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t5-text2sql

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PyTorch
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t5
text2text-generation
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: 0.1562
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- - Rouge2 Precision: 0.9172
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- - Rouge2 Recall: 0.4266
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- - Rouge2 Fmeasure: 0.5587
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  ## Model description
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@@ -42,42 +42,44 @@ 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: 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 | 11 | 2.7478 | 0.0901 | 0.0276 | 0.0406 |
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- | No log | 2.0 | 22 | 1.9795 | 0.0959 | 0.0278 | 0.0414 |
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- | No log | 3.0 | 33 | 1.4496 | 0.1002 | 0.0278 | 0.0422 |
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- | No log | 4.0 | 44 | 0.9816 | 0.0862 | 0.0274 | 0.0406 |
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- | No log | 5.0 | 55 | 0.7077 | 0.3401 | 0.1359 | 0.1879 |
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- | No log | 6.0 | 66 | 0.5237 | 0.6672 | 0.3314 | 0.4265 |
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- | No log | 7.0 | 77 | 0.4239 | 0.7212 | 0.3184 | 0.4251 |
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- | No log | 8.0 | 88 | 0.3717 | 0.8019 | 0.3633 | 0.4812 |
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- | No log | 9.0 | 99 | 0.3335 | 0.7735 | 0.3444 | 0.4587 |
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- | No log | 10.0 | 110 | 0.3048 | 0.7876 | 0.3481 | 0.465 |
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- | No log | 11.0 | 121 | 0.2738 | 0.8181 | 0.3668 | 0.4874 |
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- | No log | 12.0 | 132 | 0.2596 | 0.8463 | 0.3876 | 0.5114 |
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- | No log | 13.0 | 143 | 0.2432 | 0.8227 | 0.3713 | 0.492 |
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- | No log | 14.0 | 154 | 0.2356 | 0.8519 | 0.3877 | 0.5114 |
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- | No log | 15.0 | 165 | 0.2272 | 0.868 | 0.3899 | 0.5161 |
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- | No log | 16.0 | 176 | 0.2077 | 0.8759 | 0.3925 | 0.5199 |
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- | No log | 17.0 | 187 | 0.1991 | 0.8761 | 0.3936 | 0.5214 |
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- | No log | 18.0 | 198 | 0.1928 | 0.8894 | 0.4042 | 0.5344 |
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- | No log | 19.0 | 209 | 0.1828 | 0.8894 | 0.4042 | 0.5344 |
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- | No log | 20.0 | 220 | 0.1820 | 0.8727 | 0.3908 | 0.519 |
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- | No log | 21.0 | 231 | 0.1786 | 0.8929 | 0.4063 | 0.5366 |
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- | No log | 22.0 | 242 | 0.1761 | 0.9059 | 0.4156 | 0.5478 |
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- | No log | 23.0 | 253 | 0.1710 | 0.9059 | 0.4156 | 0.5478 |
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- | No log | 24.0 | 264 | 0.1655 | 0.9172 | 0.4266 | 0.5587 |
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- | No log | 25.0 | 275 | 0.1622 | 0.9172 | 0.4266 | 0.5587 |
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- | No log | 26.0 | 286 | 0.1597 | 0.9172 | 0.4266 | 0.5587 |
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- | No log | 27.0 | 297 | 0.1579 | 0.9172 | 0.4266 | 0.5587 |
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- | No log | 28.0 | 308 | 0.1565 | 0.9172 | 0.4266 | 0.5587 |
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- | No log | 29.0 | 319 | 0.1564 | 0.9172 | 0.4266 | 0.5587 |
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- | No log | 30.0 | 330 | 0.1562 | 0.9172 | 0.4266 | 0.5587 |
 
 
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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: 0.1528
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+ - Rouge2 Precision: 0.9252
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+ - Rouge2 Recall: 0.4354
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+ - Rouge2 Fmeasure: 0.5687
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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: 32
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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 | 11 | 2.7311 | 0.0907 | 0.0278 | 0.0409 |
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+ | No log | 2.0 | 22 | 1.9749 | 0.0948 | 0.0281 | 0.0417 |
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+ | No log | 3.0 | 33 | 1.4801 | 0.0998 | 0.0281 | 0.0428 |
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+ | No log | 4.0 | 44 | 1.0439 | 0.0928 | 0.0266 | 0.0405 |
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+ | No log | 5.0 | 55 | 0.7436 | 0.2758 | 0.1199 | 0.1633 |
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+ | No log | 6.0 | 66 | 0.5619 | 0.6723 | 0.3182 | 0.4184 |
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+ | No log | 7.0 | 77 | 0.4470 | 0.6655 | 0.3125 | 0.4093 |
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+ | No log | 8.0 | 88 | 0.3851 | 0.762 | 0.3384 | 0.4508 |
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+ | No log | 9.0 | 99 | 0.3372 | 0.7611 | 0.33 | 0.443 |
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+ | No log | 10.0 | 110 | 0.3113 | 0.7754 | 0.3396 | 0.454 |
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+ | No log | 11.0 | 121 | 0.2832 | 0.7977 | 0.3486 | 0.4682 |
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+ | No log | 12.0 | 132 | 0.2703 | 0.8346 | 0.3786 | 0.5019 |
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+ | No log | 13.0 | 143 | 0.2519 | 0.8379 | 0.3849 | 0.5058 |
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+ | No log | 14.0 | 154 | 0.2411 | 0.856 | 0.3883 | 0.5116 |
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+ | No log | 15.0 | 165 | 0.2274 | 0.8701 | 0.4023 | 0.5275 |
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+ | No log | 16.0 | 176 | 0.2117 | 0.8773 | 0.4049 | 0.5312 |
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+ | No log | 17.0 | 187 | 0.2061 | 0.8841 | 0.4015 | 0.5296 |
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+ | No log | 18.0 | 198 | 0.1957 | 0.8894 | 0.4059 | 0.5349 |
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+ | No log | 19.0 | 209 | 0.1859 | 0.9125 | 0.4274 | 0.5584 |
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+ | No log | 20.0 | 220 | 0.1866 | 0.8914 | 0.4097 | 0.5385 |
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+ | No log | 21.0 | 231 | 0.1846 | 0.8957 | 0.4128 | 0.5423 |
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+ | No log | 22.0 | 242 | 0.1797 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 23.0 | 253 | 0.1730 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 24.0 | 264 | 0.1645 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 25.0 | 275 | 0.1612 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 26.0 | 286 | 0.1599 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 27.0 | 297 | 0.1570 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 28.0 | 308 | 0.1550 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 29.0 | 319 | 0.1544 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 30.0 | 330 | 0.1534 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 31.0 | 341 | 0.1529 | 0.9252 | 0.4354 | 0.5687 |
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+ | No log | 32.0 | 352 | 0.1528 | 0.9252 | 0.4354 | 0.5687 |
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  ### Framework versions