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--- |
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base_model: aubmindlab/bert-base-arabertv02 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: arabert_cross_relevance_task4_fold3 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# arabert_cross_relevance_task4_fold3 |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3037 |
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- Qwk: 0.3732 |
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- Mse: 0.3037 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:| |
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| No log | 0.1176 | 2 | 0.5630 | 0.1562 | 0.5630 | |
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| No log | 0.2353 | 4 | 0.4644 | 0.2174 | 0.4644 | |
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| No log | 0.3529 | 6 | 0.3950 | 0.1962 | 0.3950 | |
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| No log | 0.4706 | 8 | 0.3834 | 0.2684 | 0.3834 | |
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| No log | 0.5882 | 10 | 0.3306 | 0.3081 | 0.3306 | |
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| No log | 0.7059 | 12 | 0.3268 | 0.2811 | 0.3268 | |
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| No log | 0.8235 | 14 | 0.3154 | 0.3011 | 0.3154 | |
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| No log | 0.9412 | 16 | 0.3060 | 0.3218 | 0.3060 | |
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| No log | 1.0588 | 18 | 0.3117 | 0.3651 | 0.3117 | |
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| No log | 1.1765 | 20 | 0.3057 | 0.3773 | 0.3057 | |
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| No log | 1.2941 | 22 | 0.3034 | 0.3911 | 0.3034 | |
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| No log | 1.4118 | 24 | 0.2791 | 0.4002 | 0.2791 | |
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| No log | 1.5294 | 26 | 0.2627 | 0.4023 | 0.2627 | |
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| No log | 1.6471 | 28 | 0.2813 | 0.3853 | 0.2813 | |
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| No log | 1.7647 | 30 | 0.3082 | 0.3740 | 0.3082 | |
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| No log | 1.8824 | 32 | 0.2928 | 0.3614 | 0.2928 | |
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| No log | 2.0 | 34 | 0.2792 | 0.3430 | 0.2792 | |
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| No log | 2.1176 | 36 | 0.2640 | 0.3836 | 0.2640 | |
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| No log | 2.2353 | 38 | 0.2797 | 0.4090 | 0.2797 | |
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| No log | 2.3529 | 40 | 0.3047 | 0.3991 | 0.3047 | |
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| No log | 2.4706 | 42 | 0.3233 | 0.3483 | 0.3233 | |
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| No log | 2.5882 | 44 | 0.3173 | 0.3975 | 0.3173 | |
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| No log | 2.7059 | 46 | 0.2794 | 0.3702 | 0.2794 | |
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| No log | 2.8235 | 48 | 0.2545 | 0.3595 | 0.2545 | |
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| No log | 2.9412 | 50 | 0.2598 | 0.3274 | 0.2598 | |
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| No log | 3.0588 | 52 | 0.3170 | 0.2780 | 0.3170 | |
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| No log | 3.1765 | 54 | 0.3616 | 0.2701 | 0.3616 | |
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| No log | 3.2941 | 56 | 0.3399 | 0.3799 | 0.3399 | |
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| No log | 3.4118 | 58 | 0.2912 | 0.4991 | 0.2912 | |
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| No log | 3.5294 | 60 | 0.2788 | 0.5808 | 0.2788 | |
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| No log | 3.6471 | 62 | 0.2781 | 0.5944 | 0.2781 | |
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| No log | 3.7647 | 64 | 0.2655 | 0.5547 | 0.2655 | |
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| No log | 3.8824 | 66 | 0.2739 | 0.4479 | 0.2739 | |
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| No log | 4.0 | 68 | 0.3052 | 0.3580 | 0.3052 | |
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| No log | 4.1176 | 70 | 0.3257 | 0.2703 | 0.3257 | |
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| No log | 4.2353 | 72 | 0.3077 | 0.2765 | 0.3077 | |
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| No log | 4.3529 | 74 | 0.2673 | 0.3446 | 0.2673 | |
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| No log | 4.4706 | 76 | 0.2558 | 0.4205 | 0.2558 | |
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| No log | 4.5882 | 78 | 0.2699 | 0.4751 | 0.2699 | |
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| No log | 4.7059 | 80 | 0.3058 | 0.5205 | 0.3058 | |
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| No log | 4.8235 | 82 | 0.3248 | 0.5030 | 0.3248 | |
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| No log | 4.9412 | 84 | 0.3083 | 0.4300 | 0.3083 | |
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| No log | 5.0588 | 86 | 0.2844 | 0.3512 | 0.2844 | |
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| No log | 5.1765 | 88 | 0.2595 | 0.3405 | 0.2595 | |
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| No log | 5.2941 | 90 | 0.2544 | 0.3462 | 0.2544 | |
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| No log | 5.4118 | 92 | 0.2686 | 0.3378 | 0.2686 | |
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| No log | 5.5294 | 94 | 0.3037 | 0.3446 | 0.3037 | |
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| No log | 5.6471 | 96 | 0.3453 | 0.3453 | 0.3453 | |
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| No log | 5.7647 | 98 | 0.3438 | 0.3956 | 0.3438 | |
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| No log | 5.8824 | 100 | 0.3084 | 0.4184 | 0.3084 | |
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| No log | 6.0 | 102 | 0.2757 | 0.4015 | 0.2757 | |
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| No log | 6.1176 | 104 | 0.2744 | 0.3963 | 0.2744 | |
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| No log | 6.2353 | 106 | 0.2900 | 0.4343 | 0.2900 | |
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| No log | 6.3529 | 108 | 0.3167 | 0.4357 | 0.3167 | |
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| No log | 6.4706 | 110 | 0.3249 | 0.4225 | 0.3249 | |
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| No log | 6.5882 | 112 | 0.3104 | 0.3929 | 0.3104 | |
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| No log | 6.7059 | 114 | 0.2965 | 0.3961 | 0.2965 | |
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| No log | 6.8235 | 116 | 0.2870 | 0.3956 | 0.2870 | |
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| No log | 6.9412 | 118 | 0.2835 | 0.4017 | 0.2835 | |
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| No log | 7.0588 | 120 | 0.2798 | 0.4117 | 0.2798 | |
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| No log | 7.1765 | 122 | 0.2952 | 0.3838 | 0.2952 | |
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| No log | 7.2941 | 124 | 0.3094 | 0.3838 | 0.3094 | |
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| No log | 7.4118 | 126 | 0.3254 | 0.4226 | 0.3254 | |
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| No log | 7.5294 | 128 | 0.3223 | 0.4122 | 0.3223 | |
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| No log | 7.6471 | 130 | 0.3091 | 0.3910 | 0.3091 | |
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| No log | 7.7647 | 132 | 0.3042 | 0.3896 | 0.3042 | |
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| No log | 7.8824 | 134 | 0.2977 | 0.3822 | 0.2977 | |
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| No log | 8.0 | 136 | 0.2987 | 0.3536 | 0.2987 | |
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| No log | 8.1176 | 138 | 0.3110 | 0.3437 | 0.3110 | |
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| No log | 8.2353 | 140 | 0.3121 | 0.3392 | 0.3121 | |
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| No log | 8.3529 | 142 | 0.3056 | 0.3402 | 0.3056 | |
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| No log | 8.4706 | 144 | 0.3047 | 0.3462 | 0.3047 | |
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| No log | 8.5882 | 146 | 0.3035 | 0.3572 | 0.3035 | |
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| No log | 8.7059 | 148 | 0.3024 | 0.3690 | 0.3024 | |
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| No log | 8.8235 | 150 | 0.3034 | 0.3717 | 0.3034 | |
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| No log | 8.9412 | 152 | 0.3012 | 0.3774 | 0.3012 | |
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| No log | 9.0588 | 154 | 0.2988 | 0.3774 | 0.2988 | |
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| No log | 9.1765 | 156 | 0.3009 | 0.3774 | 0.3009 | |
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| No log | 9.2941 | 158 | 0.3001 | 0.3889 | 0.3001 | |
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| No log | 9.4118 | 160 | 0.3015 | 0.3946 | 0.3015 | |
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| No log | 9.5294 | 162 | 0.3032 | 0.3946 | 0.3032 | |
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| No log | 9.6471 | 164 | 0.3050 | 0.3846 | 0.3050 | |
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| No log | 9.7647 | 166 | 0.3044 | 0.3732 | 0.3044 | |
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| No log | 9.8824 | 168 | 0.3039 | 0.3732 | 0.3039 | |
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| No log | 10.0 | 170 | 0.3037 | 0.3732 | 0.3037 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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