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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_baseline_vocabulary_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_vocabulary_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: 0.6498
- Qwk: 0.6216
- Mse: 0.6722
## 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 | 4.6252 | -0.0554 | 4.6618 |
| No log | 0.6667 | 4 | 1.9114 | 0.1179 | 1.9451 |
| No log | 1.0 | 6 | 0.9690 | 0.1617 | 1.0110 |
| No log | 1.3333 | 8 | 0.8689 | 0.3771 | 0.9143 |
| No log | 1.6667 | 10 | 0.7767 | 0.3636 | 0.8305 |
| No log | 2.0 | 12 | 0.7835 | 0.3652 | 0.8429 |
| No log | 2.3333 | 14 | 0.7429 | 0.5097 | 0.7956 |
| No log | 2.6667 | 16 | 0.7735 | 0.4543 | 0.8152 |
| No log | 3.0 | 18 | 0.8347 | 0.4231 | 0.8732 |
| No log | 3.3333 | 20 | 0.7428 | 0.4755 | 0.7774 |
| No log | 3.6667 | 22 | 0.7764 | 0.5241 | 0.8145 |
| No log | 4.0 | 24 | 0.7551 | 0.5241 | 0.7880 |
| No log | 4.3333 | 26 | 0.7760 | 0.4815 | 0.8052 |
| No log | 4.6667 | 28 | 0.8464 | 0.4103 | 0.8794 |
| No log | 5.0 | 30 | 0.7218 | 0.5290 | 0.7519 |
| No log | 5.3333 | 32 | 0.7204 | 0.5241 | 0.7555 |
| No log | 5.6667 | 34 | 0.7510 | 0.5634 | 0.7913 |
| No log | 6.0 | 36 | 0.6866 | 0.5634 | 0.7239 |
| No log | 6.3333 | 38 | 0.6265 | 0.5592 | 0.6598 |
| No log | 6.6667 | 40 | 0.6769 | 0.5913 | 0.7100 |
| No log | 7.0 | 42 | 0.6960 | 0.5913 | 0.7277 |
| No log | 7.3333 | 44 | 0.6638 | 0.5913 | 0.6914 |
| No log | 7.6667 | 46 | 0.6521 | 0.5987 | 0.6776 |
| No log | 8.0 | 48 | 0.6739 | 0.6369 | 0.6999 |
| No log | 8.3333 | 50 | 0.6666 | 0.6369 | 0.6916 |
| No log | 8.6667 | 52 | 0.6558 | 0.6655 | 0.6793 |
| No log | 9.0 | 54 | 0.6498 | 0.6216 | 0.6729 |
| No log | 9.3333 | 56 | 0.6487 | 0.6216 | 0.6712 |
| No log | 9.6667 | 58 | 0.6495 | 0.6216 | 0.6718 |
| No log | 10.0 | 60 | 0.6498 | 0.6216 | 0.6722 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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