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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_baseline_organization_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_organization_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: 0.7080
- Qwk: 0.6596
- Mse: 0.7206
## 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 | 4.7375 | -0.0187 | 4.6528 |
| No log | 0.6667 | 4 | 2.9523 | 0.0354 | 2.8976 |
| No log | 1.0 | 6 | 1.9933 | 0.0369 | 1.9520 |
| No log | 1.3333 | 8 | 1.4358 | 0.1414 | 1.4243 |
| No log | 1.6667 | 10 | 1.3507 | 0.0742 | 1.3574 |
| No log | 2.0 | 12 | 1.2869 | 0.3309 | 1.2992 |
| No log | 2.3333 | 14 | 1.3060 | 0.4324 | 1.3235 |
| No log | 2.6667 | 16 | 1.2124 | 0.4085 | 1.2278 |
| No log | 3.0 | 18 | 1.0567 | 0.4854 | 1.0647 |
| No log | 3.3333 | 20 | 0.9500 | 0.4639 | 0.9565 |
| No log | 3.6667 | 22 | 0.8719 | 0.5769 | 0.8846 |
| No log | 4.0 | 24 | 0.7991 | 0.5769 | 0.8115 |
| No log | 4.3333 | 26 | 0.7537 | 0.5769 | 0.7666 |
| No log | 4.6667 | 28 | 0.7147 | 0.5514 | 0.7274 |
| No log | 5.0 | 30 | 0.6387 | 0.5811 | 0.6471 |
| No log | 5.3333 | 32 | 0.6221 | 0.5748 | 0.6286 |
| No log | 5.6667 | 34 | 0.6363 | 0.5811 | 0.6453 |
| No log | 6.0 | 36 | 0.6849 | 0.5811 | 0.6955 |
| No log | 6.3333 | 38 | 0.6868 | 0.5811 | 0.6971 |
| No log | 6.6667 | 40 | 0.6843 | 0.5435 | 0.6932 |
| No log | 7.0 | 42 | 0.7048 | 0.5081 | 0.7131 |
| No log | 7.3333 | 44 | 0.7116 | 0.5804 | 0.7201 |
| No log | 7.6667 | 46 | 0.7162 | 0.5779 | 0.7250 |
| No log | 8.0 | 48 | 0.7262 | 0.5779 | 0.7360 |
| No log | 8.3333 | 50 | 0.7157 | 0.5779 | 0.7258 |
| No log | 8.6667 | 52 | 0.7189 | 0.5779 | 0.7301 |
| No log | 9.0 | 54 | 0.7090 | 0.6547 | 0.7206 |
| No log | 9.3333 | 56 | 0.7059 | 0.6547 | 0.7179 |
| No log | 9.6667 | 58 | 0.7067 | 0.6547 | 0.7191 |
| No log | 10.0 | 60 | 0.7080 | 0.6596 | 0.7206 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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