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t5-text2sql

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PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
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update model card README.md

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@@ -14,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0593
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- - Rouge2 Precision: 0.7821
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- - Rouge2 Recall: 0.1774
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- - Rouge2 Fmeasure: 0.2845
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0001
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -48,36 +48,36 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
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  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
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- | No log | 1.0 | 50 | 0.1988 | 0.5696 | 0.141 | 0.2222 |
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- | No log | 2.0 | 100 | 0.1259 | 0.6107 | 0.1385 | 0.2226 |
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- | No log | 3.0 | 150 | 0.0957 | 0.6533 | 0.1461 | 0.2352 |
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- | No log | 4.0 | 200 | 0.0822 | 0.665 | 0.1494 | 0.2402 |
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- | No log | 5.0 | 250 | 0.0776 | 0.691 | 0.1569 | 0.2518 |
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- | No log | 6.0 | 300 | 0.0693 | 0.6893 | 0.1544 | 0.2486 |
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- | No log | 7.0 | 350 | 0.0676 | 0.6966 | 0.1614 | 0.2583 |
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- | No log | 8.0 | 400 | 0.0653 | 0.7006 | 0.1578 | 0.2537 |
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- | No log | 9.0 | 450 | 0.0628 | 0.6784 | 0.152 | 0.2445 |
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- | 0.1876 | 10.0 | 500 | 0.0612 | 0.7203 | 0.1628 | 0.2615 |
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- | 0.1876 | 11.0 | 550 | 0.0615 | 0.7378 | 0.1673 | 0.2688 |
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- | 0.1876 | 12.0 | 600 | 0.0595 | 0.7161 | 0.1599 | 0.2573 |
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- | 0.1876 | 13.0 | 650 | 0.0588 | 0.7317 | 0.1659 | 0.2661 |
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- | 0.1876 | 14.0 | 700 | 0.0595 | 0.6959 | 0.1589 | 0.2542 |
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- | 0.1876 | 15.0 | 750 | 0.0576 | 0.74 | 0.167 | 0.2686 |
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- | 0.1876 | 16.0 | 800 | 0.0590 | 0.7149 | 0.1611 | 0.2587 |
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- | 0.1876 | 17.0 | 850 | 0.0574 | 0.7398 | 0.1664 | 0.2674 |
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- | 0.1876 | 18.0 | 900 | 0.0574 | 0.7557 | 0.171 | 0.2749 |
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- | 0.1876 | 19.0 | 950 | 0.0618 | 0.7366 | 0.1671 | 0.2676 |
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- | 0.0344 | 20.0 | 1000 | 0.0583 | 0.7692 | 0.1764 | 0.2821 |
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- | 0.0344 | 21.0 | 1050 | 0.0606 | 0.7757 | 0.1762 | 0.2823 |
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- | 0.0344 | 22.0 | 1100 | 0.0582 | 0.7622 | 0.1747 | 0.2795 |
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- | 0.0344 | 23.0 | 1150 | 0.0595 | 0.7677 | 0.1747 | 0.2798 |
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- | 0.0344 | 24.0 | 1200 | 0.0589 | 0.767 | 0.1726 | 0.2763 |
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- | 0.0344 | 25.0 | 1250 | 0.0587 | 0.7797 | 0.1769 | 0.2836 |
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- | 0.0344 | 26.0 | 1300 | 0.0584 | 0.7713 | 0.1748 | 0.2803 |
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- | 0.0344 | 27.0 | 1350 | 0.0583 | 0.7854 | 0.1779 | 0.2854 |
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- | 0.0344 | 28.0 | 1400 | 0.0590 | 0.7829 | 0.1783 | 0.2857 |
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- | 0.0344 | 29.0 | 1450 | 0.0592 | 0.7876 | 0.1786 | 0.2864 |
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- | 0.0227 | 30.0 | 1500 | 0.0593 | 0.7821 | 0.1774 | 0.2845 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0605
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+ - Rouge2 Precision: 0.7259
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+ - Rouge2 Recall: 0.1626
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+ - Rouge2 Fmeasure: 0.2617
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
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  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
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+ | No log | 1.0 | 50 | 0.3418 | 0.5312 | 0.1269 | 0.2015 |
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+ | No log | 2.0 | 100 | 0.1792 | 0.5827 | 0.1484 | 0.2329 |
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+ | No log | 3.0 | 150 | 0.1360 | 0.6261 | 0.145 | 0.2323 |
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+ | No log | 4.0 | 200 | 0.1125 | 0.6427 | 0.1444 | 0.2332 |
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+ | No log | 5.0 | 250 | 0.0990 | 0.6455 | 0.1459 | 0.2338 |
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+ | No log | 6.0 | 300 | 0.0897 | 0.6538 | 0.1476 | 0.237 |
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+ | No log | 7.0 | 350 | 0.0836 | 0.6444 | 0.1471 | 0.2363 |
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+ | No log | 8.0 | 400 | 0.0790 | 0.6818 | 0.1541 | 0.2477 |
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+ | No log | 9.0 | 450 | 0.0756 | 0.6966 | 0.1565 | 0.2518 |
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+ | 0.2853 | 10.0 | 500 | 0.0728 | 0.6819 | 0.1534 | 0.2468 |
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+ | 0.2853 | 11.0 | 550 | 0.0710 | 0.7059 | 0.1595 | 0.2566 |
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+ | 0.2853 | 12.0 | 600 | 0.0700 | 0.6955 | 0.1569 | 0.2523 |
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+ | 0.2853 | 13.0 | 650 | 0.0695 | 0.7015 | 0.1568 | 0.2525 |
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+ | 0.2853 | 14.0 | 700 | 0.0668 | 0.7017 | 0.157 | 0.2531 |
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+ | 0.2853 | 15.0 | 750 | 0.0650 | 0.6924 | 0.1554 | 0.2504 |
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+ | 0.2853 | 16.0 | 800 | 0.0652 | 0.6942 | 0.1551 | 0.2499 |
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+ | 0.2853 | 17.0 | 850 | 0.0636 | 0.6877 | 0.1528 | 0.2463 |
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+ | 0.2853 | 18.0 | 900 | 0.0625 | 0.7023 | 0.1567 | 0.2526 |
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+ | 0.2853 | 19.0 | 950 | 0.0638 | 0.7092 | 0.1591 | 0.2558 |
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+ | 0.0584 | 20.0 | 1000 | 0.0624 | 0.7043 | 0.158 | 0.2545 |
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+ | 0.0584 | 21.0 | 1050 | 0.0636 | 0.7032 | 0.1573 | 0.2534 |
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+ | 0.0584 | 22.0 | 1100 | 0.0612 | 0.6996 | 0.1558 | 0.2511 |
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+ | 0.0584 | 23.0 | 1150 | 0.0627 | 0.7184 | 0.1619 | 0.2607 |
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+ | 0.0584 | 24.0 | 1200 | 0.0615 | 0.7165 | 0.1607 | 0.2587 |
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+ | 0.0584 | 25.0 | 1250 | 0.0610 | 0.715 | 0.1601 | 0.2578 |
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+ | 0.0584 | 26.0 | 1300 | 0.0606 | 0.7218 | 0.1626 | 0.2616 |
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+ | 0.0584 | 27.0 | 1350 | 0.0602 | 0.7269 | 0.1634 | 0.2628 |
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+ | 0.0584 | 28.0 | 1400 | 0.0605 | 0.7203 | 0.1618 | 0.2605 |
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+ | 0.0584 | 29.0 | 1450 | 0.0604 | 0.7259 | 0.1626 | 0.2617 |
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+ | 0.0442 | 30.0 | 1500 | 0.0605 | 0.7259 | 0.1626 | 0.2617 |
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  ### Framework versions