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t5-text2sql

Text2Text Generation
Transformers
PyTorch
TensorBoard
t5
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: 0.0621
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- - Rouge2 Precision: 0.7239
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- - Rouge2 Recall: 0.1683
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- - Rouge2 Fmeasure: 0.2685
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  ## Model description
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@@ -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 | 0.4350 | 0.3583 | 0.0648 | 0.1084 |
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- | No log | 2.0 | 100 | 0.2193 | 0.572 | 0.1265 | 0.2046 |
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- | No log | 3.0 | 150 | 0.1605 | 0.5637 | 0.1352 | 0.2151 |
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- | No log | 4.0 | 200 | 0.1274 | 0.6209 | 0.1501 | 0.2385 |
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- | No log | 5.0 | 250 | 0.1132 | 0.6115 | 0.1456 | 0.2319 |
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- | No log | 6.0 | 300 | 0.1004 | 0.6029 | 0.144 | 0.2283 |
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- | No log | 7.0 | 350 | 0.0943 | 0.611 | 0.1485 | 0.2348 |
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- | No log | 8.0 | 400 | 0.0868 | 0.6605 | 0.1557 | 0.2473 |
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- | No log | 9.0 | 450 | 0.0820 | 0.6587 | 0.1544 | 0.246 |
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- | 0.3249 | 10.0 | 500 | 0.0792 | 0.6429 | 0.1523 | 0.2421 |
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- | 0.3249 | 11.0 | 550 | 0.0750 | 0.6516 | 0.1538 | 0.2444 |
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- | 0.3249 | 12.0 | 600 | 0.0742 | 0.6679 | 0.1575 | 0.2512 |
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- | 0.3249 | 13.0 | 650 | 0.0722 | 0.6778 | 0.1583 | 0.2524 |
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- | 0.3249 | 14.0 | 700 | 0.0695 | 0.6939 | 0.161 | 0.2571 |
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- | 0.3249 | 15.0 | 750 | 0.0683 | 0.6813 | 0.1553 | 0.2486 |
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- | 0.3249 | 16.0 | 800 | 0.0680 | 0.6833 | 0.1568 | 0.2506 |
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- | 0.3249 | 17.0 | 850 | 0.0668 | 0.6869 | 0.1577 | 0.252 |
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- | 0.3249 | 18.0 | 900 | 0.0660 | 0.7064 | 0.1627 | 0.2597 |
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- | 0.3249 | 19.0 | 950 | 0.0660 | 0.7405 | 0.1726 | 0.2752 |
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- | 0.0684 | 20.0 | 1000 | 0.0653 | 0.7295 | 0.171 | 0.2722 |
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- | 0.0684 | 21.0 | 1050 | 0.0646 | 0.7337 | 0.1709 | 0.2728 |
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- | 0.0684 | 22.0 | 1100 | 0.0632 | 0.7196 | 0.1678 | 0.2673 |
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- | 0.0684 | 23.0 | 1150 | 0.0633 | 0.7098 | 0.1651 | 0.2635 |
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- | 0.0684 | 24.0 | 1200 | 0.0622 | 0.7214 | 0.1682 | 0.2681 |
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- | 0.0684 | 25.0 | 1250 | 0.0626 | 0.7274 | 0.1692 | 0.2699 |
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- | 0.0684 | 26.0 | 1300 | 0.0622 | 0.7269 | 0.169 | 0.2696 |
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- | 0.0684 | 27.0 | 1350 | 0.0622 | 0.7287 | 0.1696 | 0.2705 |
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- | 0.0684 | 28.0 | 1400 | 0.0621 | 0.7099 | 0.1642 | 0.2624 |
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- | 0.0684 | 29.0 | 1450 | 0.0621 | 0.7239 | 0.1683 | 0.2685 |
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- | 0.0516 | 30.0 | 1500 | 0.0621 | 0.7239 | 0.1683 | 0.2685 |
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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: 0.1611
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+ - Rouge2 Precision: 0.8651
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+ - Rouge2 Recall: 0.2594
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+ - Rouge2 Fmeasure: 0.3671
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  ## Model description
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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 | 11 | 1.8867 | 0.0 | 0.0 | 0.0 |
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+ | No log | 2.0 | 22 | 0.9658 | 0.0119 | 0.0015 | 0.0027 |
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+ | No log | 3.0 | 33 | 0.6477 | 0.0468 | 0.0078 | 0.0135 |
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+ | No log | 4.0 | 44 | 0.4617 | 0.4211 | 0.1387 | 0.1938 |
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+ | No log | 5.0 | 55 | 0.3669 | 0.6388 | 0.2069 | 0.2927 |
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+ | No log | 6.0 | 66 | 0.3084 | 0.7073 | 0.2407 | 0.3357 |
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+ | No log | 7.0 | 77 | 0.2788 | 0.727 | 0.2232 | 0.3153 |
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+ | No log | 8.0 | 88 | 0.2549 | 0.7594 | 0.2343 | 0.3305 |
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+ | No log | 9.0 | 99 | 0.2368 | 0.7733 | 0.2363 | 0.3337 |
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+ | No log | 10.0 | 110 | 0.2322 | 0.7889 | 0.2393 | 0.3381 |
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+ | No log | 11.0 | 121 | 0.2151 | 0.806 | 0.2423 | 0.3436 |
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+ | No log | 12.0 | 132 | 0.2067 | 0.7995 | 0.2365 | 0.3359 |
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+ | No log | 13.0 | 143 | 0.2003 | 0.7955 | 0.235 | 0.3342 |
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+ | No log | 14.0 | 154 | 0.1899 | 0.823 | 0.2422 | 0.3461 |
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+ | No log | 15.0 | 165 | 0.1869 | 0.833 | 0.2438 | 0.3494 |
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+ | No log | 16.0 | 176 | 0.1826 | 0.833 | 0.2438 | 0.3494 |
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+ | No log | 17.0 | 187 | 0.1797 | 0.8247 | 0.2421 | 0.3468 |
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+ | No log | 18.0 | 198 | 0.1749 | 0.8333 | 0.2449 | 0.3509 |
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+ | No log | 19.0 | 209 | 0.1726 | 0.8373 | 0.2478 | 0.3536 |
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+ | No log | 20.0 | 220 | 0.1716 | 0.8373 | 0.2451 | 0.3518 |
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+ | No log | 21.0 | 231 | 0.1695 | 0.8472 | 0.2467 | 0.3542 |
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+ | No log | 22.0 | 242 | 0.1693 | 0.8452 | 0.249 | 0.357 |
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+ | No log | 23.0 | 253 | 0.1685 | 0.875 | 0.2685 | 0.3784 |
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+ | No log | 24.0 | 264 | 0.1668 | 0.8552 | 0.2587 | 0.3644 |
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+ | No log | 25.0 | 275 | 0.1641 | 0.8571 | 0.2492 | 0.357 |
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+ | No log | 26.0 | 286 | 0.1628 | 0.869 | 0.2602 | 0.3687 |
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+ | No log | 27.0 | 297 | 0.1617 | 0.8651 | 0.2594 | 0.3671 |
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+ | No log | 28.0 | 308 | 0.1611 | 0.8651 | 0.2594 | 0.3671 |
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+ | No log | 29.0 | 319 | 0.1611 | 0.8651 | 0.2594 | 0.3671 |
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+ | No log | 30.0 | 330 | 0.1611 | 0.8651 | 0.2594 | 0.3671 |
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  ### Framework versions