--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_relevance_task5_fold1 results: [] --- # arabert_cross_relevance_task5_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1600 - Qwk: 0.0355 - Mse: 0.1600 ## 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: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:| | No log | 0.1333 | 2 | 1.3976 | -0.0095 | 1.3976 | | No log | 0.2667 | 4 | 0.2802 | 0.0303 | 0.2802 | | No log | 0.4 | 6 | 0.1716 | 0.0958 | 0.1716 | | No log | 0.5333 | 8 | 0.1264 | 0.0844 | 0.1264 | | No log | 0.6667 | 10 | 0.3227 | 0.0436 | 0.3227 | | No log | 0.8 | 12 | 0.4525 | 0.0566 | 0.4525 | | No log | 0.9333 | 14 | 0.2745 | 0.0361 | 0.2745 | | No log | 1.0667 | 16 | 0.1334 | 0.0270 | 0.1334 | | No log | 1.2 | 18 | 0.1243 | 0.0270 | 0.1243 | | No log | 1.3333 | 20 | 0.1332 | 0.0338 | 0.1332 | | No log | 1.4667 | 22 | 0.1989 | 0.0254 | 0.1989 | | No log | 1.6 | 24 | 0.2524 | 0.0281 | 0.2524 | | No log | 1.7333 | 26 | 0.2192 | 0.0339 | 0.2192 | | No log | 1.8667 | 28 | 0.1883 | 0.0339 | 0.1883 | | No log | 2.0 | 30 | 0.1407 | 0.0511 | 0.1407 | | No log | 2.1333 | 32 | 0.1285 | 0.0493 | 0.1285 | | No log | 2.2667 | 34 | 0.1272 | 0.0386 | 0.1272 | | No log | 2.4 | 36 | 0.1551 | 0.0339 | 0.1551 | | No log | 2.5333 | 38 | 0.1968 | 0.0339 | 0.1968 | | No log | 2.6667 | 40 | 0.1958 | 0.0339 | 0.1958 | | No log | 2.8 | 42 | 0.1776 | 0.0339 | 0.1776 | | No log | 2.9333 | 44 | 0.1898 | 0.0339 | 0.1898 | | No log | 3.0667 | 46 | 0.1824 | 0.0339 | 0.1824 | | No log | 3.2 | 48 | 0.1597 | 0.0254 | 0.1597 | | No log | 3.3333 | 50 | 0.1434 | 0.0254 | 0.1434 | | No log | 3.4667 | 52 | 0.1327 | 0.0342 | 0.1327 | | No log | 3.6 | 54 | 0.1413 | 0.0290 | 0.1413 | | No log | 3.7333 | 56 | 0.1731 | 0.0339 | 0.1731 | | No log | 3.8667 | 58 | 0.1817 | 0.0339 | 0.1817 | | No log | 4.0 | 60 | 0.1522 | 0.0389 | 0.1522 | | No log | 4.1333 | 62 | 0.1305 | 0.0661 | 0.1305 | | No log | 4.2667 | 64 | 0.1284 | 0.0630 | 0.1284 | | No log | 4.4 | 66 | 0.1355 | 0.0393 | 0.1355 | | No log | 4.5333 | 68 | 0.1604 | 0.0339 | 0.1604 | | No log | 4.6667 | 70 | 0.2002 | 0.0339 | 0.2002 | | No log | 4.8 | 72 | 0.1955 | 0.0339 | 0.1955 | | No log | 4.9333 | 74 | 0.1658 | 0.0339 | 0.1658 | | No log | 5.0667 | 76 | 0.1489 | 0.0339 | 0.1489 | | No log | 5.2 | 78 | 0.1364 | 0.0307 | 0.1364 | | No log | 5.3333 | 80 | 0.1306 | 0.0359 | 0.1306 | | No log | 5.4667 | 82 | 0.1357 | 0.0307 | 0.1357 | | No log | 5.6 | 84 | 0.1533 | 0.0339 | 0.1533 | | No log | 5.7333 | 86 | 0.1769 | 0.0339 | 0.1769 | | No log | 5.8667 | 88 | 0.1814 | 0.0339 | 0.1814 | | No log | 6.0 | 90 | 0.1634 | 0.0273 | 0.1634 | | No log | 6.1333 | 92 | 0.1427 | 0.0273 | 0.1427 | | No log | 6.2667 | 94 | 0.1345 | 0.0324 | 0.1345 | | No log | 6.4 | 96 | 0.1360 | 0.0377 | 0.1360 | | No log | 6.5333 | 98 | 0.1445 | 0.0307 | 0.1445 | | No log | 6.6667 | 100 | 0.1592 | 0.0290 | 0.1592 | | No log | 6.8 | 102 | 0.1809 | 0.0355 | 0.1809 | | No log | 6.9333 | 104 | 0.1826 | 0.0254 | 0.1826 | | No log | 7.0667 | 106 | 0.1828 | 0.0254 | 0.1828 | | No log | 7.2 | 108 | 0.1811 | 0.0254 | 0.1811 | | No log | 7.3333 | 110 | 0.1729 | 0.0270 | 0.1729 | | No log | 7.4667 | 112 | 0.1676 | 0.0290 | 0.1676 | | No log | 7.6 | 114 | 0.1697 | 0.0273 | 0.1697 | | No log | 7.7333 | 116 | 0.1696 | 0.0254 | 0.1696 | | No log | 7.8667 | 118 | 0.1615 | 0.0254 | 0.1615 | | No log | 8.0 | 120 | 0.1570 | 0.0304 | 0.1570 | | No log | 8.1333 | 122 | 0.1567 | 0.0304 | 0.1567 | | No log | 8.2667 | 124 | 0.1631 | 0.0287 | 0.1631 | | No log | 8.4 | 126 | 0.1702 | 0.0270 | 0.1702 | | No log | 8.5333 | 128 | 0.1774 | 0.0355 | 0.1774 | | No log | 8.6667 | 130 | 0.1807 | 0.0355 | 0.1807 | | No log | 8.8 | 132 | 0.1787 | 0.0372 | 0.1787 | | No log | 8.9333 | 134 | 0.1755 | 0.0389 | 0.1755 | | No log | 9.0667 | 136 | 0.1743 | 0.0406 | 0.1743 | | No log | 9.2 | 138 | 0.1698 | 0.0406 | 0.1698 | | No log | 9.3333 | 140 | 0.1640 | 0.0423 | 0.1640 | | No log | 9.4667 | 142 | 0.1601 | 0.0441 | 0.1601 | | No log | 9.6 | 144 | 0.1589 | 0.0441 | 0.1589 | | No log | 9.7333 | 146 | 0.1591 | 0.0355 | 0.1591 | | No log | 9.8667 | 148 | 0.1596 | 0.0355 | 0.1596 | | No log | 10.0 | 150 | 0.1600 | 0.0355 | 0.1600 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1