| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: t5-text2sql |
| | results: [] |
| | --- |
| | |
| | <!-- 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-text2sql |
| |
|
| | This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1611 |
| | - Rouge2 Precision: 0.8631 |
| | - Rouge2 Recall: 0.2595 |
| | - Rouge2 Fmeasure: 0.3674 |
| |
|
| | ## 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: 30 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
| | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | | No log | 1.0 | 11 | 1.8867 | 0.0 | 0.0 | 0.0 | |
| | | No log | 2.0 | 22 | 0.9658 | 0.0119 | 0.0015 | 0.0027 | |
| | | No log | 3.0 | 33 | 0.6477 | 0.0468 | 0.0078 | 0.0135 | |
| | | No log | 4.0 | 44 | 0.4617 | 0.4251 | 0.14 | 0.1943 | |
| | | No log | 5.0 | 55 | 0.3669 | 0.6403 | 0.2091 | 0.2937 | |
| | | No log | 6.0 | 66 | 0.3084 | 0.7085 | 0.2446 | 0.3393 | |
| | | No log | 7.0 | 77 | 0.2788 | 0.7282 | 0.2246 | 0.3175 | |
| | | No log | 8.0 | 88 | 0.2549 | 0.7593 | 0.2346 | 0.332 | |
| | | No log | 9.0 | 99 | 0.2368 | 0.7738 | 0.2367 | 0.3348 | |
| | | No log | 10.0 | 110 | 0.2322 | 0.7889 | 0.2388 | 0.3393 | |
| | | No log | 11.0 | 121 | 0.2151 | 0.8056 | 0.2419 | 0.3452 | |
| | | No log | 12.0 | 132 | 0.2067 | 0.7996 | 0.2371 | 0.3382 | |
| | | No log | 13.0 | 143 | 0.2003 | 0.7943 | 0.2365 | 0.3364 | |
| | | No log | 14.0 | 154 | 0.1899 | 0.8204 | 0.244 | 0.3477 | |
| | | No log | 15.0 | 165 | 0.1869 | 0.8309 | 0.2454 | 0.3502 | |
| | | No log | 16.0 | 176 | 0.1826 | 0.8309 | 0.2454 | 0.3502 | |
| | | No log | 17.0 | 187 | 0.1797 | 0.8252 | 0.245 | 0.3488 | |
| | | No log | 18.0 | 198 | 0.1749 | 0.8353 | 0.2479 | 0.3535 | |
| | | No log | 19.0 | 209 | 0.1726 | 0.8393 | 0.2508 | 0.3566 | |
| | | No log | 20.0 | 220 | 0.1716 | 0.8373 | 0.2475 | 0.3538 | |
| | | No log | 21.0 | 231 | 0.1695 | 0.8472 | 0.2489 | 0.3553 | |
| | | No log | 22.0 | 242 | 0.1693 | 0.8472 | 0.2519 | 0.3589 | |
| | | No log | 23.0 | 253 | 0.1685 | 0.877 | 0.271 | 0.3808 | |
| | | No log | 24.0 | 264 | 0.1668 | 0.8552 | 0.2598 | 0.3666 | |
| | | No log | 25.0 | 275 | 0.1641 | 0.8552 | 0.252 | 0.3591 | |
| | | No log | 26.0 | 286 | 0.1628 | 0.8671 | 0.2598 | 0.3683 | |
| | | No log | 27.0 | 297 | 0.1617 | 0.8631 | 0.2595 | 0.3674 | |
| | | No log | 28.0 | 308 | 0.1611 | 0.8631 | 0.2595 | 0.3674 | |
| | | No log | 29.0 | 319 | 0.1611 | 0.8631 | 0.2595 | 0.3674 | |
| | | No log | 30.0 | 330 | 0.1611 | 0.8631 | 0.2595 | 0.3674 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.21.3 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| |
|
