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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_cross_relevance_task6_fold6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# arabert_cross_relevance_task6_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: 0.4430
- Qwk: 0.1465
- Mse: 0.4429
## 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 | 0.1176 | 2 | 0.7830 | 0.0204 | 0.7810 |
| No log | 0.2353 | 4 | 0.3944 | 0.0715 | 0.3940 |
| No log | 0.3529 | 6 | 0.3437 | 0.0480 | 0.3436 |
| No log | 0.4706 | 8 | 0.3309 | 0.1105 | 0.3305 |
| No log | 0.5882 | 10 | 0.2835 | 0.1105 | 0.2836 |
| No log | 0.7059 | 12 | 0.2697 | 0.1105 | 0.2702 |
| No log | 0.8235 | 14 | 0.2695 | 0.1185 | 0.2700 |
| No log | 0.9412 | 16 | 0.2715 | 0.1326 | 0.2721 |
| No log | 1.0588 | 18 | 0.2745 | 0.2102 | 0.2750 |
| No log | 1.1765 | 20 | 0.2896 | 0.2067 | 0.2899 |
| No log | 1.2941 | 22 | 0.2766 | 0.2206 | 0.2770 |
| No log | 1.4118 | 24 | 0.2712 | 0.2420 | 0.2717 |
| No log | 1.5294 | 26 | 0.2809 | 0.2235 | 0.2814 |
| No log | 1.6471 | 28 | 0.2904 | 0.2076 | 0.2908 |
| No log | 1.7647 | 30 | 0.2687 | 0.2352 | 0.2692 |
| No log | 1.8824 | 32 | 0.2581 | 0.2477 | 0.2588 |
| No log | 2.0 | 34 | 0.2565 | 0.2472 | 0.2572 |
| No log | 2.1176 | 36 | 0.2641 | 0.2099 | 0.2646 |
| No log | 2.2353 | 38 | 0.3196 | 0.2033 | 0.3198 |
| No log | 2.3529 | 40 | 0.3095 | 0.2076 | 0.3097 |
| No log | 2.4706 | 42 | 0.2662 | 0.2084 | 0.2667 |
| No log | 2.5882 | 44 | 0.2644 | 0.2790 | 0.2651 |
| No log | 2.7059 | 46 | 0.2676 | 0.2311 | 0.2682 |
| No log | 2.8235 | 48 | 0.2943 | 0.2347 | 0.2946 |
| No log | 2.9412 | 50 | 0.3231 | 0.2150 | 0.3232 |
| No log | 3.0588 | 52 | 0.3309 | 0.2064 | 0.3309 |
| No log | 3.1765 | 54 | 0.3053 | 0.2252 | 0.3056 |
| No log | 3.2941 | 56 | 0.2808 | 0.2266 | 0.2812 |
| No log | 3.4118 | 58 | 0.2831 | 0.2170 | 0.2836 |
| No log | 3.5294 | 60 | 0.2875 | 0.2211 | 0.2881 |
| No log | 3.6471 | 62 | 0.3017 | 0.2140 | 0.3022 |
| No log | 3.7647 | 64 | 0.3078 | 0.2125 | 0.3084 |
| No log | 3.8824 | 66 | 0.3215 | 0.2117 | 0.3220 |
| No log | 4.0 | 68 | 0.3285 | 0.2052 | 0.3288 |
| No log | 4.1176 | 70 | 0.3679 | 0.1824 | 0.3679 |
| No log | 4.2353 | 72 | 0.3668 | 0.1824 | 0.3668 |
| No log | 4.3529 | 74 | 0.3216 | 0.2135 | 0.3219 |
| No log | 4.4706 | 76 | 0.2935 | 0.2301 | 0.2940 |
| No log | 4.5882 | 78 | 0.2944 | 0.2406 | 0.2949 |
| No log | 4.7059 | 80 | 0.3279 | 0.2200 | 0.3282 |
| No log | 4.8235 | 82 | 0.3629 | 0.1977 | 0.3631 |
| No log | 4.9412 | 84 | 0.3823 | 0.1898 | 0.3823 |
| No log | 5.0588 | 86 | 0.3659 | 0.1977 | 0.3659 |
| No log | 5.1765 | 88 | 0.3351 | 0.2019 | 0.3353 |
| No log | 5.2941 | 90 | 0.3442 | 0.2019 | 0.3443 |
| No log | 5.4118 | 92 | 0.3693 | 0.1977 | 0.3694 |
| No log | 5.5294 | 94 | 0.3861 | 0.1937 | 0.3861 |
| No log | 5.6471 | 96 | 0.3622 | 0.1977 | 0.3624 |
| No log | 5.7647 | 98 | 0.3369 | 0.2023 | 0.3373 |
| No log | 5.8824 | 100 | 0.3520 | 0.1939 | 0.3524 |
| No log | 6.0 | 102 | 0.3764 | 0.1898 | 0.3766 |
| No log | 6.1176 | 104 | 0.4008 | 0.1975 | 0.4009 |
| No log | 6.2353 | 106 | 0.4228 | 0.1899 | 0.4229 |
| No log | 6.3529 | 108 | 0.4377 | 0.1725 | 0.4376 |
| No log | 6.4706 | 110 | 0.4032 | 0.1824 | 0.4033 |
| No log | 6.5882 | 112 | 0.3828 | 0.1937 | 0.3829 |
| No log | 6.7059 | 114 | 0.4023 | 0.1975 | 0.4023 |
| No log | 6.8235 | 116 | 0.4098 | 0.1650 | 0.4097 |
| No log | 6.9412 | 118 | 0.4555 | 0.1465 | 0.4553 |
| No log | 7.0588 | 120 | 0.5148 | 0.1339 | 0.5144 |
| No log | 7.1765 | 122 | 0.5125 | 0.1339 | 0.5122 |
| No log | 7.2941 | 124 | 0.4645 | 0.1465 | 0.4643 |
| No log | 7.4118 | 126 | 0.3966 | 0.1751 | 0.3967 |
| No log | 7.5294 | 128 | 0.3450 | 0.1979 | 0.3452 |
| No log | 7.6471 | 130 | 0.3262 | 0.2210 | 0.3265 |
| No log | 7.7647 | 132 | 0.3261 | 0.2210 | 0.3265 |
| No log | 7.8824 | 134 | 0.3438 | 0.1979 | 0.3441 |
| No log | 8.0 | 136 | 0.3772 | 0.2015 | 0.3774 |
| No log | 8.1176 | 138 | 0.4155 | 0.1719 | 0.4155 |
| No log | 8.2353 | 140 | 0.4512 | 0.1528 | 0.4511 |
| No log | 8.3529 | 142 | 0.4592 | 0.1404 | 0.4590 |
| No log | 8.4706 | 144 | 0.4422 | 0.1465 | 0.4421 |
| No log | 8.5882 | 146 | 0.4170 | 0.1620 | 0.4170 |
| No log | 8.7059 | 148 | 0.4105 | 0.1757 | 0.4105 |
| No log | 8.8235 | 150 | 0.4205 | 0.1592 | 0.4204 |
| No log | 8.9412 | 152 | 0.4328 | 0.1465 | 0.4327 |
| No log | 9.0588 | 154 | 0.4368 | 0.1465 | 0.4367 |
| No log | 9.1765 | 156 | 0.4351 | 0.1465 | 0.4350 |
| No log | 9.2941 | 158 | 0.4352 | 0.1465 | 0.4352 |
| No log | 9.4118 | 160 | 0.4333 | 0.1465 | 0.4332 |
| No log | 9.5294 | 162 | 0.4373 | 0.1465 | 0.4372 |
| No log | 9.6471 | 164 | 0.4392 | 0.1465 | 0.4391 |
| No log | 9.7647 | 166 | 0.4394 | 0.1465 | 0.4393 |
| No log | 9.8824 | 168 | 0.4416 | 0.1465 | 0.4415 |
| No log | 10.0 | 170 | 0.4430 | 0.1465 | 0.4429 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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