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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: 0.2426
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- - Rouge2 Precision: 0.7507
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- - Rouge2 Recall: 0.2125
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- - Rouge2 Fmeasure: 0.33
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  ## Model description
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@@ -42,32 +42,42 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 20
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  ### Training results
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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 | 0.9486 | 0.583 | 0.1837 | 0.2781 |
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- | No log | 2.0 | 136 | 0.5544 | 0.6286 | 0.1739 | 0.2718 |
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- | No log | 3.0 | 204 | 0.4408 | 0.6653 | 0.1854 | 0.2892 |
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- | No log | 4.0 | 272 | 0.3656 | 0.6999 | 0.1959 | 0.3048 |
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- | No log | 5.0 | 340 | 0.3403 | 0.7304 | 0.2022 | 0.3157 |
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- | No log | 6.0 | 408 | 0.3162 | 0.7358 | 0.2044 | 0.3188 |
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- | No log | 7.0 | 476 | 0.2889 | 0.7331 | 0.2033 | 0.317 |
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- | 0.7496 | 8.0 | 544 | 0.2918 | 0.7219 | 0.2013 | 0.3137 |
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- | 0.7496 | 9.0 | 612 | 0.2745 | 0.73 | 0.2045 | 0.3182 |
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- | 0.7496 | 10.0 | 680 | 0.2699 | 0.7365 | 0.204 | 0.3185 |
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- | 0.7496 | 11.0 | 748 | 0.2626 | 0.7418 | 0.2071 | 0.3228 |
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- | 0.7496 | 12.0 | 816 | 0.2623 | 0.7316 | 0.2059 | 0.3204 |
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- | 0.7496 | 13.0 | 884 | 0.2513 | 0.7476 | 0.21 | 0.3269 |
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- | 0.7496 | 14.0 | 952 | 0.2507 | 0.7417 | 0.2109 | 0.3275 |
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- | 0.2318 | 15.0 | 1020 | 0.2457 | 0.7378 | 0.2071 | 0.3224 |
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- | 0.2318 | 16.0 | 1088 | 0.2414 | 0.7384 | 0.2077 | 0.323 |
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- | 0.2318 | 17.0 | 1156 | 0.2420 | 0.7555 | 0.2134 | 0.3316 |
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- | 0.2318 | 18.0 | 1224 | 0.2439 | 0.7493 | 0.2104 | 0.3273 |
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- | 0.2318 | 19.0 | 1292 | 0.2415 | 0.7514 | 0.2121 | 0.3296 |
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- | 0.2318 | 20.0 | 1360 | 0.2426 | 0.7507 | 0.2125 | 0.33 |
 
 
 
 
 
 
 
 
 
 
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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: 0.1265
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+ - Rouge2 Precision: 0.7737
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+ - Rouge2 Recall: 0.1686
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+ - Rouge2 Fmeasure: 0.2725
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 30
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  ### Training results
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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 | 0.7014 | 0.5788 | 0.1438 | 0.2267 |
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+ | No log | 2.0 | 136 | 0.3643 | 0.6424 | 0.1388 | 0.2254 |
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+ | No log | 3.0 | 204 | 0.2811 | 0.6499 | 0.1433 | 0.2319 |
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+ | No log | 4.0 | 272 | 0.2344 | 0.6552 | 0.1401 | 0.2276 |
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+ | No log | 5.0 | 340 | 0.2165 | 0.7083 | 0.1534 | 0.2485 |
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+ | No log | 6.0 | 408 | 0.1950 | 0.6835 | 0.1498 | 0.2421 |
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+ | No log | 7.0 | 476 | 0.1801 | 0.7303 | 0.1584 | 0.2562 |
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+ | 0.5608 | 8.0 | 544 | 0.1784 | 0.7246 | 0.1565 | 0.2537 |
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+ | 0.5608 | 9.0 | 612 | 0.1670 | 0.7308 | 0.1575 | 0.2552 |
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+ | 0.5608 | 10.0 | 680 | 0.1619 | 0.7388 | 0.1587 | 0.2572 |
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+ | 0.5608 | 11.0 | 748 | 0.1545 | 0.724 | 0.1551 | 0.2518 |
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+ | 0.5608 | 12.0 | 816 | 0.1554 | 0.7271 | 0.1563 | 0.2534 |
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+ | 0.5608 | 13.0 | 884 | 0.1472 | 0.7404 | 0.1624 | 0.2624 |
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+ | 0.5608 | 14.0 | 952 | 0.1472 | 0.7508 | 0.1613 | 0.2616 |
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+ | 0.1384 | 15.0 | 1020 | 0.1429 | 0.765 | 0.1651 | 0.2679 |
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+ | 0.1384 | 16.0 | 1088 | 0.1416 | 0.7564 | 0.1626 | 0.2636 |
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+ | 0.1384 | 17.0 | 1156 | 0.1365 | 0.7737 | 0.1653 | 0.2684 |
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+ | 0.1384 | 18.0 | 1224 | 0.1344 | 0.7594 | 0.1639 | 0.2656 |
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+ | 0.1384 | 19.0 | 1292 | 0.1356 | 0.7618 | 0.1667 | 0.2693 |
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+ | 0.1384 | 20.0 | 1360 | 0.1339 | 0.7691 | 0.1673 | 0.2705 |
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+ | 0.1384 | 21.0 | 1428 | 0.1335 | 0.7624 | 0.1646 | 0.267 |
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+ | 0.1384 | 22.0 | 1496 | 0.1325 | 0.7691 | 0.1664 | 0.2694 |
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+ | 0.0974 | 23.0 | 1564 | 0.1297 | 0.7612 | 0.1657 | 0.2679 |
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+ | 0.0974 | 24.0 | 1632 | 0.1296 | 0.7628 | 0.1663 | 0.2691 |
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+ | 0.0974 | 25.0 | 1700 | 0.1288 | 0.7601 | 0.1648 | 0.2667 |
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+ | 0.0974 | 26.0 | 1768 | 0.1284 | 0.7633 | 0.1653 | 0.2679 |
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+ | 0.0974 | 27.0 | 1836 | 0.1272 | 0.7684 | 0.1663 | 0.2696 |
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+ | 0.0974 | 28.0 | 1904 | 0.1272 | 0.7729 | 0.1683 | 0.2722 |
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+ | 0.0974 | 29.0 | 1972 | 0.1265 | 0.7765 | 0.169 | 0.2732 |
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+ | 0.081 | 30.0 | 2040 | 0.1265 | 0.7737 | 0.1686 | 0.2725 |
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  ### Framework versions