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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_cross_relevance_task3_fold2
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_task3_fold2
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: 0.3250
- Qwk: -0.0612
- Mse: 0.3250
## 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 | 0.1111 | 2 | 0.3649 | 0.0417 | 0.3649 |
| No log | 0.2222 | 4 | 0.3523 | 0.0574 | 0.3523 |
| No log | 0.3333 | 6 | 0.3294 | -0.0145 | 0.3294 |
| No log | 0.4444 | 8 | 0.3150 | 0.0 | 0.3150 |
| No log | 0.5556 | 10 | 0.3425 | -0.0766 | 0.3425 |
| No log | 0.6667 | 12 | 0.5181 | -0.0897 | 0.5181 |
| No log | 0.7778 | 14 | 0.5767 | -0.0417 | 0.5767 |
| No log | 0.8889 | 16 | 0.4852 | 0.0584 | 0.4852 |
| No log | 1.0 | 18 | 0.4593 | 0.0244 | 0.4593 |
| No log | 1.1111 | 20 | 0.4073 | -0.0106 | 0.4073 |
| No log | 1.2222 | 22 | 0.3911 | 0.1832 | 0.3911 |
| No log | 1.3333 | 24 | 0.5107 | 0.0132 | 0.5107 |
| No log | 1.4444 | 26 | 0.5836 | 0.0068 | 0.5836 |
| No log | 1.5556 | 28 | 0.4981 | 0.0584 | 0.4981 |
| No log | 1.6667 | 30 | 0.4625 | -0.0802 | 0.4625 |
| No log | 1.7778 | 32 | 0.4676 | -0.0714 | 0.4676 |
| No log | 1.8889 | 34 | 0.4572 | -0.1310 | 0.4572 |
| No log | 2.0 | 36 | 0.4709 | -0.0802 | 0.4709 |
| No log | 2.1111 | 38 | 0.4247 | -0.1559 | 0.4247 |
| No log | 2.2222 | 40 | 0.4232 | -0.1413 | 0.4232 |
| No log | 2.3333 | 42 | 0.3999 | -0.1667 | 0.3999 |
| No log | 2.4444 | 44 | 0.4332 | -0.1813 | 0.4332 |
| No log | 2.5556 | 46 | 0.3931 | -0.1574 | 0.3931 |
| No log | 2.6667 | 48 | 0.3256 | -0.1331 | 0.3256 |
| No log | 2.7778 | 50 | 0.3008 | -0.0235 | 0.3008 |
| No log | 2.8889 | 52 | 0.3005 | -0.0473 | 0.3005 |
| No log | 3.0 | 54 | 0.3003 | -0.0714 | 0.3003 |
| No log | 3.1111 | 56 | 0.3285 | -0.0507 | 0.3285 |
| No log | 3.2222 | 58 | 0.3502 | -0.1508 | 0.3502 |
| No log | 3.3333 | 60 | 0.3399 | -0.0556 | 0.3399 |
| No log | 3.4444 | 62 | 0.3233 | -0.0563 | 0.3233 |
| No log | 3.5556 | 64 | 0.3562 | -0.0833 | 0.3562 |
| No log | 3.6667 | 66 | 0.3846 | -0.0981 | 0.3846 |
| No log | 3.7778 | 68 | 0.3873 | -0.2037 | 0.3873 |
| No log | 3.8889 | 70 | 0.3435 | -0.0870 | 0.3435 |
| No log | 4.0 | 72 | 0.3453 | -0.0971 | 0.3453 |
| No log | 4.1111 | 74 | 0.3477 | -0.1594 | 0.3477 |
| No log | 4.2222 | 76 | 0.3299 | -0.0563 | 0.3299 |
| No log | 4.3333 | 78 | 0.3344 | -0.0507 | 0.3344 |
| No log | 4.4444 | 80 | 0.3511 | -0.2097 | 0.3511 |
| No log | 4.5556 | 82 | 0.3423 | -0.1154 | 0.3423 |
| No log | 4.6667 | 84 | 0.3303 | -0.0971 | 0.3303 |
| No log | 4.7778 | 86 | 0.3029 | -0.0473 | 0.3029 |
| No log | 4.8889 | 88 | 0.2956 | -0.0235 | 0.2956 |
| No log | 5.0 | 90 | 0.2958 | -0.0235 | 0.2958 |
| No log | 5.1111 | 92 | 0.2967 | -0.0235 | 0.2967 |
| No log | 5.2222 | 94 | 0.3097 | -0.0616 | 0.3097 |
| No log | 5.3333 | 96 | 0.3615 | -0.0366 | 0.3615 |
| No log | 5.4444 | 98 | 0.4248 | -0.1224 | 0.4248 |
| No log | 5.5556 | 100 | 0.4152 | -0.1000 | 0.4152 |
| No log | 5.6667 | 102 | 0.3541 | 0.0200 | 0.3541 |
| No log | 5.7778 | 104 | 0.3081 | -0.0764 | 0.3081 |
| No log | 5.8889 | 106 | 0.2999 | -0.0473 | 0.2999 |
| No log | 6.0 | 108 | 0.3089 | -0.0616 | 0.3089 |
| No log | 6.1111 | 110 | 0.3305 | -0.1620 | 0.3305 |
| No log | 6.2222 | 112 | 0.3428 | -0.1397 | 0.3428 |
| No log | 6.3333 | 114 | 0.3362 | -0.1496 | 0.3362 |
| No log | 6.4444 | 116 | 0.3379 | -0.1194 | 0.3379 |
| No log | 6.5556 | 118 | 0.3505 | -0.0827 | 0.3505 |
| No log | 6.6667 | 120 | 0.3567 | -0.0827 | 0.3567 |
| No log | 6.7778 | 122 | 0.3610 | -0.0772 | 0.3610 |
| No log | 6.8889 | 124 | 0.3491 | -0.0769 | 0.3491 |
| No log | 7.0 | 126 | 0.3359 | -0.0448 | 0.3359 |
| No log | 7.1111 | 128 | 0.3213 | -0.0714 | 0.3213 |
| No log | 7.2222 | 130 | 0.3241 | -0.0714 | 0.3241 |
| No log | 7.3333 | 132 | 0.3400 | -0.0985 | 0.3400 |
| No log | 7.4444 | 134 | 0.3581 | -0.0537 | 0.3581 |
| No log | 7.5556 | 136 | 0.3622 | -0.0833 | 0.3622 |
| No log | 7.6667 | 138 | 0.3448 | -0.0659 | 0.3448 |
| No log | 7.7778 | 140 | 0.3219 | -0.0764 | 0.3219 |
| No log | 7.8889 | 142 | 0.3076 | -0.0616 | 0.3076 |
| No log | 8.0 | 144 | 0.3064 | -0.0616 | 0.3064 |
| No log | 8.1111 | 146 | 0.3132 | -0.0616 | 0.3132 |
| No log | 8.2222 | 148 | 0.3268 | -0.0612 | 0.3268 |
| No log | 8.3333 | 150 | 0.3441 | -0.1260 | 0.3441 |
| No log | 8.4444 | 152 | 0.3508 | -0.1328 | 0.3508 |
| No log | 8.5556 | 154 | 0.3460 | -0.1154 | 0.3460 |
| No log | 8.6667 | 156 | 0.3385 | -0.0606 | 0.3385 |
| No log | 8.7778 | 158 | 0.3312 | -0.0294 | 0.3312 |
| No log | 8.8889 | 160 | 0.3268 | -0.0294 | 0.3268 |
| No log | 9.0 | 162 | 0.3251 | -0.0507 | 0.3251 |
| No log | 9.1111 | 164 | 0.3282 | -0.0294 | 0.3282 |
| No log | 9.2222 | 166 | 0.3331 | -0.1090 | 0.3331 |
| No log | 9.3333 | 168 | 0.3347 | -0.1090 | 0.3347 |
| No log | 9.4444 | 170 | 0.3357 | -0.1090 | 0.3357 |
| No log | 9.5556 | 172 | 0.3343 | -0.1090 | 0.3343 |
| No log | 9.6667 | 174 | 0.3318 | -0.0556 | 0.3318 |
| No log | 9.7778 | 176 | 0.3286 | -0.0662 | 0.3286 |
| No log | 9.8889 | 178 | 0.3261 | -0.0766 | 0.3261 |
| No log | 10.0 | 180 | 0.3250 | -0.0612 | 0.3250 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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