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README.md CHANGED
@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3077
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  - Accuracy: 0.9444
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- - Macro F1: 0.9186
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  ## Model description
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@@ -50,108 +50,108 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
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- | 2.07 | 0.1029 | 100 | 1.9057 | 0.4138 | 0.1540 |
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- | 1.7842 | 0.2058 | 200 | 1.6173 | 0.4919 | 0.2150 |
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- | 1.5378 | 0.3086 | 300 | 1.4258 | 0.5677 | 0.3002 |
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- | 1.2923 | 0.4115 | 400 | 1.1695 | 0.6493 | 0.3491 |
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- | 1.1272 | 0.5144 | 500 | 0.9815 | 0.7089 | 0.4382 |
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- | 0.9809 | 0.6173 | 600 | 0.8672 | 0.7442 | 0.5058 |
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- | 0.855 | 0.7202 | 700 | 0.8270 | 0.7697 | 0.5703 |
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- | 0.7658 | 0.8230 | 800 | 0.6889 | 0.8108 | 0.6455 |
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- | 0.6988 | 0.9259 | 900 | 0.6324 | 0.8264 | 0.6717 |
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- | 0.6251 | 1.0288 | 1000 | 0.5763 | 0.8420 | 0.7004 |
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- | 0.5792 | 1.1317 | 1100 | 0.5838 | 0.8362 | 0.7120 |
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- | 0.4802 | 1.2346 | 1200 | 0.4946 | 0.8657 | 0.7463 |
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- | 0.4694 | 1.3374 | 1300 | 0.4492 | 0.8802 | 0.7613 |
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- | 0.3975 | 1.4403 | 1400 | 0.4199 | 0.8843 | 0.7700 |
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- | 0.3451 | 1.5432 | 1500 | 0.3699 | 0.9028 | 0.7864 |
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- | 0.3683 | 1.6461 | 1600 | 0.3512 | 0.9062 | 0.7900 |
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- | 0.3099 | 1.7490 | 1700 | 0.3301 | 0.9109 | 0.8071 |
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- | 0.3189 | 1.8519 | 1800 | 0.3230 | 0.9144 | 0.8001 |
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- | 0.3085 | 1.9547 | 1900 | 0.3109 | 0.9190 | 0.8172 |
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- | 0.3222 | 2.0576 | 2000 | 0.3220 | 0.9132 | 0.8126 |
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- | 0.2405 | 2.1605 | 2100 | 0.3131 | 0.9167 | 0.8233 |
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- | 0.2274 | 2.2634 | 2200 | 0.3182 | 0.9138 | 0.8248 |
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- | 0.2272 | 2.3663 | 2300 | 0.3287 | 0.9184 | 0.8177 |
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- | 0.2426 | 2.4691 | 2400 | 0.3074 | 0.9242 | 0.8355 |
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- | 0.2319 | 2.5720 | 2500 | 0.2970 | 0.9230 | 0.8574 |
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- | 0.2475 | 2.6749 | 2600 | 0.2965 | 0.9201 | 0.8567 |
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- | 0.1874 | 2.7778 | 2700 | 0.2860 | 0.9253 | 0.8505 |
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- | 0.2181 | 2.8807 | 2800 | 0.2904 | 0.9248 | 0.8451 |
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- | 0.2253 | 2.9835 | 2900 | 0.2865 | 0.9236 | 0.8724 |
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- | 0.1796 | 3.0864 | 3000 | 0.2856 | 0.9277 | 0.8554 |
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- | 0.1899 | 3.1893 | 3100 | 0.2751 | 0.9294 | 0.8788 |
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- | 0.1919 | 3.2922 | 3200 | 0.2739 | 0.9288 | 0.8539 |
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- | 0.1701 | 3.3951 | 3300 | 0.2869 | 0.9288 | 0.8756 |
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- | 0.1484 | 3.4979 | 3400 | 0.2793 | 0.9323 | 0.8849 |
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- | 0.1828 | 3.6008 | 3500 | 0.2834 | 0.9294 | 0.8694 |
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- | 0.1434 | 3.7037 | 3600 | 0.2777 | 0.9358 | 0.8899 |
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- | 0.1683 | 3.8066 | 3700 | 0.3088 | 0.9265 | 0.8900 |
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- | 0.1606 | 3.9095 | 3800 | 0.2850 | 0.9346 | 0.8930 |
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- | 0.1425 | 4.0123 | 3900 | 0.3057 | 0.9294 | 0.8906 |
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- | 0.0901 | 4.1152 | 4000 | 0.2891 | 0.9323 | 0.9021 |
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- | 0.1163 | 4.2181 | 4100 | 0.2978 | 0.9334 | 0.8925 |
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- | 0.1591 | 4.3210 | 4200 | 0.2765 | 0.9381 | 0.9104 |
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- | 0.1248 | 4.4239 | 4300 | 0.2929 | 0.9346 | 0.8987 |
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- | 0.1241 | 4.5267 | 4400 | 0.3260 | 0.9271 | 0.8930 |
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- | 0.1421 | 4.6296 | 4500 | 0.2775 | 0.9375 | 0.9029 |
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- | 0.1196 | 4.7325 | 4600 | 0.2864 | 0.9375 | 0.9016 |
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- | 0.1511 | 4.8354 | 4700 | 0.2844 | 0.9369 | 0.9057 |
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- | 0.1102 | 4.9383 | 4800 | 0.3208 | 0.9311 | 0.8990 |
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- | 0.1024 | 5.0412 | 4900 | 0.2944 | 0.9416 | 0.9115 |
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- | 0.0775 | 5.1440 | 5000 | 0.3047 | 0.9392 | 0.9100 |
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- | 0.0981 | 5.2469 | 5100 | 0.2907 | 0.9369 | 0.9046 |
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- | 0.1059 | 5.3498 | 5200 | 0.2998 | 0.9404 | 0.9091 |
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- | 0.1135 | 5.4527 | 5300 | 0.2842 | 0.9410 | 0.9076 |
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- | 0.1005 | 5.5556 | 5400 | 0.2958 | 0.9387 | 0.9118 |
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- | 0.0902 | 5.6584 | 5500 | 0.2852 | 0.9398 | 0.9077 |
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- | 0.103 | 5.7613 | 5600 | 0.2929 | 0.9375 | 0.9062 |
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- | 0.1027 | 5.8642 | 5700 | 0.3032 | 0.9363 | 0.9042 |
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- | 0.1265 | 5.9671 | 5800 | 0.3035 | 0.9369 | 0.9051 |
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- | 0.0942 | 6.0700 | 5900 | 0.2956 | 0.9410 | 0.9147 |
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- | 0.0858 | 6.1728 | 6000 | 0.2931 | 0.9404 | 0.9105 |
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- | 0.0905 | 6.2757 | 6100 | 0.3160 | 0.9352 | 0.9146 |
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- | 0.0826 | 6.3786 | 6200 | 0.3019 | 0.9381 | 0.9063 |
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- | 0.0666 | 6.4815 | 6300 | 0.2854 | 0.9392 | 0.9028 |
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- | 0.1132 | 6.5844 | 6400 | 0.3054 | 0.9358 | 0.9039 |
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- | 0.091 | 6.6872 | 6500 | 0.3000 | 0.9416 | 0.9115 |
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- | 0.0663 | 6.7901 | 6600 | 0.2956 | 0.9421 | 0.9140 |
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- | 0.0778 | 6.8930 | 6700 | 0.3055 | 0.9421 | 0.9152 |
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- | 0.07 | 6.9959 | 6800 | 0.2994 | 0.9421 | 0.9136 |
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- | 0.0709 | 7.0988 | 6900 | 0.2948 | 0.9392 | 0.9084 |
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- | 0.0573 | 7.2016 | 7000 | 0.2999 | 0.9410 | 0.9166 |
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- | 0.0538 | 7.3045 | 7100 | 0.2954 | 0.9410 | 0.9131 |
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- | 0.0588 | 7.4074 | 7200 | 0.3010 | 0.9392 | 0.9061 |
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- | 0.0764 | 7.5103 | 7300 | 0.2977 | 0.9416 | 0.9164 |
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- | 0.0709 | 7.6132 | 7400 | 0.2991 | 0.9427 | 0.9209 |
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- | 0.0696 | 7.7160 | 7500 | 0.3100 | 0.9416 | 0.9110 |
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- | 0.0725 | 7.8189 | 7600 | 0.3086 | 0.9450 | 0.9165 |
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- | 0.0732 | 7.9218 | 7700 | 0.3160 | 0.9439 | 0.9168 |
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- | 0.0705 | 8.0247 | 7800 | 0.3029 | 0.9421 | 0.9143 |
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- | 0.0556 | 8.1276 | 7900 | 0.2993 | 0.9421 | 0.9127 |
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- | 0.062 | 8.2305 | 8000 | 0.3140 | 0.9416 | 0.9149 |
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- | 0.0597 | 8.3333 | 8100 | 0.3135 | 0.9433 | 0.9202 |
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- | 0.0478 | 8.4362 | 8200 | 0.3016 | 0.9444 | 0.9147 |
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- | 0.0644 | 8.5391 | 8300 | 0.3068 | 0.9421 | 0.9125 |
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- | 0.0562 | 8.6420 | 8400 | 0.3026 | 0.9433 | 0.9132 |
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- | 0.0638 | 8.7449 | 8500 | 0.3046 | 0.9433 | 0.9134 |
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- | 0.0575 | 8.8477 | 8600 | 0.3052 | 0.9433 | 0.9114 |
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- | 0.0597 | 8.9506 | 8700 | 0.3131 | 0.9416 | 0.9145 |
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- | 0.0313 | 9.0535 | 8800 | 0.3119 | 0.9444 | 0.9165 |
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- | 0.0461 | 9.1564 | 8900 | 0.3102 | 0.9427 | 0.9126 |
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- | 0.0589 | 9.2593 | 9000 | 0.3060 | 0.9450 | 0.9158 |
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- | 0.0668 | 9.3621 | 9100 | 0.3116 | 0.9433 | 0.9159 |
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- | 0.0599 | 9.4650 | 9200 | 0.3125 | 0.9433 | 0.9192 |
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- | 0.0524 | 9.5679 | 9300 | 0.3117 | 0.9439 | 0.9176 |
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- | 0.0489 | 9.6708 | 9400 | 0.3082 | 0.9439 | 0.9179 |
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- | 0.0431 | 9.7737 | 9500 | 0.3068 | 0.9450 | 0.9172 |
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- | 0.0518 | 9.8765 | 9600 | 0.3071 | 0.9439 | 0.9139 |
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- | 0.0577 | 9.9794 | 9700 | 0.3077 | 0.9444 | 0.9186 |
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  ### Framework versions
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  - Transformers 4.51.3
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  - Pytorch 2.6.0+cu124
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- - Datasets 3.5.0
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  - Tokenizers 0.21.1
 
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  This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3012
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  - Accuracy: 0.9444
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+ - Macro F1: 0.9147
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
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+ | 2.07 | 0.1029 | 100 | 1.9058 | 0.4144 | 0.1548 |
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+ | 1.7855 | 0.2058 | 200 | 1.6273 | 0.4907 | 0.2162 |
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+ | 1.539 | 0.3086 | 300 | 1.4125 | 0.5683 | 0.2940 |
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+ | 1.2834 | 0.4115 | 400 | 1.1540 | 0.6586 | 0.3591 |
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+ | 1.1157 | 0.5144 | 500 | 0.9695 | 0.7095 | 0.4362 |
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+ | 0.9701 | 0.6173 | 600 | 0.8559 | 0.7488 | 0.5098 |
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+ | 0.848 | 0.7202 | 700 | 0.8231 | 0.7714 | 0.5732 |
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+ | 0.7585 | 0.8230 | 800 | 0.6777 | 0.8171 | 0.6513 |
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+ | 0.698 | 0.9259 | 900 | 0.6211 | 0.8281 | 0.6726 |
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+ | 0.6143 | 1.0288 | 1000 | 0.5631 | 0.8466 | 0.7037 |
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+ | 0.5656 | 1.1317 | 1100 | 0.5575 | 0.8519 | 0.7296 |
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+ | 0.4638 | 1.2346 | 1200 | 0.4738 | 0.8738 | 0.7532 |
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+ | 0.4572 | 1.3374 | 1300 | 0.4279 | 0.8877 | 0.7689 |
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+ | 0.3826 | 1.4403 | 1400 | 0.4296 | 0.8814 | 0.7680 |
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+ | 0.3424 | 1.5432 | 1500 | 0.3643 | 0.9045 | 0.7900 |
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+ | 0.3636 | 1.6461 | 1600 | 0.3541 | 0.9057 | 0.7895 |
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+ | 0.3061 | 1.7490 | 1700 | 0.3264 | 0.9120 | 0.7982 |
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+ | 0.3145 | 1.8519 | 1800 | 0.3232 | 0.9126 | 0.7974 |
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+ | 0.3074 | 1.9547 | 1900 | 0.3124 | 0.9167 | 0.8140 |
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+ | 0.3145 | 2.0576 | 2000 | 0.3228 | 0.9144 | 0.8238 |
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+ | 0.2337 | 2.1605 | 2100 | 0.3152 | 0.9167 | 0.8236 |
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+ | 0.2283 | 2.2634 | 2200 | 0.3145 | 0.9184 | 0.8286 |
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+ | 0.2265 | 2.3663 | 2300 | 0.3230 | 0.9207 | 0.8212 |
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+ | 0.2428 | 2.4691 | 2400 | 0.3038 | 0.9248 | 0.8434 |
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+ | 0.2301 | 2.5720 | 2500 | 0.2946 | 0.9225 | 0.8523 |
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+ | 0.2471 | 2.6749 | 2600 | 0.2937 | 0.9201 | 0.8516 |
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+ | 0.1854 | 2.7778 | 2700 | 0.2864 | 0.9288 | 0.8594 |
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+ | 0.2154 | 2.8807 | 2800 | 0.2896 | 0.9259 | 0.8526 |
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+ | 0.2196 | 2.9835 | 2900 | 0.2891 | 0.9271 | 0.8727 |
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+ | 0.1772 | 3.0864 | 3000 | 0.2884 | 0.9271 | 0.8537 |
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+ | 0.1876 | 3.1893 | 3100 | 0.2827 | 0.9282 | 0.8730 |
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+ | 0.1898 | 3.2922 | 3200 | 0.2698 | 0.9300 | 0.8584 |
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+ | 0.1592 | 3.3951 | 3300 | 0.2923 | 0.9334 | 0.8752 |
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+ | 0.1423 | 3.4979 | 3400 | 0.2738 | 0.9329 | 0.8862 |
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+ | 0.1818 | 3.6008 | 3500 | 0.2811 | 0.9294 | 0.8825 |
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+ | 0.1357 | 3.7037 | 3600 | 0.2758 | 0.9381 | 0.8940 |
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+ | 0.1667 | 3.8066 | 3700 | 0.3017 | 0.9317 | 0.8941 |
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+ | 0.1609 | 3.9095 | 3800 | 0.2881 | 0.9340 | 0.8914 |
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+ | 0.141 | 4.0123 | 3900 | 0.3014 | 0.9329 | 0.9001 |
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+ | 0.0912 | 4.1152 | 4000 | 0.2830 | 0.9346 | 0.9008 |
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+ | 0.1152 | 4.2181 | 4100 | 0.3018 | 0.9329 | 0.8922 |
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+ | 0.1576 | 4.3210 | 4200 | 0.2790 | 0.9375 | 0.9090 |
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+ | 0.1255 | 4.4239 | 4300 | 0.2893 | 0.9329 | 0.8968 |
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+ | 0.1186 | 4.5267 | 4400 | 0.3274 | 0.9265 | 0.8982 |
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+ | 0.1362 | 4.6296 | 4500 | 0.2865 | 0.9363 | 0.9025 |
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+ | 0.1171 | 4.7325 | 4600 | 0.2941 | 0.9358 | 0.9042 |
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+ | 0.1494 | 4.8354 | 4700 | 0.2841 | 0.9352 | 0.9006 |
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+ | 0.1065 | 4.9383 | 4800 | 0.3263 | 0.9300 | 0.9010 |
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+ | 0.104 | 5.0412 | 4900 | 0.2949 | 0.9398 | 0.9146 |
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+ | 0.0767 | 5.1440 | 5000 | 0.3041 | 0.9375 | 0.9025 |
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+ | 0.0924 | 5.2469 | 5100 | 0.2908 | 0.9381 | 0.9125 |
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+ | 0.1106 | 5.3498 | 5200 | 0.3003 | 0.9387 | 0.9006 |
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+ | 0.1099 | 5.4527 | 5300 | 0.2844 | 0.9410 | 0.9084 |
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+ | 0.0985 | 5.5556 | 5400 | 0.2936 | 0.9363 | 0.9070 |
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+ | 0.087 | 5.6584 | 5500 | 0.2828 | 0.9404 | 0.9089 |
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+ | 0.0997 | 5.7613 | 5600 | 0.2881 | 0.9387 | 0.9119 |
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+ | 0.0982 | 5.8642 | 5700 | 0.2950 | 0.9363 | 0.9087 |
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+ | 0.1242 | 5.9671 | 5800 | 0.3027 | 0.9398 | 0.9074 |
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+ | 0.0947 | 6.0700 | 5900 | 0.2926 | 0.9398 | 0.9115 |
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+ | 0.0845 | 6.1728 | 6000 | 0.2823 | 0.9427 | 0.9101 |
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+ | 0.0881 | 6.2757 | 6100 | 0.3071 | 0.9363 | 0.9144 |
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+ | 0.0806 | 6.3786 | 6200 | 0.2972 | 0.9392 | 0.9101 |
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+ | 0.0676 | 6.4815 | 6300 | 0.2839 | 0.9381 | 0.8999 |
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+ | 0.1106 | 6.5844 | 6400 | 0.3066 | 0.9358 | 0.9020 |
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+ | 0.1031 | 6.6872 | 6500 | 0.2971 | 0.9387 | 0.9034 |
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+ | 0.0691 | 6.7901 | 6600 | 0.2932 | 0.9416 | 0.9130 |
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+ | 0.0779 | 6.8930 | 6700 | 0.3020 | 0.9410 | 0.9121 |
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+ | 0.0676 | 6.9959 | 6800 | 0.2931 | 0.9416 | 0.9144 |
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+ | 0.0721 | 7.0988 | 6900 | 0.2877 | 0.9398 | 0.9114 |
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+ | 0.0552 | 7.2016 | 7000 | 0.2978 | 0.9450 | 0.9241 |
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+ | 0.0502 | 7.3045 | 7100 | 0.2899 | 0.9416 | 0.9122 |
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+ | 0.0572 | 7.4074 | 7200 | 0.2941 | 0.9421 | 0.9131 |
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+ | 0.079 | 7.5103 | 7300 | 0.2913 | 0.9427 | 0.9161 |
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+ | 0.0676 | 7.6132 | 7400 | 0.2965 | 0.9410 | 0.9135 |
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+ | 0.0673 | 7.7160 | 7500 | 0.3055 | 0.9404 | 0.9076 |
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+ | 0.0709 | 7.8189 | 7600 | 0.3005 | 0.9421 | 0.9122 |
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+ | 0.0709 | 7.9218 | 7700 | 0.3023 | 0.9416 | 0.9160 |
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+ | 0.0679 | 8.0247 | 7800 | 0.2973 | 0.9410 | 0.9108 |
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+ | 0.0542 | 8.1276 | 7900 | 0.2913 | 0.9427 | 0.9174 |
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+ | 0.0605 | 8.2305 | 8000 | 0.3070 | 0.9421 | 0.9154 |
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+ | 0.0578 | 8.3333 | 8100 | 0.3053 | 0.9410 | 0.9140 |
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+ | 0.0457 | 8.4362 | 8200 | 0.2946 | 0.9450 | 0.9214 |
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+ | 0.0631 | 8.5391 | 8300 | 0.3010 | 0.9433 | 0.9094 |
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+ | 0.0563 | 8.6420 | 8400 | 0.2957 | 0.9433 | 0.9177 |
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+ | 0.0624 | 8.7449 | 8500 | 0.2965 | 0.9468 | 0.9209 |
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+ | 0.0557 | 8.8477 | 8600 | 0.3052 | 0.9416 | 0.9135 |
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+ | 0.058 | 8.9506 | 8700 | 0.3091 | 0.9416 | 0.9129 |
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+ | 0.0319 | 9.0535 | 8800 | 0.3078 | 0.9439 | 0.9156 |
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+ | 0.0454 | 9.1564 | 8900 | 0.3069 | 0.9433 | 0.9109 |
142
+ | 0.0583 | 9.2593 | 9000 | 0.2996 | 0.9450 | 0.9156 |
143
+ | 0.0668 | 9.3621 | 9100 | 0.3026 | 0.9427 | 0.9132 |
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+ | 0.0615 | 9.4650 | 9200 | 0.3065 | 0.9421 | 0.9115 |
145
+ | 0.0506 | 9.5679 | 9300 | 0.3037 | 0.9439 | 0.9150 |
146
+ | 0.0514 | 9.6708 | 9400 | 0.3012 | 0.9450 | 0.9164 |
147
+ | 0.0447 | 9.7737 | 9500 | 0.3004 | 0.9450 | 0.9156 |
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+ | 0.0532 | 9.8765 | 9600 | 0.3009 | 0.9444 | 0.9147 |
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+ | 0.0568 | 9.9794 | 9700 | 0.3012 | 0.9444 | 0.9147 |
150
 
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  ### Framework versions
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154
  - Transformers 4.51.3
155
  - Pytorch 2.6.0+cu124
156
+ - Datasets 2.14.4
157
  - Tokenizers 0.21.1
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