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t5-test2sql
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1207
- Rouge2 Precision: 0.9214
- Rouge2 Recall: 0.4259
- Rouge2 Fmeasure: 0.5578
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|---|---|---|---|---|---|---|
| No log | 1.0 | 11 | 2.7293 | 0.1012 | 0.0305 | 0.0453 |
| No log | 2.0 | 22 | 1.9009 | 0.0937 | 0.0292 | 0.0427 |
| No log | 3.0 | 33 | 1.3525 | 0.1002 | 0.0349 | 0.0502 |
| No log | 4.0 | 44 | 0.8837 | 0.1462 | 0.0529 | 0.0744 |
| No log | 5.0 | 55 | 0.6460 | 0.5546 | 0.2531 | 0.3371 |
| No log | 6.0 | 66 | 0.5050 | 0.729 | 0.3571 | 0.4631 |
| No log | 7.0 | 77 | 0.4239 | 0.6944 | 0.3048 | 0.4088 |
| No log | 8.0 | 88 | 0.3799 | 0.7868 | 0.3674 | 0.4807 |
| No log | 9.0 | 99 | 0.3405 | 0.7266 | 0.3126 | 0.4213 |
| No log | 10.0 | 110 | 0.3055 | 0.8447 | 0.3876 | 0.5104 |
| No log | 11.0 | 121 | 0.2741 | 0.8546 | 0.3955 | 0.5201 |
| No log | 12.0 | 132 | 0.2605 | 0.8676 | 0.4049 | 0.5308 |
| No log | 13.0 | 143 | 0.2446 | 0.8424 | 0.3814 | 0.5047 |
| No log | 14.0 | 154 | 0.2287 | 0.8659 | 0.3945 | 0.5238 |
| No log | 15.0 | 165 | 0.2209 | 0.9064 | 0.4273 | 0.556 |
| No log | 16.0 | 176 | 0.1990 | 0.888 | 0.409 | 0.5383 |
| No log | 17.0 | 187 | 0.1941 | 0.9118 | 0.4305 | 0.5602 |
| No log | 18.0 | 198 | 0.1785 | 0.9118 | 0.4305 | 0.5602 |
| No log | 19.0 | 209 | 0.1669 | 0.919 | 0.4324 | 0.5636 |
| No log | 20.0 | 220 | 0.1749 | 0.9138 | 0.4289 | 0.5608 |
| No log | 21.0 | 231 | 0.1598 | 0.9047 | 0.4248 | 0.556 |
| No log | 22.0 | 242 | 0.1501 | 0.9098 | 0.4294 | 0.5596 |
| No log | 23.0 | 253 | 0.1456 | 0.9138 | 0.4307 | 0.5618 |
| No log | 24.0 | 264 | 0.1419 | 0.893 | 0.4185 | 0.5467 |
| No log | 25.0 | 275 | 0.1359 | 0.9005 | 0.4212 | 0.55 |
| No log | 26.0 | 286 | 0.1338 | 0.8979 | 0.4212 | 0.5494 |
| No log | 27.0 | 297 | 0.1319 | 0.9005 | 0.4212 | 0.55 |
| No log | 28.0 | 308 | 0.1325 | 0.9005 | 0.4212 | 0.55 |
| No log | 29.0 | 319 | 0.1335 | 0.9093 | 0.4231 | 0.5529 |
| No log | 30.0 | 330 | 0.1240 | 0.9093 | 0.4231 | 0.5529 |
| No log | 31.0 | 341 | 0.1222 | 0.9053 | 0.4231 | 0.5527 |
| No log | 32.0 | 352 | 0.1265 | 0.9214 | 0.4259 | 0.5578 |
| No log | 33.0 | 363 | 0.1286 | 0.9214 | 0.4259 | 0.5578 |
| No log | 34.0 | 374 | 0.1283 | 0.9214 | 0.4259 | 0.5578 |
| No log | 35.0 | 385 | 0.1279 | 0.9214 | 0.4259 | 0.5578 |
| No log | 36.0 | 396 | 0.1285 | 0.9214 | 0.4259 | 0.5578 |
| No log | 37.0 | 407 | 0.1291 | 0.9093 | 0.4231 | 0.5529 |
| No log | 38.0 | 418 | 0.1270 | 0.9093 | 0.4231 | 0.5529 |
| No log | 39.0 | 429 | 0.1225 | 0.9093 | 0.4231 | 0.5529 |
| No log | 40.0 | 440 | 0.1205 | 0.9093 | 0.4231 | 0.5529 |
| No log | 41.0 | 451 | 0.1210 | 0.9093 | 0.4231 | 0.5529 |
| No log | 42.0 | 462 | 0.1230 | 0.9093 | 0.4231 | 0.5529 |
| No log | 43.0 | 473 | 0.1250 | 0.9093 | 0.4231 | 0.5529 |
| No log | 44.0 | 484 | 0.1223 | 0.9214 | 0.4259 | 0.5578 |
| No log | 45.0 | 495 | 0.1226 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 46.0 | 506 | 0.1213 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 47.0 | 517 | 0.1205 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 48.0 | 528 | 0.1203 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 49.0 | 539 | 0.1206 | 0.9214 | 0.4259 | 0.5578 |
| 0.5006 | 50.0 | 550 | 0.1207 | 0.9214 | 0.4259 | 0.5578 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
