--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: arabert_cross_vocabulary_task1_fold5 results: [] --- # arabert_cross_vocabulary_task1_fold5 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: 0.2973 - Qwk: 0.8562 - Mse: 0.2979 ## 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 | 1.8520 | 0.1480 | 1.8511 | | No log | 0.2667 | 4 | 1.0651 | 0.3341 | 1.0655 | | No log | 0.4 | 6 | 1.1438 | 0.4927 | 1.1443 | | No log | 0.5333 | 8 | 0.9722 | 0.6250 | 0.9725 | | No log | 0.6667 | 10 | 0.6730 | 0.6107 | 0.6733 | | No log | 0.8 | 12 | 0.5341 | 0.7630 | 0.5344 | | No log | 0.9333 | 14 | 0.4706 | 0.8016 | 0.4708 | | No log | 1.0667 | 16 | 0.4255 | 0.8194 | 0.4256 | | No log | 1.2 | 18 | 0.4901 | 0.8387 | 0.4901 | | No log | 1.3333 | 20 | 0.4642 | 0.8545 | 0.4642 | | No log | 1.4667 | 22 | 0.3781 | 0.8345 | 0.3782 | | No log | 1.6 | 24 | 0.3229 | 0.8175 | 0.3231 | | No log | 1.7333 | 26 | 0.3530 | 0.8366 | 0.3532 | | No log | 1.8667 | 28 | 0.4548 | 0.8719 | 0.4547 | | No log | 2.0 | 30 | 0.6182 | 0.8633 | 0.6181 | | No log | 2.1333 | 32 | 0.5731 | 0.8699 | 0.5733 | | No log | 2.2667 | 34 | 0.3622 | 0.8422 | 0.3626 | | No log | 2.4 | 36 | 0.2913 | 0.8430 | 0.2916 | | No log | 2.5333 | 38 | 0.2887 | 0.8426 | 0.2890 | | No log | 2.6667 | 40 | 0.2802 | 0.8370 | 0.2805 | | No log | 2.8 | 42 | 0.3048 | 0.8363 | 0.3050 | | No log | 2.9333 | 44 | 0.3487 | 0.8575 | 0.3490 | | No log | 3.0667 | 46 | 0.3168 | 0.8493 | 0.3171 | | No log | 3.2 | 48 | 0.2820 | 0.8372 | 0.2823 | | No log | 3.3333 | 50 | 0.3036 | 0.8603 | 0.3040 | | No log | 3.4667 | 52 | 0.3945 | 0.8659 | 0.3949 | | No log | 3.6 | 54 | 0.3717 | 0.8677 | 0.3721 | | No log | 3.7333 | 56 | 0.2969 | 0.8614 | 0.2973 | | No log | 3.8667 | 58 | 0.2569 | 0.8385 | 0.2572 | | No log | 4.0 | 60 | 0.2971 | 0.7270 | 0.2975 | | No log | 4.1333 | 62 | 0.3312 | 0.6927 | 0.3316 | | No log | 4.2667 | 64 | 0.2800 | 0.7702 | 0.2804 | | No log | 4.4 | 66 | 0.2896 | 0.8502 | 0.2900 | | No log | 4.5333 | 68 | 0.4169 | 0.8694 | 0.4173 | | No log | 4.6667 | 70 | 0.4616 | 0.8813 | 0.4620 | | No log | 4.8 | 72 | 0.3663 | 0.8659 | 0.3668 | | No log | 4.9333 | 74 | 0.2765 | 0.8520 | 0.2769 | | No log | 5.0667 | 76 | 0.3059 | 0.7201 | 0.3063 | | No log | 5.2 | 78 | 0.3256 | 0.6901 | 0.3260 | | No log | 5.3333 | 80 | 0.2884 | 0.7612 | 0.2888 | | No log | 5.4667 | 82 | 0.2705 | 0.8362 | 0.2710 | | No log | 5.6 | 84 | 0.3435 | 0.8601 | 0.3440 | | No log | 5.7333 | 86 | 0.4426 | 0.8795 | 0.4430 | | No log | 5.8667 | 88 | 0.4328 | 0.8789 | 0.4333 | | No log | 6.0 | 90 | 0.3734 | 0.8717 | 0.3738 | | No log | 6.1333 | 92 | 0.3017 | 0.8603 | 0.3022 | | No log | 6.2667 | 94 | 0.2650 | 0.8366 | 0.2655 | | No log | 6.4 | 96 | 0.2634 | 0.8175 | 0.2639 | | No log | 6.5333 | 98 | 0.2676 | 0.8404 | 0.2681 | | No log | 6.6667 | 100 | 0.2966 | 0.8607 | 0.2972 | | No log | 6.8 | 102 | 0.3292 | 0.8611 | 0.3297 | | No log | 6.9333 | 104 | 0.3536 | 0.8685 | 0.3541 | | No log | 7.0667 | 106 | 0.3466 | 0.8685 | 0.3470 | | No log | 7.2 | 108 | 0.3070 | 0.8599 | 0.3075 | | No log | 7.3333 | 110 | 0.2886 | 0.8636 | 0.2891 | | No log | 7.4667 | 112 | 0.2905 | 0.8636 | 0.2910 | | No log | 7.6 | 114 | 0.2879 | 0.8636 | 0.2884 | | No log | 7.7333 | 116 | 0.2898 | 0.8633 | 0.2904 | | No log | 7.8667 | 118 | 0.2981 | 0.8587 | 0.2987 | | No log | 8.0 | 120 | 0.2958 | 0.8593 | 0.2963 | | No log | 8.1333 | 122 | 0.3001 | 0.8607 | 0.3006 | | No log | 8.2667 | 124 | 0.2931 | 0.8606 | 0.2936 | | No log | 8.4 | 126 | 0.2793 | 0.8630 | 0.2798 | | No log | 8.5333 | 128 | 0.2685 | 0.8532 | 0.2690 | | No log | 8.6667 | 130 | 0.2691 | 0.8518 | 0.2696 | | No log | 8.8 | 132 | 0.2769 | 0.8571 | 0.2775 | | No log | 8.9333 | 134 | 0.2923 | 0.8575 | 0.2929 | | No log | 9.0667 | 136 | 0.3156 | 0.8576 | 0.3161 | | No log | 9.2 | 138 | 0.3326 | 0.8643 | 0.3332 | | No log | 9.3333 | 140 | 0.3349 | 0.8643 | 0.3355 | | No log | 9.4667 | 142 | 0.3280 | 0.8657 | 0.3286 | | No log | 9.6 | 144 | 0.3162 | 0.8599 | 0.3168 | | No log | 9.7333 | 146 | 0.3062 | 0.8582 | 0.3068 | | No log | 9.8667 | 148 | 0.2995 | 0.8549 | 0.3001 | | No log | 10.0 | 150 | 0.2973 | 0.8562 | 0.2979 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1