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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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@@ -16,8 +16,8 @@ This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base)
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0713
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  - Rouge2 Precision: 0.8988
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- - Rouge2 Recall: 0.2683
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- - Rouge2 Fmeasure: 0.3801
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  ## Model description
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@@ -49,35 +49,35 @@ 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 | 0.7076 | 0.0068 | 0.0008 | 0.0014 |
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- | No log | 2.0 | 22 | 0.3632 | 0.2242 | 0.0621 | 0.092 |
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- | No log | 3.0 | 33 | 0.2298 | 0.5528 | 0.1846 | 0.2602 |
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- | No log | 4.0 | 44 | 0.1671 | 0.7674 | 0.2365 | 0.3316 |
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- | No log | 5.0 | 55 | 0.1435 | 0.8177 | 0.2505 | 0.351 |
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- | No log | 6.0 | 66 | 0.1280 | 0.8131 | 0.2541 | 0.3556 |
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- | No log | 7.0 | 77 | 0.1171 | 0.8294 | 0.2493 | 0.3543 |
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- | No log | 8.0 | 88 | 0.1070 | 0.8373 | 0.2624 | 0.3675 |
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- | No log | 9.0 | 99 | 0.1017 | 0.8829 | 0.2672 | 0.3787 |
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- | No log | 10.0 | 110 | 0.0958 | 0.8801 | 0.2651 | 0.3754 |
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- | No log | 11.0 | 121 | 0.0912 | 0.881 | 0.2657 | 0.3766 |
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- | No log | 12.0 | 132 | 0.0873 | 0.8849 | 0.2661 | 0.3775 |
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- | No log | 13.0 | 143 | 0.0850 | 0.8889 | 0.2678 | 0.3789 |
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- | No log | 14.0 | 154 | 0.0840 | 0.8948 | 0.2678 | 0.379 |
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- | No log | 15.0 | 165 | 0.0818 | 0.9008 | 0.2689 | 0.3818 |
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- | No log | 16.0 | 176 | 0.0813 | 0.8948 | 0.2678 | 0.379 |
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- | No log | 17.0 | 187 | 0.0768 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 18.0 | 198 | 0.0755 | 0.881 | 0.2645 | 0.3743 |
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- | No log | 19.0 | 209 | 0.0761 | 0.8948 | 0.2678 | 0.379 |
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- | No log | 20.0 | 220 | 0.0765 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 21.0 | 231 | 0.0732 | 0.9048 | 0.2695 | 0.3826 |
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- | No log | 22.0 | 242 | 0.0731 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 23.0 | 253 | 0.0734 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 24.0 | 264 | 0.0731 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 25.0 | 275 | 0.0709 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 26.0 | 286 | 0.0709 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 27.0 | 297 | 0.0711 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 28.0 | 308 | 0.0716 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 29.0 | 319 | 0.0715 | 0.8988 | 0.2683 | 0.3801 |
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- | No log | 30.0 | 330 | 0.0713 | 0.8988 | 0.2683 | 0.3801 |
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  ### Framework versions
 
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0713
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  - Rouge2 Precision: 0.8988
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+ - Rouge2 Recall: 0.269
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+ - Rouge2 Fmeasure: 0.3814
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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 | 0.7076 | 0.0068 | 0.0008 | 0.0014 |
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+ | No log | 2.0 | 22 | 0.3632 | 0.2268 | 0.0628 | 0.0938 |
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+ | No log | 3.0 | 33 | 0.2298 | 0.5578 | 0.1857 | 0.2626 |
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+ | No log | 4.0 | 44 | 0.1671 | 0.7623 | 0.2369 | 0.3318 |
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+ | No log | 5.0 | 55 | 0.1435 | 0.817 | 0.2519 | 0.3542 |
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+ | No log | 6.0 | 66 | 0.1280 | 0.813 | 0.2555 | 0.3576 |
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+ | No log | 7.0 | 77 | 0.1171 | 0.8302 | 0.2509 | 0.3566 |
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+ | No log | 8.0 | 88 | 0.1070 | 0.836 | 0.2634 | 0.3692 |
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+ | No log | 9.0 | 99 | 0.1017 | 0.8824 | 0.2677 | 0.3798 |
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+ | No log | 10.0 | 110 | 0.0958 | 0.8802 | 0.2654 | 0.3762 |
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+ | No log | 11.0 | 121 | 0.0912 | 0.881 | 0.2662 | 0.3774 |
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+ | No log | 12.0 | 132 | 0.0873 | 0.8829 | 0.2671 | 0.3782 |
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+ | No log | 13.0 | 143 | 0.0850 | 0.8889 | 0.268 | 0.3801 |
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+ | No log | 14.0 | 154 | 0.0840 | 0.8948 | 0.268 | 0.3802 |
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+ | No log | 15.0 | 165 | 0.0818 | 0.9008 | 0.2696 | 0.382 |
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+ | No log | 16.0 | 176 | 0.0813 | 0.8948 | 0.268 | 0.3802 |
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+ | No log | 17.0 | 187 | 0.0768 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 18.0 | 198 | 0.0755 | 0.8829 | 0.2661 | 0.375 |
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+ | No log | 19.0 | 209 | 0.0761 | 0.8948 | 0.268 | 0.3802 |
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+ | No log | 20.0 | 220 | 0.0765 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 21.0 | 231 | 0.0732 | 0.9048 | 0.2707 | 0.3836 |
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+ | No log | 22.0 | 242 | 0.0731 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 23.0 | 253 | 0.0734 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 24.0 | 264 | 0.0731 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 25.0 | 275 | 0.0709 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 26.0 | 286 | 0.0709 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 27.0 | 297 | 0.0711 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 28.0 | 308 | 0.0716 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 29.0 | 319 | 0.0715 | 0.8988 | 0.269 | 0.3814 |
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+ | No log | 30.0 | 330 | 0.0713 | 0.8988 | 0.269 | 0.3814 |
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  ### Framework versions