 | 0.2471 | 0.2469 | 105.5526 | 0.8713 |
| 3.0879 | 4.2105 | 400 | 2.9823 | 0.3951 | 0.1489 | 0.2482 | 0.2481 | 104.5263 | 0.8717 |
| 3.0176 | 4.6316 | 440 | 2.9804 | 0.3974 | 0.1498 | 0.2498 | 0.2496 | 105.0526 | 0.8722 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Dec 5, 2024
- Updated
- Dec 5, 2024