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# t5-small-finetuned-text2log-finetuned-nl-to-fol

This model is a fine-tuned version of [mrm8488/t5-small-finetuned-text2log](https://huggingface.co/mrm8488/t5-small-finetuned-text2log) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0267
- Bleu: 36.0754
- Gen Len: 18.6964

## 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: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log        | 1.0   | 14   | 1.8310          | 1.6355  | 11.0    |
| No log        | 2.0   | 28   | 1.0849          | 18.3378 | 18.8661 |
| No log        | 3.0   | 42   | 0.8576          | 25.1889 | 18.7411 |
| No log        | 4.0   | 56   | 0.7132          | 26.484  | 18.7589 |
| No log        | 5.0   | 70   | 0.5993          | 26.645  | 18.7054 |
| No log        | 6.0   | 84   | 0.5101          | 26.1493 | 18.6964 |
| No log        | 7.0   | 98   | 0.4494          | 26.0154 | 18.7054 |
| No log        | 8.0   | 112  | 0.4026          | 28.7807 | 18.7054 |
| No log        | 9.0   | 126  | 0.3672          | 28.3994 | 18.7054 |
| No log        | 10.0  | 140  | 0.3403          | 28.6664 | 18.6429 |
| No log        | 11.0  | 154  | 0.3181          | 29.5906 | 18.7321 |
| No log        | 12.0  | 168  | 0.3000          | 30.4838 | 18.7857 |
| No log        | 13.0  | 182  | 0.2870          | 29.3458 | 18.6964 |
| No log        | 14.0  | 196  | 0.2731          | 28.5458 | 18.6429 |
| No log        | 15.0  | 210  | 0.2582          | 31.0445 | 18.6429 |
| No log        | 16.0  | 224  | 0.2491          | 31.14   | 18.6964 |
| No log        | 17.0  | 238  | 0.2367          | 30.9688 | 18.7054 |
| No log        | 18.0  | 252  | 0.2262          | 31.2437 | 18.7054 |
| No log        | 19.0  | 266  | 0.2206          | 30.6938 | 18.6518 |
| No log        | 20.0  | 280  | 0.2137          | 31.1833 | 18.7054 |
| No log        | 21.0  | 294  | 0.2043          | 31.1204 | 18.7054 |
| No log        | 22.0  | 308  | 0.1978          | 31.9392 | 18.7054 |
| No log        | 23.0  | 322  | 0.1915          | 31.9986 | 18.7054 |
| No log        | 24.0  | 336  | 0.1850          | 32.4128 | 18.7054 |
| No log        | 25.0  | 350  | 0.1789          | 32.2531 | 18.7054 |
| No log        | 26.0  | 364  | 0.1716          | 31.5966 | 18.7054 |
| No log        | 27.0  | 378  | 0.1670          | 31.4045 | 18.7054 |
| No log        | 28.0  | 392  | 0.1631          | 31.6554 | 18.7054 |
| No log        | 29.0  | 406  | 0.1575          | 31.6052 | 18.7054 |
| No log        | 30.0  | 420  | 0.1530          | 31.6284 | 18.7054 |
| No log        | 31.0  | 434  | 0.1500          | 31.6599 | 18.7054 |
| No log        | 32.0  | 448  | 0.1449          | 32.2263 | 18.7054 |
| No log        | 33.0  | 462  | 0.1409          | 31.7732 | 18.7054 |
| No log        | 34.0  | 476  | 0.1385          | 31.6335 | 18.7054 |
| No log        | 35.0  | 490  | 0.1323          | 31.5864 | 18.7054 |
| 0.5638        | 36.0  | 504  | 0.1300          | 31.8469 | 18.7054 |
| 0.5638        | 37.0  | 518  | 0.1294          | 31.571  | 18.6964 |
| 0.5638        | 38.0  | 532  | 0.1255          | 31.6656 | 18.6964 |
| 0.5638        | 39.0  | 546  | 0.1239          | 31.6699 | 18.6964 |
| 0.5638        | 40.0  | 560  | 0.1223          | 31.6207 | 18.6964 |
| 0.5638        | 41.0  | 574  | 0.1179          | 31.7496 | 18.6964 |
| 0.5638        | 42.0  | 588  | 0.1155          | 31.8694 | 18.6964 |
| 0.5638        | 43.0  | 602  | 0.1139          | 31.8661 | 18.6696 |
| 0.5638        | 44.0  | 616  | 0.1119          | 31.9764 | 18.6964 |
| 0.5638        | 45.0  | 630  | 0.1097          | 31.9264 | 18.6964 |
| 0.5638        | 46.0  | 644  | 0.1077          | 31.8732 | 18.6964 |
| 0.5638        | 47.0  | 658  | 0.1065          | 32.1597 | 18.6964 |
| 0.5638        | 48.0  | 672  | 0.1046          | 31.984  | 18.6964 |
| 0.5638        | 49.0  | 686  | 0.1059          | 32.0725 | 18.6964 |
| 0.5638        | 50.0  | 700  | 0.1009          | 31.9764 | 18.6964 |
| 0.5638        | 51.0  | 714  | 0.1010          | 31.9335 | 18.6964 |
| 0.5638        | 52.0  | 728  | 0.1007          | 31.9461 | 18.6964 |
| 0.5638        | 53.0  | 742  | 0.0983          | 32.3444 | 18.6964 |
| 0.5638        | 54.0  | 756  | 0.0948          | 32.2457 | 18.6964 |
| 0.5638        | 55.0  | 770  | 0.0925          | 32.37   | 18.6964 |
| 0.5638        | 56.0  | 784  | 0.0915          | 32.6063 | 18.6964 |
| 0.5638        | 57.0  | 798  | 0.0891          | 32.5387 | 18.6964 |
| 0.5638        | 58.0  | 812  | 0.0886          | 32.5844 | 18.6964 |
| 0.5638        | 59.0  | 826  | 0.0873          | 32.8077 | 18.6964 |
| 0.5638        | 60.0  | 840  | 0.0850          | 32.8999 | 18.6964 |
| 0.5638        | 61.0  | 854  | 0.0844          | 32.9431 | 18.6964 |
| 0.5638        | 62.0  | 868  | 0.0835          | 32.4755 | 18.6339 |
| 0.5638        | 63.0  | 882  | 0.0829          | 32.8512 | 18.6964 |
| 0.5638        | 64.0  | 896  | 0.0834          | 32.9652 | 18.6964 |
| 0.5638        | 65.0  | 910  | 0.0803          | 32.7494 | 18.6964 |
| 0.5638        | 66.0  | 924  | 0.0803          | 33.1091 | 18.6964 |
| 0.5638        | 67.0  | 938  | 0.0799          | 32.7754 | 18.6964 |
| 0.5638        | 68.0  | 952  | 0.0773          | 32.691  | 18.6964 |
| 0.5638        | 69.0  | 966  | 0.0766          | 32.6675 | 18.6964 |
| 0.5638        | 70.0  | 980  | 0.0757          | 32.8043 | 18.6964 |
| 0.5638        | 71.0  | 994  | 0.0747          | 33.0999 | 18.6964 |
| 0.1955        | 72.0  | 1008 | 0.0758          | 32.831  | 18.6964 |
| 0.1955        | 73.0  | 1022 | 0.0716          | 33.0565 | 18.6964 |
| 0.1955        | 74.0  | 1036 | 0.0720          | 32.9855 | 18.6964 |
| 0.1955        | 75.0  | 1050 | 0.0708          | 33.2507 | 18.6964 |
| 0.1955        | 76.0  | 1064 | 0.0693          | 33.3378 | 18.6964 |
| 0.1955        | 77.0  | 1078 | 0.0708          | 33.4991 | 18.6964 |
| 0.1955        | 78.0  | 1092 | 0.0680          | 33.6325 | 18.6964 |
| 0.1955        | 79.0  | 1106 | 0.0670          | 34.0041 | 18.6964 |
| 0.1955        | 80.0  | 1120 | 0.0665          | 33.8912 | 18.6964 |
| 0.1955        | 81.0  | 1134 | 0.0667          | 33.8682 | 18.6964 |
| 0.1955        | 82.0  | 1148 | 0.0669          | 33.9798 | 18.6964 |
| 0.1955        | 83.0  | 1162 | 0.0661          | 33.8137 | 18.6964 |
| 0.1955        | 84.0  | 1176 | 0.0640          | 34.0485 | 18.6964 |
| 0.1955        | 85.0  | 1190 | 0.0639          | 34.0046 | 18.6964 |
| 0.1955        | 86.0  | 1204 | 0.0628          | 33.9692 | 18.6964 |
| 0.1955        | 87.0  | 1218 | 0.0620          | 34.0327 | 18.6964 |
| 0.1955        | 88.0  | 1232 | 0.0633          | 33.6707 | 18.6964 |
| 0.1955        | 89.0  | 1246 | 0.0624          | 33.6019 | 18.6964 |
| 0.1955        | 90.0  | 1260 | 0.0602          | 33.7535 | 18.6964 |
| 0.1955        | 91.0  | 1274 | 0.0623          | 33.96   | 18.6964 |
| 0.1955        | 92.0  | 1288 | 0.0596          | 33.9758 | 18.6964 |
| 0.1955        | 93.0  | 1302 | 0.0575          | 34.0948 | 18.6964 |
| 0.1955        | 94.0  | 1316 | 0.0580          | 34.0769 | 18.6964 |
| 0.1955        | 95.0  | 1330 | 0.0564          | 34.0158 | 18.6964 |
| 0.1955        | 96.0  | 1344 | 0.0556          | 34.0563 | 18.6964 |
| 0.1955        | 97.0  | 1358 | 0.0554          | 33.9136 | 18.6964 |
| 0.1955        | 98.0  | 1372 | 0.0561          | 34.1803 | 18.6964 |
| 0.1955        | 99.0  | 1386 | 0.0528          | 34.1803 | 18.6964 |
| 0.1955        | 100.0 | 1400 | 0.0541          | 34.1803 | 18.6964 |
| 0.1955        | 101.0 | 1414 | 0.0586          | 34.2268 | 18.6964 |
| 0.1955        | 102.0 | 1428 | 0.0527          | 34.2268 | 18.6964 |
| 0.1955        | 103.0 | 1442 | 0.0521          | 34.2876 | 18.6964 |
| 0.1955        | 104.0 | 1456 | 0.0512          | 34.1363 | 18.6964 |
| 0.1955        | 105.0 | 1470 | 0.0505          | 34.1803 | 18.6964 |
| 0.1955        | 106.0 | 1484 | 0.0507          | 34.3964 | 18.6964 |
| 0.1955        | 107.0 | 1498 | 0.0531          | 34.3264 | 18.6964 |
| 0.1348        | 108.0 | 1512 | 0.0503          | 34.3964 | 18.6964 |
| 0.1348        | 109.0 | 1526 | 0.0479          | 34.2748 | 18.6964 |
| 0.1348        | 110.0 | 1540 | 0.0486          | 34.4116 | 18.6964 |
| 0.1348        | 111.0 | 1554 | 0.0469          | 34.4479 | 18.6964 |
| 0.1348        | 112.0 | 1568 | 0.0466          | 34.4479 | 18.6964 |
| 0.1348        | 113.0 | 1582 | 0.0459          | 34.6456 | 18.6964 |
| 0.1348        | 114.0 | 1596 | 0.0470          | 34.5772 | 18.6964 |
| 0.1348        | 115.0 | 1610 | 0.0474          | 34.7063 | 18.6964 |
| 0.1348        | 116.0 | 1624 | 0.0470          | 34.8006 | 18.6964 |
| 0.1348        | 117.0 | 1638 | 0.0446          | 34.8006 | 18.6964 |
| 0.1348        | 118.0 | 1652 | 0.0450          | 34.6814 | 18.6964 |
| 0.1348        | 119.0 | 1666 | 0.0448          | 34.6371 | 18.6964 |
| 0.1348        | 120.0 | 1680 | 0.0436          | 34.8505 | 18.6964 |
| 0.1348        | 121.0 | 1694 | 0.0436          | 34.8948 | 18.6964 |
| 0.1348        | 122.0 | 1708 | 0.0420          | 35.0312 | 18.6964 |
| 0.1348        | 123.0 | 1722 | 0.0414          | 35.0994 | 18.6964 |
| 0.1348        | 124.0 | 1736 | 0.0419          | 34.8656 | 18.6964 |
| 0.1348        | 125.0 | 1750 | 0.0410          | 35.3786 | 18.6964 |
| 0.1348        | 126.0 | 1764 | 0.0411          | 35.3786 | 18.6964 |
| 0.1348        | 127.0 | 1778 | 0.0420          | 35.3341 | 18.6964 |
| 0.1348        | 128.0 | 1792 | 0.0397          | 35.3341 | 18.6964 |
| 0.1348        | 129.0 | 1806 | 0.0391          | 35.3341 | 18.6964 |
| 0.1348        | 130.0 | 1820 | 0.0384          | 35.3341 | 18.6964 |
| 0.1348        | 131.0 | 1834 | 0.0383          | 35.4629 | 18.6964 |
| 0.1348        | 132.0 | 1848 | 0.0378          | 35.5074 | 18.6964 |
| 0.1348        | 133.0 | 1862 | 0.0371          | 35.5755 | 18.6964 |
| 0.1348        | 134.0 | 1876 | 0.0369          | 35.7042 | 18.6964 |
| 0.1348        | 135.0 | 1890 | 0.0367          | 35.7042 | 18.6964 |
| 0.1348        | 136.0 | 1904 | 0.0372          | 35.5847 | 18.6964 |
| 0.1348        | 137.0 | 1918 | 0.0364          | 35.8328 | 18.6964 |
| 0.1348        | 138.0 | 1932 | 0.0361          | 35.7881 | 18.6964 |
| 0.1348        | 139.0 | 1946 | 0.0358          | 35.7881 | 18.6964 |
| 0.1348        | 140.0 | 1960 | 0.0358          | 35.6669 | 18.6964 |
| 0.1348        | 141.0 | 1974 | 0.0365          | 35.7132 | 18.6964 |
| 0.1348        | 142.0 | 1988 | 0.0354          | 35.7648 | 18.6964 |
| 0.104         | 143.0 | 2002 | 0.0350          | 35.7648 | 18.6964 |
| 0.104         | 144.0 | 2016 | 0.0348          | 35.5989 | 18.6964 |
| 0.104         | 145.0 | 2030 | 0.0351          | 35.5543 | 18.6964 |
| 0.104         | 146.0 | 2044 | 0.0356          | 35.5543 | 18.6964 |
| 0.104         | 147.0 | 2058 | 0.0346          | 35.5989 | 18.6964 |
| 0.104         | 148.0 | 2072 | 0.0336          | 35.5989 | 18.6964 |
| 0.104         | 149.0 | 2086 | 0.0335          | 35.7648 | 18.6964 |
| 0.104         | 150.0 | 2100 | 0.0340          | 35.6669 | 18.6964 |
| 0.104         | 151.0 | 2114 | 0.0332          | 35.8328 | 18.6964 |
| 0.104         | 152.0 | 2128 | 0.0330          | 35.6669 | 18.6964 |
| 0.104         | 153.0 | 2142 | 0.0333          | 35.6669 | 18.6964 |
| 0.104         | 154.0 | 2156 | 0.0326          | 35.6669 | 18.6964 |
| 0.104         | 155.0 | 2170 | 0.0321          | 35.6669 | 18.6964 |
| 0.104         | 156.0 | 2184 | 0.0312          | 35.7274 | 18.6964 |
| 0.104         | 157.0 | 2198 | 0.0311          | 35.7274 | 18.6964 |
| 0.104         | 158.0 | 2212 | 0.0309          | 35.7737 | 18.6964 |
| 0.104         | 159.0 | 2226 | 0.0312          | 35.7274 | 18.6964 |
| 0.104         | 160.0 | 2240 | 0.0305          | 35.7274 | 18.6964 |
| 0.104         | 161.0 | 2254 | 0.0301          | 35.7274 | 18.6964 |
| 0.104         | 162.0 | 2268 | 0.0304          | 35.7274 | 18.6964 |
| 0.104         | 163.0 | 2282 | 0.0309          | 35.7274 | 18.6964 |
| 0.104         | 164.0 | 2296 | 0.0301          | 35.7274 | 18.6964 |
| 0.104         | 165.0 | 2310 | 0.0296          | 35.7274 | 18.6964 |
| 0.104         | 166.0 | 2324 | 0.0292          | 35.7274 | 18.6964 |
| 0.104         | 167.0 | 2338 | 0.0290          | 35.7274 | 18.6964 |
| 0.104         | 168.0 | 2352 | 0.0295          | 35.7737 | 18.6964 |
| 0.104         | 169.0 | 2366 | 0.0298          | 35.7737 | 18.6964 |
| 0.104         | 170.0 | 2380 | 0.0290          | 35.7737 | 18.6964 |
| 0.104         | 171.0 | 2394 | 0.0287          | 35.7737 | 18.6964 |
| 0.104         | 172.0 | 2408 | 0.0289          | 35.7737 | 18.6964 |
| 0.104         | 173.0 | 2422 | 0.0283          | 35.7737 | 18.6964 |
| 0.104         | 174.0 | 2436 | 0.0280          | 35.7737 | 18.6964 |
| 0.104         | 175.0 | 2450 | 0.0280          | 35.7737 | 18.6964 |
| 0.104         | 176.0 | 2464 | 0.0278          | 35.7737 | 18.6964 |
| 0.104         | 177.0 | 2478 | 0.0275          | 35.8416 | 18.6964 |
| 0.104         | 178.0 | 2492 | 0.0274          | 35.8416 | 18.6964 |
| 0.0892        | 179.0 | 2506 | 0.0272          | 35.8416 | 18.6964 |
| 0.0892        | 180.0 | 2520 | 0.0272          | 35.8416 | 18.6964 |
| 0.0892        | 181.0 | 2534 | 0.0275          | 35.8416 | 18.6964 |
| 0.0892        | 182.0 | 2548 | 0.0274          | 35.8416 | 18.6964 |
| 0.0892        | 183.0 | 2562 | 0.0273          | 35.8416 | 18.6964 |
| 0.0892        | 184.0 | 2576 | 0.0274          | 35.8416 | 18.6964 |
| 0.0892        | 185.0 | 2590 | 0.0275          | 35.8416 | 18.6964 |
| 0.0892        | 186.0 | 2604 | 0.0275          | 35.8416 | 18.6964 |
| 0.0892        | 187.0 | 2618 | 0.0277          | 35.8416 | 18.6964 |
| 0.0892        | 188.0 | 2632 | 0.0276          | 35.9612 | 18.6964 |
| 0.0892        | 189.0 | 2646 | 0.0271          | 35.9612 | 18.6964 |
| 0.0892        | 190.0 | 2660 | 0.0269          | 35.9612 | 18.6964 |
| 0.0892        | 191.0 | 2674 | 0.0267          | 35.9612 | 18.6964 |
| 0.0892        | 192.0 | 2688 | 0.0266          | 35.9612 | 18.6964 |
| 0.0892        | 193.0 | 2702 | 0.0265          | 35.9612 | 18.6964 |
| 0.0892        | 194.0 | 2716 | 0.0265          | 35.9612 | 18.6964 |
| 0.0892        | 195.0 | 2730 | 0.0266          | 36.0754 | 18.6964 |
| 0.0892        | 196.0 | 2744 | 0.0266          | 36.0754 | 18.6964 |
| 0.0892        | 197.0 | 2758 | 0.0267          | 36.0754 | 18.6964 |
| 0.0892        | 198.0 | 2772 | 0.0267          | 36.0754 | 18.6964 |
| 0.0892        | 199.0 | 2786 | 0.0267          | 36.0754 | 18.6964 |
| 0.0892        | 200.0 | 2800 | 0.0267          | 36.0754 | 18.6964 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-text2log-finetuned-nl-to-fol", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-finetuned-text2log-finetuned-nl-to-fol
===============================================


This model is a fine-tuned version of mrm8488/t5-small-finetuned-text2log on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0267
* Bleu: 36.0754
* Gen Len: 18.6964


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: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### 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: 200
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1