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t5-text2sql_v1

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: 0.1245
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- - Rouge2 Precision: 0.7634
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- - Rouge2 Recall: 0.1643
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- - Rouge2 Fmeasure: 0.2668
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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 | 68 | 0.7074 | 0.5932 | 0.1448 | 0.2296 |
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- | No log | 2.0 | 136 | 0.3721 | 0.6346 | 0.1387 | 0.225 |
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- | No log | 3.0 | 204 | 0.2772 | 0.6492 | 0.1436 | 0.2322 |
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- | No log | 4.0 | 272 | 0.2343 | 0.6778 | 0.1452 | 0.2358 |
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- | No log | 5.0 | 340 | 0.2119 | 0.7235 | 0.1533 | 0.2495 |
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- | No log | 6.0 | 408 | 0.1922 | 0.7267 | 0.1583 | 0.2556 |
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- | No log | 7.0 | 476 | 0.1807 | 0.7299 | 0.1575 | 0.2551 |
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- | 0.5699 | 8.0 | 544 | 0.1772 | 0.7163 | 0.1541 | 0.25 |
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- | 0.5699 | 9.0 | 612 | 0.1612 | 0.729 | 0.156 | 0.2533 |
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- | 0.5699 | 10.0 | 680 | 0.1610 | 0.7354 | 0.1563 | 0.2541 |
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- | 0.5699 | 11.0 | 748 | 0.1534 | 0.7397 | 0.158 | 0.2566 |
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- | 0.5699 | 12.0 | 816 | 0.1483 | 0.7497 | 0.1602 | 0.2601 |
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- | 0.5699 | 13.0 | 884 | 0.1456 | 0.7579 | 0.1664 | 0.2684 |
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- | 0.5699 | 14.0 | 952 | 0.1430 | 0.7528 | 0.161 | 0.2615 |
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- | 0.1382 | 15.0 | 1020 | 0.1383 | 0.7492 | 0.1624 | 0.2632 |
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- | 0.1382 | 16.0 | 1088 | 0.1386 | 0.7525 | 0.1623 | 0.263 |
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- | 0.1382 | 17.0 | 1156 | 0.1357 | 0.7644 | 0.1649 | 0.2674 |
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- | 0.1382 | 18.0 | 1224 | 0.1337 | 0.7396 | 0.1602 | 0.2599 |
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- | 0.1382 | 19.0 | 1292 | 0.1336 | 0.7498 | 0.1606 | 0.2609 |
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- | 0.1382 | 20.0 | 1360 | 0.1300 | 0.7529 | 0.1617 | 0.2626 |
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- | 0.1382 | 21.0 | 1428 | 0.1299 | 0.7522 | 0.1631 | 0.2645 |
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- | 0.1382 | 22.0 | 1496 | 0.1280 | 0.7585 | 0.1635 | 0.2654 |
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- | 0.0969 | 23.0 | 1564 | 0.1263 | 0.7601 | 0.1648 | 0.2669 |
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- | 0.0969 | 24.0 | 1632 | 0.1265 | 0.7683 | 0.1649 | 0.268 |
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- | 0.0969 | 25.0 | 1700 | 0.1263 | 0.7755 | 0.1677 | 0.2717 |
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- | 0.0969 | 26.0 | 1768 | 0.1251 | 0.7675 | 0.1653 | 0.2684 |
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- | 0.0969 | 27.0 | 1836 | 0.1243 | 0.7743 | 0.1684 | 0.2728 |
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- | 0.0969 | 28.0 | 1904 | 0.1247 | 0.7673 | 0.1656 | 0.2689 |
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- | 0.0969 | 29.0 | 1972 | 0.1245 | 0.7634 | 0.1643 | 0.2668 |
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- | 0.0807 | 30.0 | 2040 | 0.1245 | 0.7634 | 0.1643 | 0.2668 |
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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: 0.0772
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+ - Rouge2 Precision: 0.8835
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+ - Rouge2 Recall: 0.39
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+ - Rouge2 Fmeasure: 0.5088
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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 | 1.9420 | 0.0755 | 0.022 | 0.0323 |
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+ | No log | 2.0 | 22 | 1.2731 | 0.0912 | 0.0263 | 0.039 |
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+ | No log | 3.0 | 33 | 0.8717 | 0.0993 | 0.0284 | 0.0424 |
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+ | No log | 4.0 | 44 | 0.5705 | 0.1014 | 0.032 | 0.0464 |
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+ | No log | 5.0 | 55 | 0.3929 | 0.4151 | 0.1528 | 0.2149 |
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+ | No log | 6.0 | 66 | 0.2911 | 0.7778 | 0.351 | 0.4594 |
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+ | No log | 7.0 | 77 | 0.2290 | 0.781 | 0.3305 | 0.4395 |
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+ | No log | 8.0 | 88 | 0.1995 | 0.7381 | 0.2992 | 0.4018 |
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+ | No log | 9.0 | 99 | 0.1768 | 0.752 | 0.3147 | 0.4202 |
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+ | No log | 10.0 | 110 | 0.1554 | 0.7242 | 0.3136 | 0.412 |
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+ | No log | 11.0 | 121 | 0.1446 | 0.8128 | 0.3583 | 0.4694 |
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+ | No log | 12.0 | 132 | 0.1337 | 0.8194 | 0.3653 | 0.478 |
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+ | No log | 13.0 | 143 | 0.1264 | 0.8088 | 0.3564 | 0.4675 |
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+ | No log | 14.0 | 154 | 0.1170 | 0.8036 | 0.3502 | 0.462 |
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+ | No log | 15.0 | 165 | 0.1078 | 0.8851 | 0.3981 | 0.5188 |
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+ | No log | 16.0 | 176 | 0.1046 | 0.8716 | 0.3864 | 0.5054 |
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+ | No log | 17.0 | 187 | 0.1007 | 0.8753 | 0.3851 | 0.5042 |
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+ | No log | 18.0 | 198 | 0.0951 | 0.8756 | 0.3941 | 0.5126 |
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+ | No log | 19.0 | 209 | 0.0928 | 0.8414 | 0.3565 | 0.4708 |
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+ | No log | 20.0 | 220 | 0.0894 | 0.854 | 0.3642 | 0.4808 |
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+ | No log | 21.0 | 231 | 0.0863 | 0.8954 | 0.3954 | 0.5168 |
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+ | No log | 22.0 | 242 | 0.0832 | 0.888 | 0.3931 | 0.5122 |
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+ | No log | 23.0 | 253 | 0.0828 | 0.8835 | 0.39 | 0.5088 |
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+ | No log | 24.0 | 264 | 0.0820 | 0.8835 | 0.39 | 0.5088 |
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+ | No log | 25.0 | 275 | 0.0803 | 0.8835 | 0.39 | 0.5088 |
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+ | No log | 26.0 | 286 | 0.0792 | 0.8835 | 0.39 | 0.5088 |
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+ | No log | 27.0 | 297 | 0.0784 | 0.8761 | 0.3886 | 0.5066 |
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+ | No log | 28.0 | 308 | 0.0775 | 0.8835 | 0.39 | 0.5088 |
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+ | No log | 29.0 | 319 | 0.0772 | 0.8835 | 0.39 | 0.5088 |
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+ | No log | 30.0 | 330 | 0.0772 | 0.8835 | 0.39 | 0.5088 |
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  ### Framework versions