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t5-text2sql_v3
This model is a fine-tuned version of mousaazari/t5-text2sql_v1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1501
- Rouge2 Precision: 0.6088
- Rouge2 Recall: 0.3597
- Rouge2 Fmeasure: 0.4201
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|---|---|---|---|---|---|---|
| No log | 1.0 | 430 | 0.3126 | 0.3937 | 0.2301 | 0.2679 |
| 0.4851 | 2.0 | 860 | 0.2583 | 0.4656 | 0.2854 | 0.3289 |
| 0.3271 | 3.0 | 1290 | 0.2256 | 0.4858 | 0.2875 | 0.3337 |
| 0.2696 | 4.0 | 1720 | 0.2075 | 0.5193 | 0.3127 | 0.3614 |
| 0.2376 | 5.0 | 2150 | 0.1937 | 0.5387 | 0.3258 | 0.3773 |
| 0.2072 | 6.0 | 2580 | 0.1839 | 0.5524 | 0.3344 | 0.3876 |
| 0.1875 | 7.0 | 3010 | 0.1752 | 0.5644 | 0.3333 | 0.3882 |
| 0.1875 | 8.0 | 3440 | 0.1704 | 0.5751 | 0.3426 | 0.399 |
| 0.1736 | 9.0 | 3870 | 0.1653 | 0.5821 | 0.3458 | 0.4027 |
| 0.1585 | 10.0 | 4300 | 0.1603 | 0.5841 | 0.3435 | 0.4013 |
| 0.1498 | 11.0 | 4730 | 0.1576 | 0.5905 | 0.3535 | 0.4103 |
| 0.1427 | 12.0 | 5160 | 0.1548 | 0.6031 | 0.3533 | 0.4135 |
| 0.1342 | 13.0 | 5590 | 0.1541 | 0.5976 | 0.3519 | 0.411 |
| 0.1294 | 14.0 | 6020 | 0.1534 | 0.6058 | 0.3549 | 0.4161 |
| 0.1294 | 15.0 | 6450 | 0.1518 | 0.6117 | 0.3593 | 0.4203 |
| 0.1239 | 16.0 | 6880 | 0.1509 | 0.61 | 0.3597 | 0.4202 |
| 0.1198 | 17.0 | 7310 | 0.1508 | 0.6076 | 0.3588 | 0.4195 |
| 0.1147 | 18.0 | 7740 | 0.1503 | 0.6139 | 0.3607 | 0.4219 |
| 0.1155 | 19.0 | 8170 | 0.1503 | 0.6092 | 0.3597 | 0.4201 |
| 0.1115 | 20.0 | 8600 | 0.1501 | 0.6088 | 0.3597 | 0.4201 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.0+cu118
- Datasets 2.8.0
- Tokenizers 0.13.3
