--- library_name: transformers base_model: aubmindlab/bert-base-arabert tags: - generated_from_trainer metrics: - accuracy model-index: - name: arabic-hs-group-prediction results: [] --- # arabic-hs-group-prediction This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3113 - Accuracy: 0.9416 - Macro F1: 0.9191 ## 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: 5e-06 - train_batch_size: 16 - eval_batch_size: 20 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:| | 2.07 | 0.1029 | 100 | 1.9059 | 0.4144 | 0.1548 | | 1.7788 | 0.2058 | 200 | 1.6169 | 0.4878 | 0.2150 | | 1.5295 | 0.3086 | 300 | 1.4055 | 0.5833 | 0.3090 | | 1.2792 | 0.4115 | 400 | 1.1486 | 0.6597 | 0.3616 | | 1.1122 | 0.5144 | 500 | 0.9699 | 0.7054 | 0.4298 | | 0.9725 | 0.6173 | 600 | 0.8522 | 0.7517 | 0.5204 | | 0.8443 | 0.7202 | 700 | 0.8157 | 0.7714 | 0.5817 | | 0.7555 | 0.8230 | 800 | 0.6756 | 0.8166 | 0.6595 | | 0.6891 | 0.9259 | 900 | 0.6250 | 0.8247 | 0.6702 | | 0.6112 | 1.0288 | 1000 | 0.5553 | 0.8507 | 0.7110 | | 0.5633 | 1.1317 | 1100 | 0.5647 | 0.8455 | 0.7226 | | 0.4588 | 1.2346 | 1200 | 0.4636 | 0.8733 | 0.7531 | | 0.4503 | 1.3374 | 1300 | 0.4208 | 0.8854 | 0.7669 | | 0.3767 | 1.4403 | 1400 | 0.4149 | 0.8843 | 0.7718 | | 0.3386 | 1.5432 | 1500 | 0.3522 | 0.9103 | 0.7954 | | 0.3535 | 1.6461 | 1600 | 0.3411 | 0.9086 | 0.7912 | | 0.2997 | 1.7490 | 1700 | 0.3224 | 0.9120 | 0.8087 | | 0.3072 | 1.8519 | 1800 | 0.3181 | 0.9184 | 0.8049 | | 0.303 | 1.9547 | 1900 | 0.3026 | 0.9196 | 0.8173 | | 0.3119 | 2.0576 | 2000 | 0.3192 | 0.9155 | 0.8238 | | 0.2306 | 2.1605 | 2100 | 0.3113 | 0.9201 | 0.8277 | | 0.2245 | 2.2634 | 2200 | 0.3124 | 0.9167 | 0.8167 | | 0.2184 | 2.3663 | 2300 | 0.3244 | 0.9196 | 0.8199 | | 0.2373 | 2.4691 | 2400 | 0.3015 | 0.9236 | 0.8340 | | 0.226 | 2.5720 | 2500 | 0.2960 | 0.9253 | 0.8624 | | 0.2449 | 2.6749 | 2600 | 0.2978 | 0.9190 | 0.8558 | | 0.1855 | 2.7778 | 2700 | 0.2847 | 0.9300 | 0.8638 | | 0.2124 | 2.8807 | 2800 | 0.2897 | 0.9259 | 0.8554 | | 0.2198 | 2.9835 | 2900 | 0.2942 | 0.9253 | 0.8826 | | 0.1739 | 3.0864 | 3000 | 0.2851 | 0.9282 | 0.8559 | | 0.1845 | 3.1893 | 3100 | 0.2802 | 0.9294 | 0.8828 | | 0.1866 | 3.2922 | 3200 | 0.2695 | 0.9323 | 0.8665 | | 0.1567 | 3.3951 | 3300 | 0.2897 | 0.9311 | 0.8739 | | 0.1408 | 3.4979 | 3400 | 0.2769 | 0.9346 | 0.8975 | | 0.1746 | 3.6008 | 3500 | 0.2846 | 0.9317 | 0.8967 | | 0.1337 | 3.7037 | 3600 | 0.2776 | 0.9375 | 0.9045 | | 0.1594 | 3.8066 | 3700 | 0.2949 | 0.9340 | 0.9068 | | 0.1603 | 3.9095 | 3800 | 0.2878 | 0.9358 | 0.9040 | | 0.137 | 4.0123 | 3900 | 0.3009 | 0.9346 | 0.9058 | | 0.0908 | 4.1152 | 4000 | 0.2885 | 0.9346 | 0.9053 | | 0.1138 | 4.2181 | 4100 | 0.2972 | 0.9329 | 0.9020 | | 0.1569 | 4.3210 | 4200 | 0.2779 | 0.9381 | 0.9087 | | 0.1242 | 4.4239 | 4300 | 0.2842 | 0.9358 | 0.9098 | | 0.1199 | 4.5267 | 4400 | 0.3212 | 0.9300 | 0.9043 | | 0.1329 | 4.6296 | 4500 | 0.2855 | 0.9369 | 0.9082 | | 0.1135 | 4.7325 | 4600 | 0.2909 | 0.9392 | 0.9089 | | 0.1477 | 4.8354 | 4700 | 0.2766 | 0.9381 | 0.9101 | | 0.1051 | 4.9383 | 4800 | 0.3126 | 0.9340 | 0.9072 | | 0.1002 | 5.0412 | 4900 | 0.2971 | 0.9410 | 0.9128 | | 0.0754 | 5.1440 | 5000 | 0.3037 | 0.9416 | 0.9119 | | 0.0909 | 5.2469 | 5100 | 0.2938 | 0.9392 | 0.9098 | | 0.1075 | 5.3498 | 5200 | 0.2992 | 0.9421 | 0.9129 | | 0.1107 | 5.4527 | 5300 | 0.2896 | 0.9416 | 0.9104 | | 0.0972 | 5.5556 | 5400 | 0.2904 | 0.9387 | 0.9126 | | 0.0873 | 5.6584 | 5500 | 0.2805 | 0.9410 | 0.9127 | | 0.0975 | 5.7613 | 5600 | 0.2888 | 0.9387 | 0.9101 | | 0.0978 | 5.8642 | 5700 | 0.2980 | 0.9375 | 0.9109 | | 0.1219 | 5.9671 | 5800 | 0.2966 | 0.9392 | 0.9092 | | 0.0903 | 6.0700 | 5900 | 0.2950 | 0.9392 | 0.9117 | | 0.083 | 6.1728 | 6000 | 0.2923 | 0.9421 | 0.9141 | | 0.0819 | 6.2757 | 6100 | 0.3091 | 0.9375 | 0.9195 | | 0.0747 | 6.3786 | 6200 | 0.3012 | 0.9404 | 0.9147 | | 0.0631 | 6.4815 | 6300 | 0.2882 | 0.9416 | 0.9110 | | 0.1097 | 6.5844 | 6400 | 0.3174 | 0.9363 | 0.9037 | | 0.0907 | 6.6872 | 6500 | 0.3069 | 0.9416 | 0.9156 | | 0.0654 | 6.7901 | 6600 | 0.3004 | 0.9427 | 0.9185 | | 0.0785 | 6.8930 | 6700 | 0.3069 | 0.9410 | 0.9187 | | 0.0609 | 6.9959 | 6800 | 0.3000 | 0.9421 | 0.9156 | | 0.0715 | 7.0988 | 6900 | 0.2964 | 0.9398 | 0.9114 | | 0.0566 | 7.2016 | 7000 | 0.3081 | 0.9433 | 0.9230 | | 0.0487 | 7.3045 | 7100 | 0.3027 | 0.9416 | 0.9161 | | 0.0555 | 7.4074 | 7200 | 0.3001 | 0.9427 | 0.9165 | | 0.0762 | 7.5103 | 7300 | 0.2982 | 0.9416 | 0.9157 | | 0.0691 | 7.6132 | 7400 | 0.3003 | 0.9427 | 0.9166 | | 0.0654 | 7.7160 | 7500 | 0.3037 | 0.9433 | 0.9185 | | 0.0685 | 7.8189 | 7600 | 0.3103 | 0.9427 | 0.9172 | | 0.0723 | 7.9218 | 7700 | 0.3143 | 0.9427 | 0.9209 | | 0.0655 | 8.0247 | 7800 | 0.3083 | 0.9427 | 0.9209 | | 0.0501 | 8.1276 | 7900 | 0.3002 | 0.9416 | 0.9199 | | 0.0611 | 8.2305 | 8000 | 0.3117 | 0.9421 | 0.9181 | | 0.0557 | 8.3333 | 8100 | 0.3129 | 0.9410 | 0.9207 | | 0.0471 | 8.4362 | 8200 | 0.3072 | 0.9421 | 0.9169 | | 0.0637 | 8.5391 | 8300 | 0.3114 | 0.9450 | 0.9164 | | 0.0553 | 8.6420 | 8400 | 0.3054 | 0.9439 | 0.9175 | | 0.0585 | 8.7449 | 8500 | 0.3073 | 0.9433 | 0.9187 | | 0.055 | 8.8477 | 8600 | 0.3130 | 0.9427 | 0.9149 | | 0.0576 | 8.9506 | 8700 | 0.3187 | 0.9433 | 0.9198 | | 0.0273 | 9.0535 | 8800 | 0.3165 | 0.9439 | 0.9194 | | 0.0425 | 9.1564 | 8900 | 0.3131 | 0.9444 | 0.9167 | | 0.0565 | 9.2593 | 9000 | 0.3106 | 0.9439 | 0.9182 | | 0.0649 | 9.3621 | 9100 | 0.3142 | 0.9433 | 0.9182 | | 0.0554 | 9.4650 | 9200 | 0.3155 | 0.9427 | 0.9224 | | 0.0475 | 9.5679 | 9300 | 0.3140 | 0.9433 | 0.9232 | | 0.0485 | 9.6708 | 9400 | 0.3117 | 0.9416 | 0.9188 | | 0.0431 | 9.7737 | 9500 | 0.3111 | 0.9421 | 0.9198 | | 0.0504 | 9.8765 | 9600 | 0.3109 | 0.9421 | 0.9177 | | 0.0548 | 9.9794 | 9700 | 0.3113 | 0.9416 | 0.9191 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0