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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_baseline_mechanics_task1_fold0
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_baseline_mechanics_task1_fold0
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: 0.6765
- Qwk: 0.4590
- Mse: 0.6922
## 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: 16
- eval_batch_size: 16
- 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 | 0.3333 | 2 | 4.5225 | -0.0658 | 4.5651 |
| No log | 0.6667 | 4 | 1.8523 | 0.1370 | 1.8774 |
| No log | 1.0 | 6 | 0.9383 | 0.2326 | 0.9611 |
| No log | 1.3333 | 8 | 0.8679 | 0.3119 | 0.8947 |
| No log | 1.6667 | 10 | 0.8994 | 0.4015 | 0.9273 |
| No log | 2.0 | 12 | 1.2109 | 0.0582 | 1.2468 |
| No log | 2.3333 | 14 | 1.0652 | 0.0783 | 1.0979 |
| No log | 2.6667 | 16 | 0.8918 | 0.4726 | 0.9192 |
| No log | 3.0 | 18 | 0.9069 | 0.3931 | 0.9346 |
| No log | 3.3333 | 20 | 0.8533 | 0.3636 | 0.8812 |
| No log | 3.6667 | 22 | 0.7332 | 0.4582 | 0.7567 |
| No log | 4.0 | 24 | 0.7505 | 0.5333 | 0.7733 |
| No log | 4.3333 | 26 | 0.7390 | 0.475 | 0.7612 |
| No log | 4.6667 | 28 | 0.7760 | 0.3824 | 0.8000 |
| No log | 5.0 | 30 | 0.8078 | 0.3913 | 0.8335 |
| No log | 5.3333 | 32 | 0.7440 | 0.4450 | 0.7671 |
| No log | 5.6667 | 34 | 0.7175 | 0.4906 | 0.7395 |
| No log | 6.0 | 36 | 0.7050 | 0.5254 | 0.7261 |
| No log | 6.3333 | 38 | 0.7088 | 0.4450 | 0.7299 |
| No log | 6.6667 | 40 | 0.6952 | 0.4727 | 0.7141 |
| No log | 7.0 | 42 | 0.6847 | 0.4770 | 0.7014 |
| No log | 7.3333 | 44 | 0.6839 | 0.5054 | 0.7003 |
| No log | 7.6667 | 46 | 0.6830 | 0.4822 | 0.6983 |
| No log | 8.0 | 48 | 0.6803 | 0.4822 | 0.6954 |
| No log | 8.3333 | 50 | 0.6819 | 0.4279 | 0.6979 |
| No log | 8.6667 | 52 | 0.6851 | 0.4360 | 0.7017 |
| No log | 9.0 | 54 | 0.6844 | 0.4360 | 0.7010 |
| No log | 9.3333 | 56 | 0.6804 | 0.4360 | 0.6965 |
| No log | 9.6667 | 58 | 0.6769 | 0.4590 | 0.6926 |
| No log | 10.0 | 60 | 0.6765 | 0.4590 | 0.6922 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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