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t5-text2sql

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PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
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update model card README.md

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@@ -15,9 +15,9 @@ 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: 0.9143
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- - Rouge2 Recall: 0.425
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- - Rouge2 Fmeasure: 0.556
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  ## Model description
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@@ -48,41 +48,41 @@ 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 | 2.7478 | 0.0912 | 0.028 | 0.0411 |
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- | No log | 2.0 | 22 | 1.9795 | 0.0963 | 0.0283 | 0.0423 |
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- | No log | 3.0 | 33 | 1.4496 | 0.1011 | 0.0283 | 0.0432 |
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- | No log | 4.0 | 44 | 0.9816 | 0.0869 | 0.0275 | 0.0407 |
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- | No log | 5.0 | 55 | 0.7077 | 0.3471 | 0.1353 | 0.1886 |
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- | No log | 6.0 | 66 | 0.5237 | 0.6686 | 0.3307 | 0.4264 |
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- | No log | 7.0 | 77 | 0.4239 | 0.722 | 0.3185 | 0.4243 |
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- | No log | 8.0 | 88 | 0.3717 | 0.8005 | 0.3625 | 0.4794 |
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- | No log | 9.0 | 99 | 0.3335 | 0.7724 | 0.3419 | 0.4566 |
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- | No log | 10.0 | 110 | 0.3048 | 0.7861 | 0.3466 | 0.4636 |
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- | No log | 11.0 | 121 | 0.2738 | 0.8176 | 0.3659 | 0.4857 |
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- | No log | 12.0 | 132 | 0.2596 | 0.845 | 0.3873 | 0.5112 |
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- | No log | 13.0 | 143 | 0.2432 | 0.8231 | 0.3693 | 0.4885 |
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- | No log | 14.0 | 154 | 0.2356 | 0.8516 | 0.3879 | 0.5113 |
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- | No log | 15.0 | 165 | 0.2272 | 0.8667 | 0.3887 | 0.5141 |
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- | No log | 16.0 | 176 | 0.2077 | 0.875 | 0.3907 | 0.5174 |
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- | No log | 17.0 | 187 | 0.1991 | 0.8754 | 0.3921 | 0.5186 |
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- | No log | 18.0 | 198 | 0.1928 | 0.886 | 0.401 | 0.5307 |
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- | No log | 19.0 | 209 | 0.1828 | 0.886 | 0.401 | 0.5307 |
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- | No log | 20.0 | 220 | 0.1820 | 0.8707 | 0.3887 | 0.5165 |
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- | No log | 21.0 | 231 | 0.1786 | 0.89 | 0.4036 | 0.5336 |
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- | No log | 22.0 | 242 | 0.1761 | 0.9039 | 0.4138 | 0.5438 |
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- | No log | 23.0 | 253 | 0.1710 | 0.9039 | 0.4138 | 0.5438 |
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- | No log | 24.0 | 264 | 0.1655 | 0.9143 | 0.425 | 0.556 |
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- | No log | 25.0 | 275 | 0.1622 | 0.9143 | 0.425 | 0.556 |
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- | No log | 26.0 | 286 | 0.1597 | 0.9143 | 0.425 | 0.556 |
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- | No log | 27.0 | 297 | 0.1579 | 0.9143 | 0.425 | 0.556 |
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- | No log | 28.0 | 308 | 0.1565 | 0.9143 | 0.425 | 0.556 |
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- | No log | 29.0 | 319 | 0.1564 | 0.9143 | 0.425 | 0.556 |
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- | No log | 30.0 | 330 | 0.1562 | 0.9143 | 0.425 | 0.556 |
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  ### Framework versions
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  - Transformers 4.20.1
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- - Pytorch 1.11.0+cu113
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  - Datasets 2.3.2
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  - Tokenizers 0.12.1
 
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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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  | 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
84
 
85
  - Transformers 4.20.1
86
+ - Pytorch 1.12.0+cu113
87
  - Datasets 2.3.2
88
  - Tokenizers 0.12.1