--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_relevance_task4_fold6 results: [] --- # arabert_cross_relevance_task4_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: 0.3394 - Qwk: 0.1601 - Mse: 0.3398 ## 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 | 0.1111 | 2 | 0.4458 | 0.0318 | 0.4461 | | No log | 0.2222 | 4 | 0.3469 | 0.0319 | 0.3472 | | No log | 0.3333 | 6 | 0.3536 | 0.0805 | 0.3537 | | No log | 0.4444 | 8 | 0.3107 | 0.0717 | 0.3106 | | No log | 0.5556 | 10 | 0.2972 | 0.1151 | 0.2971 | | No log | 0.6667 | 12 | 0.2762 | 0.1226 | 0.2762 | | No log | 0.7778 | 14 | 0.2644 | 0.1492 | 0.2647 | | No log | 0.8889 | 16 | 0.2552 | 0.1559 | 0.2557 | | No log | 1.0 | 18 | 0.2658 | 0.2351 | 0.2661 | | No log | 1.1111 | 20 | 0.2702 | 0.2761 | 0.2706 | | No log | 1.2222 | 22 | 0.2662 | 0.2796 | 0.2669 | | No log | 1.3333 | 24 | 0.2499 | 0.2669 | 0.2508 | | No log | 1.4444 | 26 | 0.2460 | 0.2651 | 0.2468 | | No log | 1.5556 | 28 | 0.2523 | 0.2545 | 0.2531 | | No log | 1.6667 | 30 | 0.2554 | 0.2137 | 0.2561 | | No log | 1.7778 | 32 | 0.2471 | 0.2220 | 0.2478 | | No log | 1.8889 | 34 | 0.2416 | 0.2899 | 0.2423 | | No log | 2.0 | 36 | 0.2485 | 0.2753 | 0.2491 | | No log | 2.1111 | 38 | 0.2427 | 0.2134 | 0.2433 | | No log | 2.2222 | 40 | 0.2377 | 0.2055 | 0.2383 | | No log | 2.3333 | 42 | 0.2333 | 0.2092 | 0.2338 | | No log | 2.4444 | 44 | 0.2390 | 0.2332 | 0.2394 | | No log | 2.5556 | 46 | 0.2482 | 0.2477 | 0.2487 | | No log | 2.6667 | 48 | 0.2585 | 0.2256 | 0.2591 | | No log | 2.7778 | 50 | 0.2626 | 0.2812 | 0.2633 | | No log | 2.8889 | 52 | 0.2502 | 0.2684 | 0.2508 | | No log | 3.0 | 54 | 0.2402 | 0.1962 | 0.2407 | | No log | 3.1111 | 56 | 0.2434 | 0.1883 | 0.2439 | | No log | 3.2222 | 58 | 0.2516 | 0.2024 | 0.2522 | | No log | 3.3333 | 60 | 0.2671 | 0.3187 | 0.2678 | | No log | 3.4444 | 62 | 0.2665 | 0.2324 | 0.2672 | | No log | 3.5556 | 64 | 0.2719 | 0.1977 | 0.2725 | | No log | 3.6667 | 66 | 0.2689 | 0.2130 | 0.2695 | | No log | 3.7778 | 68 | 0.2612 | 0.2140 | 0.2618 | | No log | 3.8889 | 70 | 0.2566 | 0.2395 | 0.2572 | | No log | 4.0 | 72 | 0.2553 | 0.2583 | 0.2560 | | No log | 4.1111 | 74 | 0.2604 | 0.2476 | 0.2610 | | No log | 4.2222 | 76 | 0.2643 | 0.2402 | 0.2649 | | No log | 4.3333 | 78 | 0.2719 | 0.2220 | 0.2725 | | No log | 4.4444 | 80 | 0.2816 | 0.1895 | 0.2822 | | No log | 4.5556 | 82 | 0.2776 | 0.2106 | 0.2783 | | No log | 4.6667 | 84 | 0.2758 | 0.2275 | 0.2765 | | No log | 4.7778 | 86 | 0.2876 | 0.2033 | 0.2882 | | No log | 4.8889 | 88 | 0.3127 | 0.1941 | 0.3132 | | No log | 5.0 | 90 | 0.3256 | 0.1643 | 0.3260 | | No log | 5.1111 | 92 | 0.3061 | 0.1895 | 0.3065 | | No log | 5.2222 | 94 | 0.2811 | 0.2186 | 0.2816 | | No log | 5.3333 | 96 | 0.2823 | 0.2186 | 0.2827 | | No log | 5.4444 | 98 | 0.2910 | 0.2137 | 0.2913 | | No log | 5.5556 | 100 | 0.3094 | 0.2076 | 0.3096 | | No log | 5.6667 | 102 | 0.3152 | 0.2118 | 0.3154 | | No log | 5.7778 | 104 | 0.3477 | 0.1897 | 0.3478 | | No log | 5.8889 | 106 | 0.3597 | 0.1780 | 0.3598 | | No log | 6.0 | 108 | 0.3476 | 0.1780 | 0.3479 | | No log | 6.1111 | 110 | 0.3267 | 0.1992 | 0.3272 | | No log | 6.2222 | 112 | 0.3089 | 0.2125 | 0.3095 | | No log | 6.3333 | 114 | 0.3128 | 0.1908 | 0.3134 | | No log | 6.4444 | 116 | 0.3211 | 0.1742 | 0.3215 | | No log | 6.5556 | 118 | 0.3339 | 0.1590 | 0.3343 | | No log | 6.6667 | 120 | 0.3109 | 0.1741 | 0.3113 | | No log | 6.7778 | 122 | 0.2827 | 0.2060 | 0.2832 | | No log | 6.8889 | 124 | 0.2767 | 0.2060 | 0.2772 | | No log | 7.0 | 126 | 0.2730 | 0.2130 | 0.2736 | | No log | 7.1111 | 128 | 0.2806 | 0.2009 | 0.2812 | | No log | 7.2222 | 130 | 0.2954 | 0.2098 | 0.2959 | | No log | 7.3333 | 132 | 0.3020 | 0.2033 | 0.3025 | | No log | 7.4444 | 134 | 0.3007 | 0.2098 | 0.3012 | | No log | 7.5556 | 136 | 0.2958 | 0.2054 | 0.2964 | | No log | 7.6667 | 138 | 0.3086 | 0.1781 | 0.3091 | | No log | 7.7778 | 140 | 0.3217 | 0.1623 | 0.3222 | | No log | 7.8889 | 142 | 0.3310 | 0.1677 | 0.3315 | | No log | 8.0 | 144 | 0.3269 | 0.1668 | 0.3274 | | No log | 8.1111 | 146 | 0.3173 | 0.1682 | 0.3179 | | No log | 8.2222 | 148 | 0.3173 | 0.1682 | 0.3178 | | No log | 8.3333 | 150 | 0.3261 | 0.1634 | 0.3266 | | No log | 8.4444 | 152 | 0.3255 | 0.1634 | 0.3259 | | No log | 8.5556 | 154 | 0.3229 | 0.1711 | 0.3234 | | No log | 8.6667 | 156 | 0.3219 | 0.1668 | 0.3225 | | No log | 8.7778 | 158 | 0.3264 | 0.1634 | 0.3269 | | No log | 8.8889 | 160 | 0.3314 | 0.1558 | 0.3319 | | No log | 9.0 | 162 | 0.3302 | 0.1558 | 0.3307 | | No log | 9.1111 | 164 | 0.3292 | 0.1601 | 0.3297 | | No log | 9.2222 | 166 | 0.3320 | 0.1601 | 0.3325 | | No log | 9.3333 | 168 | 0.3371 | 0.1601 | 0.3376 | | No log | 9.4444 | 170 | 0.3396 | 0.1601 | 0.3400 | | No log | 9.5556 | 172 | 0.3414 | 0.1601 | 0.3418 | | No log | 9.6667 | 174 | 0.3407 | 0.1601 | 0.3411 | | No log | 9.7778 | 176 | 0.3400 | 0.1601 | 0.3404 | | No log | 9.8889 | 178 | 0.3392 | 0.1601 | 0.3396 | | No log | 10.0 | 180 | 0.3394 | 0.1601 | 0.3398 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1