--- library_name: transformers license: apache-2.0 base_model: cssupport/t5-small-awesome-text-to-sql tags: - generated_from_trainer model-index: - name: results results: [] --- # results This model is a fine-tuned version of [cssupport/t5-small-awesome-text-to-sql](https://huggingface.co/cssupport/t5-small-awesome-text-to-sql) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1343 ## 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: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 0.6591 | 0.04 | 100 | 0.4689 | | 0.4841 | 0.08 | 200 | 0.3828 | | 0.4239 | 0.12 | 300 | 0.3401 | | 0.3792 | 0.16 | 400 | 0.3111 | | 0.3679 | 0.2 | 500 | 0.2896 | | 0.3301 | 0.24 | 600 | 0.2749 | | 0.3104 | 0.28 | 700 | 0.2616 | | 0.3118 | 0.32 | 800 | 0.2518 | | 0.2981 | 0.36 | 900 | 0.2430 | | 0.2925 | 0.4 | 1000 | 0.2355 | | 0.2772 | 0.44 | 1100 | 0.2289 | | 0.2643 | 0.48 | 1200 | 0.2235 | | 0.2608 | 0.52 | 1300 | 0.2186 | | 0.2594 | 0.56 | 1400 | 0.2145 | | 0.2631 | 0.6 | 1500 | 0.2088 | | 0.2553 | 0.64 | 1600 | 0.2047 | | 0.2481 | 0.68 | 1700 | 0.2011 | | 0.2387 | 0.72 | 1800 | 0.1980 | | 0.2446 | 0.76 | 1900 | 0.1946 | | 0.2355 | 0.8 | 2000 | 0.1916 | | 0.2405 | 0.84 | 2100 | 0.1895 | | 0.2287 | 0.88 | 2200 | 0.1870 | | 0.2291 | 0.92 | 2300 | 0.1848 | | 0.2164 | 0.96 | 2400 | 0.1834 | | 0.2126 | 1.0 | 2500 | 0.1819 | | 0.216 | 1.04 | 2600 | 0.1797 | | 0.2139 | 1.08 | 2700 | 0.1776 | | 0.21 | 1.12 | 2800 | 0.1761 | | 0.2055 | 1.16 | 2900 | 0.1749 | | 0.2083 | 1.2 | 3000 | 0.1730 | | 0.1979 | 1.24 | 3100 | 0.1718 | | 0.2056 | 1.28 | 3200 | 0.1705 | | 0.1999 | 1.32 | 3300 | 0.1696 | | 0.2014 | 1.3600 | 3400 | 0.1681 | | 0.2052 | 1.4 | 3500 | 0.1667 | | 0.1858 | 1.44 | 3600 | 0.1659 | | 0.2085 | 1.48 | 3700 | 0.1640 | | 0.1971 | 1.52 | 3800 | 0.1634 | | 0.199 | 1.56 | 3900 | 0.1625 | | 0.1962 | 1.6 | 4000 | 0.1618 | | 0.1953 | 1.6400 | 4100 | 0.1604 | | 0.1925 | 1.6800 | 4200 | 0.1593 | | 0.1998 | 1.72 | 4300 | 0.1588 | | 0.1937 | 1.76 | 4400 | 0.1572 | | 0.1934 | 1.8 | 4500 | 0.1571 | | 0.1931 | 1.8400 | 4600 | 0.1563 | | 0.1924 | 1.88 | 4700 | 0.1547 | | 0.1864 | 1.92 | 4800 | 0.1546 | | 0.1878 | 1.96 | 4900 | 0.1535 | | 0.1821 | 2.0 | 5000 | 0.1533 | | 0.1824 | 2.04 | 5100 | 0.1526 | | 0.1814 | 2.08 | 5200 | 0.1523 | | 0.1823 | 2.12 | 5300 | 0.1509 | | 0.1826 | 2.16 | 5400 | 0.1507 | | 0.1828 | 2.2 | 5500 | 0.1500 | | 0.1787 | 2.24 | 5600 | 0.1495 | | 0.1832 | 2.2800 | 5700 | 0.1491 | | 0.173 | 2.32 | 5800 | 0.1485 | | 0.1773 | 2.36 | 5900 | 0.1481 | | 0.1751 | 2.4 | 6000 | 0.1479 | | 0.1658 | 2.44 | 6100 | 0.1472 | | 0.1803 | 2.48 | 6200 | 0.1465 | | 0.1725 | 2.52 | 6300 | 0.1458 | | 0.1711 | 2.56 | 6400 | 0.1456 | | 0.1796 | 2.6 | 6500 | 0.1454 | | 0.1739 | 2.64 | 6600 | 0.1447 | | 0.1689 | 2.68 | 6700 | 0.1441 | | 0.166 | 2.7200 | 6800 | 0.1441 | | 0.1655 | 2.76 | 6900 | 0.1437 | | 0.173 | 2.8 | 7000 | 0.1435 | | 0.1693 | 2.84 | 7100 | 0.1423 | | 0.1739 | 2.88 | 7200 | 0.1426 | | 0.1703 | 2.92 | 7300 | 0.1419 | | 0.1675 | 2.96 | 7400 | 0.1419 | | 0.1723 | 3.0 | 7500 | 0.1410 | | 0.1655 | 3.04 | 7600 | 0.1410 | | 0.1671 | 3.08 | 7700 | 0.1409 | | 0.1633 | 3.12 | 7800 | 0.1408 | | 0.1611 | 3.16 | 7900 | 0.1408 | | 0.1622 | 3.2 | 8000 | 0.1403 | | 0.1619 | 3.24 | 8100 | 0.1399 | | 0.1667 | 3.2800 | 8200 | 0.1395 | | 0.164 | 3.32 | 8300 | 0.1393 | | 0.1635 | 3.36 | 8400 | 0.1388 | | 0.1672 | 3.4 | 8500 | 0.1386 | | 0.164 | 3.44 | 8600 | 0.1383 | | 0.1673 | 3.48 | 8700 | 0.1381 | | 0.1643 | 3.52 | 8800 | 0.1379 | | 0.1612 | 3.56 | 8900 | 0.1379 | | 0.1578 | 3.6 | 9000 | 0.1378 | | 0.1651 | 3.64 | 9100 | 0.1377 | | 0.1631 | 3.68 | 9200 | 0.1374 | | 0.1645 | 3.7200 | 9300 | 0.1370 | | 0.1627 | 3.76 | 9400 | 0.1366 | | 0.1607 | 3.8 | 9500 | 0.1371 | | 0.1621 | 3.84 | 9600 | 0.1365 | | 0.1683 | 3.88 | 9700 | 0.1363 | | 0.1559 | 3.92 | 9800 | 0.1361 | | 0.1601 | 3.96 | 9900 | 0.1361 | | 0.1589 | 4.0 | 10000 | 0.1360 | | 0.16 | 4.04 | 10100 | 0.1360 | | 0.1589 | 4.08 | 10200 | 0.1359 | | 0.1569 | 4.12 | 10300 | 0.1358 | | 0.1579 | 4.16 | 10400 | 0.1357 | | 0.1667 | 4.2 | 10500 | 0.1356 | | 0.1567 | 4.24 | 10600 | 0.1354 | | 0.1596 | 4.28 | 10700 | 0.1353 | | 0.1584 | 4.32 | 10800 | 0.1351 | | 0.1605 | 4.36 | 10900 | 0.1352 | | 0.1543 | 4.4 | 11000 | 0.1351 | | 0.1588 | 4.44 | 11100 | 0.1348 | | 0.1553 | 4.48 | 11200 | 0.1348 | | 0.1533 | 4.52 | 11300 | 0.1349 | | 0.1529 | 4.5600 | 11400 | 0.1347 | | 0.1597 | 4.6 | 11500 | 0.1347 | | 0.1518 | 4.64 | 11600 | 0.1347 | | 0.1585 | 4.68 | 11700 | 0.1345 | | 0.156 | 4.72 | 11800 | 0.1345 | | 0.1593 | 4.76 | 11900 | 0.1344 | | 0.1622 | 4.8 | 12000 | 0.1344 | | 0.1553 | 4.84 | 12100 | 0.1344 | | 0.1645 | 4.88 | 12200 | 0.1344 | | 0.1535 | 4.92 | 12300 | 0.1343 | | 0.1572 | 4.96 | 12400 | 0.1343 | | 0.1614 | 5.0 | 12500 | 0.1343 | ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0