--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_relevance_task3_fold6 results: [] --- # arabert_cross_relevance_task3_fold6 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: 0.3958 - Qwk: 0.2096 - Mse: 0.3959 ## 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 | 0.1176 | 2 | 0.7510 | 0.0098 | 0.7490 | | No log | 0.2353 | 4 | 0.3906 | 0.1162 | 0.3902 | | No log | 0.3529 | 6 | 0.3053 | 0.1347 | 0.3057 | | No log | 0.4706 | 8 | 0.2972 | 0.1520 | 0.2976 | | No log | 0.5882 | 10 | 0.2828 | 0.1830 | 0.2831 | | No log | 0.7059 | 12 | 0.2698 | 0.1906 | 0.2703 | | No log | 0.8235 | 14 | 0.2743 | 0.1500 | 0.2750 | | No log | 0.9412 | 16 | 0.2742 | 0.1578 | 0.2747 | | No log | 1.0588 | 18 | 0.3079 | 0.1726 | 0.3083 | | No log | 1.1765 | 20 | 0.3022 | 0.1999 | 0.3025 | | No log | 1.2941 | 22 | 0.2720 | 0.2049 | 0.2724 | | No log | 1.4118 | 24 | 0.2712 | 0.2178 | 0.2717 | | No log | 1.5294 | 26 | 0.2820 | 0.2018 | 0.2823 | | No log | 1.6471 | 28 | 0.2988 | 0.2137 | 0.2990 | | No log | 1.7647 | 30 | 0.2801 | 0.2099 | 0.2803 | | No log | 1.8824 | 32 | 0.2536 | 0.2155 | 0.2541 | | No log | 2.0 | 34 | 0.2547 | 0.2222 | 0.2553 | | No log | 2.1176 | 36 | 0.2817 | 0.1893 | 0.2821 | | No log | 2.2353 | 38 | 0.3173 | 0.2127 | 0.3175 | | No log | 2.3529 | 40 | 0.2805 | 0.1991 | 0.2808 | | No log | 2.4706 | 42 | 0.2617 | 0.2112 | 0.2624 | | No log | 2.5882 | 44 | 0.2662 | 0.2183 | 0.2670 | | No log | 2.7059 | 46 | 0.2560 | 0.2109 | 0.2565 | | No log | 2.8235 | 48 | 0.3002 | 0.2083 | 0.3005 | | No log | 2.9412 | 50 | 0.3062 | 0.2127 | 0.3065 | | No log | 3.0588 | 52 | 0.2753 | 0.1995 | 0.2757 | | No log | 3.1765 | 54 | 0.2479 | 0.2057 | 0.2485 | | No log | 3.2941 | 56 | 0.2470 | 0.1860 | 0.2476 | | No log | 3.4118 | 58 | 0.2610 | 0.2099 | 0.2614 | | No log | 3.5294 | 60 | 0.2690 | 0.2044 | 0.2693 | | No log | 3.6471 | 62 | 0.2641 | 0.2067 | 0.2645 | | No log | 3.7647 | 64 | 0.2712 | 0.1995 | 0.2715 | | No log | 3.8824 | 66 | 0.2789 | 0.1943 | 0.2792 | | No log | 4.0 | 68 | 0.2918 | 0.2127 | 0.2920 | | No log | 4.1176 | 70 | 0.2717 | 0.2038 | 0.2720 | | No log | 4.2353 | 72 | 0.2866 | 0.2127 | 0.2868 | | No log | 4.3529 | 74 | 0.2850 | 0.2127 | 0.2852 | | No log | 4.4706 | 76 | 0.2771 | 0.2292 | 0.2774 | | No log | 4.5882 | 78 | 0.2687 | 0.1915 | 0.2690 | | No log | 4.7059 | 80 | 0.2758 | 0.2044 | 0.2761 | | No log | 4.8235 | 82 | 0.3101 | 0.2211 | 0.3103 | | No log | 4.9412 | 84 | 0.3572 | 0.2110 | 0.3573 | | No log | 5.0588 | 86 | 0.3906 | 0.1858 | 0.3906 | | No log | 5.1765 | 88 | 0.3591 | 0.1982 | 0.3592 | | No log | 5.2941 | 90 | 0.3170 | 0.2140 | 0.3173 | | No log | 5.4118 | 92 | 0.3098 | 0.2140 | 0.3102 | | No log | 5.5294 | 94 | 0.3073 | 0.2048 | 0.3077 | | No log | 5.6471 | 96 | 0.3304 | 0.2001 | 0.3307 | | No log | 5.7647 | 98 | 0.3825 | 0.2103 | 0.3825 | | No log | 5.8824 | 100 | 0.3978 | 0.1979 | 0.3978 | | No log | 6.0 | 102 | 0.3627 | 0.2110 | 0.3629 | | No log | 6.1176 | 104 | 0.3352 | 0.1985 | 0.3354 | | No log | 6.2353 | 106 | 0.3174 | 0.2127 | 0.3177 | | No log | 6.3529 | 108 | 0.3418 | 0.2070 | 0.3421 | | No log | 6.4706 | 110 | 0.3437 | 0.1982 | 0.3439 | | No log | 6.5882 | 112 | 0.3452 | 0.1982 | 0.3454 | | No log | 6.7059 | 114 | 0.3485 | 0.2070 | 0.3487 | | No log | 6.8235 | 116 | 0.3713 | 0.1979 | 0.3714 | | No log | 6.9412 | 118 | 0.3854 | 0.1858 | 0.3855 | | No log | 7.0588 | 120 | 0.3950 | 0.1821 | 0.3949 | | No log | 7.1765 | 122 | 0.3832 | 0.1858 | 0.3832 | | No log | 7.2941 | 124 | 0.3695 | 0.2019 | 0.3697 | | No log | 7.4118 | 126 | 0.3763 | 0.2058 | 0.3764 | | No log | 7.5294 | 128 | 0.3884 | 0.1898 | 0.3885 | | No log | 7.6471 | 130 | 0.4063 | 0.1785 | 0.4064 | | No log | 7.7647 | 132 | 0.3900 | 0.1780 | 0.3901 | | No log | 7.8824 | 134 | 0.3822 | 0.1739 | 0.3823 | | No log | 8.0 | 136 | 0.3725 | 0.1856 | 0.3726 | | No log | 8.1176 | 138 | 0.3559 | 0.1854 | 0.3560 | | No log | 8.2353 | 140 | 0.3576 | 0.2023 | 0.3578 | | No log | 8.3529 | 142 | 0.3825 | 0.1977 | 0.3826 | | No log | 8.4706 | 144 | 0.3960 | 0.2015 | 0.3961 | | No log | 8.5882 | 146 | 0.3877 | 0.2015 | 0.3877 | | No log | 8.7059 | 148 | 0.3846 | 0.2096 | 0.3847 | | No log | 8.8235 | 150 | 0.3711 | 0.2058 | 0.3712 | | No log | 8.9412 | 152 | 0.3592 | 0.2103 | 0.3594 | | No log | 9.0588 | 154 | 0.3526 | 0.2103 | 0.3529 | | No log | 9.1765 | 156 | 0.3597 | 0.2141 | 0.3598 | | No log | 9.2941 | 158 | 0.3711 | 0.2058 | 0.3712 | | No log | 9.4118 | 160 | 0.3827 | 0.2096 | 0.3828 | | No log | 9.5294 | 162 | 0.3867 | 0.2096 | 0.3867 | | No log | 9.6471 | 164 | 0.3912 | 0.2096 | 0.3912 | | No log | 9.7647 | 166 | 0.3942 | 0.2096 | 0.3942 | | No log | 9.8824 | 168 | 0.3960 | 0.2096 | 0.3960 | | No log | 10.0 | 170 | 0.3958 | 0.2096 | 0.3959 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1