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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: 0.1260
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- - Rouge2 Precision: 0.7647
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- - Rouge2 Recall: 0.1682
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- - Rouge2 Fmeasure: 0.2715
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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 | 0.6880 | 0.5449 | 0.1342 | 0.2115 |
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- | No log | 2.0 | 136 | 0.3651 | 0.6371 | 0.1381 | 0.2242 |
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- | No log | 3.0 | 204 | 0.2780 | 0.6573 | 0.143 | 0.2322 |
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- | No log | 4.0 | 272 | 0.2347 | 0.6729 | 0.144 | 0.2342 |
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- | No log | 5.0 | 340 | 0.2160 | 0.7186 | 0.1544 | 0.2505 |
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- | No log | 6.0 | 408 | 0.1963 | 0.7025 | 0.1523 | 0.2465 |
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- | No log | 7.0 | 476 | 0.1797 | 0.7246 | 0.1564 | 0.2536 |
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- | 0.5593 | 8.0 | 544 | 0.1781 | 0.7285 | 0.1551 | 0.2521 |
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- | 0.5593 | 9.0 | 612 | 0.1674 | 0.7353 | 0.1583 | 0.2564 |
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- | 0.5593 | 10.0 | 680 | 0.1622 | 0.7412 | 0.1585 | 0.2572 |
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- | 0.5593 | 11.0 | 748 | 0.1525 | 0.7274 | 0.1559 | 0.2528 |
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- | 0.5593 | 12.0 | 816 | 0.1542 | 0.7399 | 0.158 | 0.2565 |
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- | 0.5593 | 13.0 | 884 | 0.1462 | 0.7575 | 0.1642 | 0.2655 |
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- | 0.5593 | 14.0 | 952 | 0.1453 | 0.7599 | 0.1632 | 0.265 |
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- | 0.1388 | 15.0 | 1020 | 0.1415 | 0.7601 | 0.1635 | 0.2654 |
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- | 0.1388 | 16.0 | 1088 | 0.1404 | 0.7622 | 0.1644 | 0.2663 |
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- | 0.1388 | 17.0 | 1156 | 0.1349 | 0.7673 | 0.1646 | 0.2673 |
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- | 0.1388 | 18.0 | 1224 | 0.1325 | 0.758 | 0.1624 | 0.264 |
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- | 0.1388 | 19.0 | 1292 | 0.1347 | 0.7713 | 0.1691 | 0.2731 |
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- | 0.1388 | 20.0 | 1360 | 0.1343 | 0.7664 | 0.1679 | 0.2711 |
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- | 0.1388 | 21.0 | 1428 | 0.1333 | 0.7547 | 0.1631 | 0.2644 |
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- | 0.1388 | 22.0 | 1496 | 0.1315 | 0.7649 | 0.1662 | 0.2687 |
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- | 0.0973 | 23.0 | 1564 | 0.1297 | 0.7615 | 0.1659 | 0.2683 |
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- | 0.0973 | 24.0 | 1632 | 0.1295 | 0.7588 | 0.1657 | 0.2679 |
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- | 0.0973 | 25.0 | 1700 | 0.1289 | 0.7636 | 0.1662 | 0.2692 |
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- | 0.0973 | 26.0 | 1768 | 0.1282 | 0.7689 | 0.1671 | 0.2705 |
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- | 0.0973 | 27.0 | 1836 | 0.1273 | 0.7675 | 0.1684 | 0.272 |
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- | 0.0973 | 28.0 | 1904 | 0.1270 | 0.7652 | 0.1672 | 0.2703 |
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- | 0.0973 | 29.0 | 1972 | 0.1260 | 0.7647 | 0.1682 | 0.2715 |
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- | 0.0813 | 30.0 | 2040 | 0.1260 | 0.7647 | 0.1682 | 0.2715 |
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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: 0.0733
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+ - Rouge2 Precision: 0.913
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+ - Rouge2 Recall: 0.4038
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+ - Rouge2 Fmeasure: 0.5257
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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 | 1.9724 | 0.0946 | 0.0257 | 0.0385 |
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+ | No log | 2.0 | 22 | 1.2952 | 0.0925 | 0.0263 | 0.039 |
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+ | No log | 3.0 | 33 | 0.8639 | 0.1083 | 0.0263 | 0.0409 |
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+ | No log | 4.0 | 44 | 0.5684 | 0.2212 | 0.0923 | 0.1248 |
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+ | No log | 5.0 | 55 | 0.3793 | 0.4327 | 0.1813 | 0.2445 |
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+ | No log | 6.0 | 66 | 0.2920 | 0.6667 | 0.3112 | 0.4008 |
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+ | No log | 7.0 | 77 | 0.2351 | 0.7234 | 0.309 | 0.409 |
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+ | No log | 8.0 | 88 | 0.2055 | 0.7374 | 0.3117 | 0.414 |
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+ | No log | 9.0 | 99 | 0.1787 | 0.7306 | 0.3002 | 0.4029 |
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+ | No log | 10.0 | 110 | 0.1540 | 0.7773 | 0.3267 | 0.4352 |
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+ | No log | 11.0 | 121 | 0.1406 | 0.7676 | 0.319 | 0.4246 |
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+ | No log | 12.0 | 132 | 0.1299 | 0.8478 | 0.3723 | 0.4887 |
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+ | No log | 13.0 | 143 | 0.1172 | 0.8202 | 0.3533 | 0.4675 |
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+ | No log | 14.0 | 154 | 0.1133 | 0.8543 | 0.3802 | 0.4976 |
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+ | No log | 15.0 | 165 | 0.1049 | 0.8543 | 0.3802 | 0.4976 |
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+ | No log | 16.0 | 176 | 0.0988 | 0.8252 | 0.3448 | 0.4603 |
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+ | No log | 17.0 | 187 | 0.0921 | 0.8702 | 0.385 | 0.5037 |
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+ | No log | 18.0 | 198 | 0.0877 | 0.8591 | 0.3684 | 0.4878 |
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+ | No log | 19.0 | 209 | 0.0878 | 0.8591 | 0.3654 | 0.4849 |
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+ | No log | 20.0 | 220 | 0.0849 | 0.8591 | 0.3654 | 0.4849 |
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+ | No log | 21.0 | 231 | 0.0806 | 0.882 | 0.3761 | 0.4974 |
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+ | No log | 22.0 | 242 | 0.0791 | 0.9232 | 0.4083 | 0.532 |
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+ | No log | 23.0 | 253 | 0.0794 | 0.8974 | 0.3869 | 0.5098 |
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+ | No log | 24.0 | 264 | 0.0773 | 0.9198 | 0.4104 | 0.5338 |
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+ | No log | 25.0 | 275 | 0.0744 | 0.9232 | 0.4083 | 0.532 |
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+ | No log | 26.0 | 286 | 0.0735 | 0.9232 | 0.4083 | 0.532 |
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+ | No log | 27.0 | 297 | 0.0742 | 0.9272 | 0.4115 | 0.5359 |
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+ | No log | 28.0 | 308 | 0.0740 | 0.913 | 0.4038 | 0.5257 |
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+ | No log | 29.0 | 319 | 0.0734 | 0.913 | 0.4038 | 0.5257 |
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+ | No log | 30.0 | 330 | 0.0733 | 0.913 | 0.4038 | 0.5257 |
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  ### Framework versions