| --- |
| tags: |
| - generated_from_trainer |
| datasets: |
| - songlab/gpn-msa-sapiens-dataset |
| model-index: |
| - name: checkpoints |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # checkpoints |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the songlab/gpn-msa-sapiens-dataset dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1624 |
|
|
| ## 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: 2048 |
| - eval_batch_size: 2048 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 100 |
| - training_steps: 10000 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:-----:|:---------------:| |
| | 0.6255 | 0.0232 | 50 | 0.2028 | |
| | 0.1532 | 0.0464 | 100 | 0.1865 | |
| | 0.1448 | 0.0695 | 150 | 0.1812 | |
| | 0.1431 | 0.0927 | 200 | 0.1786 | |
| | 0.1423 | 0.1159 | 250 | 0.1769 | |
| | 0.1413 | 0.1391 | 300 | 0.1753 | |
| | 0.1391 | 0.1623 | 350 | 0.1754 | |
| | 0.1398 | 0.1854 | 400 | 0.1761 | |
| | 0.1398 | 0.2086 | 450 | 0.1732 | |
| | 0.1384 | 0.2318 | 500 | 0.1733 | |
| | 0.1386 | 0.2550 | 550 | 0.1720 | |
| | 0.1377 | 0.2782 | 600 | 0.1741 | |
| | 0.1372 | 0.3013 | 650 | 0.1739 | |
| | 0.1374 | 0.3245 | 700 | 0.1725 | |
| | 0.1378 | 0.3477 | 750 | 0.1731 | |
| | 0.1371 | 0.3709 | 800 | 0.1706 | |
| | 0.1364 | 0.3941 | 850 | 0.1731 | |
| | 0.1382 | 0.4172 | 900 | 0.1730 | |
| | 0.1369 | 0.4404 | 950 | 0.1730 | |
| | 0.1373 | 0.4636 | 1000 | 0.1722 | |
| | 0.1357 | 0.4868 | 1050 | 0.1730 | |
| | 0.135 | 0.5100 | 1100 | 0.1715 | |
| | 0.1357 | 0.5331 | 1150 | 0.1702 | |
| | 0.1366 | 0.5563 | 1200 | 0.1708 | |
| | 0.1352 | 0.5795 | 1250 | 0.1722 | |
| | 0.1366 | 0.6027 | 1300 | 0.1698 | |
| | 0.1353 | 0.6259 | 1350 | 0.1702 | |
| | 0.1363 | 0.6490 | 1400 | 0.1706 | |
| | 0.1362 | 0.6722 | 1450 | 0.1689 | |
| | 0.1345 | 0.6954 | 1500 | 0.1679 | |
| | 0.1355 | 0.7186 | 1550 | 0.1690 | |
| | 0.135 | 0.7418 | 1600 | 0.1681 | |
| | 0.1348 | 0.7650 | 1650 | 0.1672 | |
| | 0.1343 | 0.7881 | 1700 | 0.1673 | |
| | 0.1342 | 0.8113 | 1750 | 0.1693 | |
| | 0.1334 | 0.8345 | 1800 | 0.1665 | |
| | 0.136 | 0.8577 | 1850 | 0.1671 | |
| | 0.1349 | 0.8809 | 1900 | 0.1690 | |
| | 0.1345 | 0.9040 | 1950 | 0.1672 | |
| | 0.1332 | 0.9272 | 2000 | 0.1669 | |
| | 0.1349 | 0.9504 | 2050 | 0.1685 | |
| | 0.1355 | 0.9736 | 2100 | 0.1678 | |
| | 0.134 | 0.9968 | 2150 | 0.1670 | |
| | 0.1345 | 1.0199 | 2200 | 0.1672 | |
| | 0.1345 | 1.0431 | 2250 | 0.1681 | |
| | 0.1339 | 1.0663 | 2300 | 0.1662 | |
| | 0.1333 | 1.0895 | 2350 | 0.1674 | |
| | 0.1336 | 1.1127 | 2400 | 0.1651 | |
| | 0.1335 | 1.1358 | 2450 | 0.1657 | |
| | 0.1335 | 1.1590 | 2500 | 0.1671 | |
| | 0.1322 | 1.1822 | 2550 | 0.1655 | |
| | 0.1333 | 1.2054 | 2600 | 0.1664 | |
| | 0.1325 | 1.2286 | 2650 | 0.1660 | |
| | 0.1334 | 1.2517 | 2700 | 0.1654 | |
| | 0.1326 | 1.2749 | 2750 | 0.1657 | |
| | 0.1323 | 1.2981 | 2800 | 0.1658 | |
| | 0.1324 | 1.3213 | 2850 | 0.1662 | |
| | 0.1327 | 1.3445 | 2900 | 0.1664 | |
| | 0.1324 | 1.3676 | 2950 | 0.1670 | |
| | 0.1325 | 1.3908 | 3000 | 0.1659 | |
| | 0.1318 | 1.4140 | 3050 | 0.1639 | |
| | 0.1328 | 1.4372 | 3100 | 0.1665 | |
| | 0.1332 | 1.4604 | 3150 | 0.1676 | |
| | 0.1326 | 1.4835 | 3200 | 0.1649 | |
| | 0.1327 | 1.5067 | 3250 | 0.1664 | |
| | 0.1332 | 1.5299 | 3300 | 0.1658 | |
| | 0.1321 | 1.5531 | 3350 | 0.1662 | |
| | 0.1324 | 1.5763 | 3400 | 0.1642 | |
| | 0.1317 | 1.5994 | 3450 | 0.1658 | |
| | 0.1326 | 1.6226 | 3500 | 0.1651 | |
| | 0.1328 | 1.6458 | 3550 | 0.1651 | |
| | 0.1327 | 1.6690 | 3600 | 0.1654 | |
| | 0.1314 | 1.6922 | 3650 | 0.1639 | |
| | 0.1319 | 1.7153 | 3700 | 0.1658 | |
| | 0.1308 | 1.7385 | 3750 | 0.1657 | |
| | 0.1322 | 1.7617 | 3800 | 0.1649 | |
| | 0.1315 | 1.7849 | 3850 | 0.1649 | |
| | 0.1316 | 1.8081 | 3900 | 0.1639 | |
| | 0.1312 | 1.8312 | 3950 | 0.1659 | |
| | 0.1323 | 1.8544 | 4000 | 0.1648 | |
| | 0.132 | 1.8776 | 4050 | 0.1641 | |
| | 0.1308 | 1.9008 | 4100 | 0.1647 | |
| | 0.1323 | 1.9240 | 4150 | 0.1644 | |
| | 0.131 | 1.9471 | 4200 | 0.1631 | |
| | 0.1313 | 1.9703 | 4250 | 0.1632 | |
| | 0.1309 | 1.9935 | 4300 | 0.1644 | |
| | 0.1319 | 2.0167 | 4350 | 0.1643 | |
| | 0.1316 | 2.0399 | 4400 | 0.1641 | |
| | 0.1312 | 2.0631 | 4450 | 0.1633 | |
| | 0.1313 | 2.0862 | 4500 | 0.1646 | |
| | 0.1316 | 2.1094 | 4550 | 0.1629 | |
| | 0.1306 | 2.1326 | 4600 | 0.1644 | |
| | 0.1313 | 2.1558 | 4650 | 0.1636 | |
| | 0.131 | 2.1790 | 4700 | 0.1635 | |
| | 0.1311 | 2.2021 | 4750 | 0.1637 | |
| | 0.1315 | 2.2253 | 4800 | 0.1641 | |
| | 0.1314 | 2.2485 | 4850 | 0.1641 | |
| | 0.1306 | 2.2717 | 4900 | 0.1636 | |
| | 0.1314 | 2.2949 | 4950 | 0.1635 | |
| | 0.1321 | 2.3180 | 5000 | 0.1628 | |
| | 0.13 | 2.3412 | 5050 | 0.1635 | |
| | 0.1315 | 2.3644 | 5100 | 0.1641 | |
| | 0.1311 | 2.3876 | 5150 | 0.1638 | |
| | 0.1304 | 2.4108 | 5200 | 0.1647 | |
| | 0.131 | 2.4339 | 5250 | 0.1624 | |
| | 0.131 | 2.4571 | 5300 | 0.1633 | |
| | 0.1308 | 2.4803 | 5350 | 0.1639 | |
| | 0.1314 | 2.5035 | 5400 | 0.1643 | |
| | 0.1307 | 2.5267 | 5450 | 0.1631 | |
| | 0.1314 | 2.5498 | 5500 | 0.1635 | |
| | 0.1303 | 2.5730 | 5550 | 0.1635 | |
| | 0.1304 | 2.5962 | 5600 | 0.1631 | |
| | 0.1298 | 2.6194 | 5650 | 0.1623 | |
| | 0.1305 | 2.6426 | 5700 | 0.1627 | |
| | 0.1308 | 2.6657 | 5750 | 0.1624 | |
| | 0.1306 | 2.6889 | 5800 | 0.1639 | |
| | 0.1303 | 2.7121 | 5850 | 0.1633 | |
| | 0.131 | 2.7353 | 5900 | 0.1625 | |
| | 0.1305 | 2.7585 | 5950 | 0.1637 | |
| | 0.1307 | 2.7816 | 6000 | 0.1623 | |
| | 0.1296 | 2.8048 | 6050 | 0.1633 | |
| | 0.1312 | 2.8280 | 6100 | 0.1632 | |
| | 0.1305 | 2.8512 | 6150 | 0.1640 | |
| | 0.1303 | 2.8744 | 6200 | 0.1632 | |
| | 0.1302 | 2.8975 | 6250 | 0.1621 | |
| | 0.1298 | 2.9207 | 6300 | 0.1624 | |
| | 0.1313 | 2.9439 | 6350 | 0.1632 | |
| | 0.1304 | 2.9671 | 6400 | 0.1624 | |
| | 0.13 | 2.9903 | 6450 | 0.1637 | |
| | 0.1313 | 3.0134 | 6500 | 0.1630 | |
| | 0.131 | 3.0366 | 6550 | 0.1612 | |
| | 0.1311 | 3.0598 | 6600 | 0.1621 | |
| | 0.1301 | 3.0830 | 6650 | 0.1619 | |
| | 0.1298 | 3.1062 | 6700 | 0.1622 | |
| | 0.1302 | 3.1293 | 6750 | 0.1609 | |
| | 0.1311 | 3.1525 | 6800 | 0.1623 | |
| | 0.1304 | 3.1757 | 6850 | 0.1625 | |
| | 0.1306 | 3.1989 | 6900 | 0.1615 | |
| | 0.1302 | 3.2221 | 6950 | 0.1622 | |
| | 0.1307 | 3.2452 | 7000 | 0.1623 | |
| | 0.1302 | 3.2684 | 7050 | 0.1626 | |
| | 0.1296 | 3.2916 | 7100 | 0.1622 | |
| | 0.1321 | 3.3148 | 7150 | 0.1622 | |
| | 0.1307 | 3.3380 | 7200 | 0.1628 | |
| | 0.1301 | 3.3611 | 7250 | 0.1625 | |
| | 0.1307 | 3.3843 | 7300 | 0.1632 | |
| | 0.1305 | 3.4075 | 7350 | 0.1627 | |
| | 0.1303 | 3.4307 | 7400 | 0.1626 | |
| | 0.13 | 3.4539 | 7450 | 0.1625 | |
| | 0.1288 | 3.4771 | 7500 | 0.1633 | |
| | 0.1301 | 3.5002 | 7550 | 0.1618 | |
| | 0.1304 | 3.5234 | 7600 | 0.1620 | |
| | 0.1303 | 3.5466 | 7650 | 0.1621 | |
| | 0.1308 | 3.5698 | 7700 | 0.1630 | |
| | 0.1297 | 3.5930 | 7750 | 0.1621 | |
| | 0.13 | 3.6161 | 7800 | 0.1618 | |
| | 0.1305 | 3.6393 | 7850 | 0.1628 | |
| | 0.1314 | 3.6625 | 7900 | 0.1624 | |
| | 0.1299 | 3.6857 | 7950 | 0.1616 | |
| | 0.1294 | 3.7089 | 8000 | 0.1610 | |
| | 0.1293 | 3.7320 | 8050 | 0.1618 | |
| | 0.1303 | 3.7552 | 8100 | 0.1616 | |
| | 0.1318 | 3.7784 | 8150 | 0.1621 | |
| | 0.1295 | 3.8016 | 8200 | 0.1613 | |
| | 0.1309 | 3.8248 | 8250 | 0.1620 | |
| | 0.1288 | 3.8479 | 8300 | 0.1615 | |
| | 0.1296 | 3.8711 | 8350 | 0.1623 | |
| | 0.1302 | 3.8943 | 8400 | 0.1621 | |
| | 0.1303 | 3.9175 | 8450 | 0.1625 | |
| | 0.1296 | 3.9407 | 8500 | 0.1624 | |
| | 0.1302 | 3.9638 | 8550 | 0.1615 | |
| | 0.1311 | 3.9870 | 8600 | 0.1618 | |
| | 0.13 | 4.0102 | 8650 | 0.1617 | |
| | 0.1299 | 4.0334 | 8700 | 0.1623 | |
| | 0.1302 | 4.0566 | 8750 | 0.1627 | |
| | 0.1302 | 4.0797 | 8800 | 0.1612 | |
| | 0.1308 | 4.1029 | 8850 | 0.1627 | |
| | 0.1298 | 4.1261 | 8900 | 0.1633 | |
| | 0.1297 | 4.1493 | 8950 | 0.1626 | |
| | 0.1298 | 4.1725 | 9000 | 0.1617 | |
| | 0.1304 | 4.1956 | 9050 | 0.1623 | |
| | 0.1302 | 4.2188 | 9100 | 0.1616 | |
| | 0.1298 | 4.2420 | 9150 | 0.1626 | |
| | 0.1294 | 4.2652 | 9200 | 0.1625 | |
| | 0.1308 | 4.2884 | 9250 | 0.1621 | |
| | 0.1302 | 4.3115 | 9300 | 0.1624 | |
| | 0.1311 | 4.3347 | 9350 | 0.1613 | |
| | 0.1304 | 4.3579 | 9400 | 0.1629 | |
| | 0.13 | 4.3811 | 9450 | 0.1635 | |
| | 0.1295 | 4.4043 | 9500 | 0.1616 | |
| | 0.1305 | 4.4274 | 9550 | 0.1617 | |
| | 0.13 | 4.4506 | 9600 | 0.1630 | |
| | 0.1297 | 4.4738 | 9650 | 0.1613 | |
| | 0.13 | 4.4970 | 9700 | 0.1622 | |
| | 0.1302 | 4.5202 | 9750 | 0.1617 | |
| | 0.1305 | 4.5433 | 9800 | 0.1622 | |
| | 0.1299 | 4.5665 | 9850 | 0.1631 | |
| | 0.1299 | 4.5897 | 9900 | 0.1618 | |
| | 0.1307 | 4.6129 | 9950 | 0.1629 | |
| | 0.1293 | 4.6361 | 10000 | 0.1616 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.40.2 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.19.1 |
|
|
