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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: 0.0739
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- - Rouge2 Precision: 0.9355
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- - Rouge2 Recall: 0.3211
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- - Rouge2 Fmeasure: 0.455
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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 | 11 | 2.1518 | 0.0531 | 0.0137 | 0.0212 |
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- | No log | 2.0 | 22 | 1.3941 | 0.0642 | 0.0183 | 0.0271 |
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- | No log | 3.0 | 33 | 0.9268 | 0.0619 | 0.0183 | 0.027 |
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- | No log | 4.0 | 44 | 0.5761 | 0.0424 | 0.015 | 0.0213 |
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- | No log | 5.0 | 55 | 0.3922 | 0.4683 | 0.1715 | 0.2361 |
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- | No log | 6.0 | 66 | 0.2948 | 0.6561 | 0.238 | 0.326 |
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- | No log | 7.0 | 77 | 0.2294 | 0.8072 | 0.2847 | 0.3958 |
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- | No log | 8.0 | 88 | 0.1959 | 0.7839 | 0.2851 | 0.3965 |
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- | No log | 9.0 | 99 | 0.1674 | 0.8266 | 0.2916 | 0.4074 |
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- | No log | 10.0 | 110 | 0.1488 | 0.8717 | 0.3057 | 0.428 |
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- | No log | 11.0 | 121 | 0.1328 | 0.8851 | 0.3178 | 0.4412 |
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- | No log | 12.0 | 132 | 0.1205 | 0.8887 | 0.3197 | 0.4453 |
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- | No log | 13.0 | 143 | 0.1133 | 0.8887 | 0.3197 | 0.4453 |
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- | No log | 14.0 | 154 | 0.1054 | 0.9124 | 0.3328 | 0.4595 |
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- | No log | 15.0 | 165 | 0.1000 | 0.9214 | 0.3352 | 0.4632 |
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- | No log | 16.0 | 176 | 0.0964 | 0.9133 | 0.3318 | 0.4598 |
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- | No log | 17.0 | 187 | 0.0957 | 0.9205 | 0.3325 | 0.4612 |
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- | No log | 18.0 | 198 | 0.0883 | 0.9405 | 0.3352 | 0.4663 |
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- | No log | 19.0 | 209 | 0.0845 | 0.9413 | 0.3336 | 0.4666 |
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- | No log | 20.0 | 220 | 0.0825 | 0.9172 | 0.3238 | 0.4532 |
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- | No log | 21.0 | 231 | 0.0803 | 0.9172 | 0.3238 | 0.4532 |
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- | No log | 22.0 | 242 | 0.0788 | 0.929 | 0.3301 | 0.4617 |
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- | No log | 23.0 | 253 | 0.0789 | 0.9463 | 0.3343 | 0.468 |
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- | No log | 24.0 | 264 | 0.0784 | 0.9355 | 0.3211 | 0.455 |
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- | No log | 25.0 | 275 | 0.0778 | 0.9355 | 0.3211 | 0.455 |
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- | No log | 26.0 | 286 | 0.0767 | 0.9355 | 0.3211 | 0.455 |
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- | No log | 27.0 | 297 | 0.0759 | 0.9355 | 0.3211 | 0.455 |
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- | No log | 28.0 | 308 | 0.0749 | 0.9355 | 0.3211 | 0.455 |
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- | No log | 29.0 | 319 | 0.0742 | 0.9355 | 0.3211 | 0.455 |
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- | No log | 30.0 | 330 | 0.0739 | 0.9355 | 0.3211 | 0.455 |
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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: 0.1245
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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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  | 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