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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_baseline_organization_task5_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_task5_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: 0.5797
- Qwk: 0.6737
- Mse: 0.5797
## 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 | 1.3224 | 0.1029 | 1.3224 |
| No log | 0.6667 | 4 | 1.1724 | 0.0 | 1.1724 |
| No log | 1.0 | 6 | 1.0188 | 0.0 | 1.0188 |
| No log | 1.3333 | 8 | 0.9367 | 0.0 | 0.9367 |
| No log | 1.6667 | 10 | 0.9707 | 0.0461 | 0.9707 |
| No log | 2.0 | 12 | 1.0017 | 0.0940 | 1.0017 |
| No log | 2.3333 | 14 | 1.0101 | 0.3066 | 1.0101 |
| No log | 2.6667 | 16 | 0.9862 | 0.2453 | 0.9862 |
| No log | 3.0 | 18 | 0.9340 | 0.3962 | 0.9340 |
| No log | 3.3333 | 20 | 0.8741 | 0.4096 | 0.8741 |
| No log | 3.6667 | 22 | 0.8431 | 0.4403 | 0.8431 |
| No log | 4.0 | 24 | 0.7909 | 0.4898 | 0.7909 |
| No log | 4.3333 | 26 | 0.7355 | 0.5 | 0.7355 |
| No log | 4.6667 | 28 | 0.6751 | 0.6084 | 0.6751 |
| No log | 5.0 | 30 | 0.6350 | 0.5224 | 0.6350 |
| No log | 5.3333 | 32 | 0.6127 | 0.5224 | 0.6127 |
| No log | 5.6667 | 34 | 0.6032 | 0.5810 | 0.6032 |
| No log | 6.0 | 36 | 0.5973 | 0.5810 | 0.5973 |
| No log | 6.3333 | 38 | 0.5902 | 0.5810 | 0.5902 |
| No log | 6.6667 | 40 | 0.5956 | 0.7043 | 0.5956 |
| No log | 7.0 | 42 | 0.5976 | 0.7043 | 0.5976 |
| No log | 7.3333 | 44 | 0.5970 | 0.6401 | 0.5970 |
| No log | 7.6667 | 46 | 0.6061 | 0.6114 | 0.6061 |
| No log | 8.0 | 48 | 0.6064 | 0.6114 | 0.6064 |
| No log | 8.3333 | 50 | 0.5917 | 0.6401 | 0.5917 |
| No log | 8.6667 | 52 | 0.5644 | 0.6737 | 0.5644 |
| No log | 9.0 | 54 | 0.5637 | 0.6737 | 0.5637 |
| No log | 9.3333 | 56 | 0.5746 | 0.6737 | 0.5746 |
| No log | 9.6667 | 58 | 0.5782 | 0.6737 | 0.5782 |
| No log | 10.0 | 60 | 0.5797 | 0.6737 | 0.5797 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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