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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_cross_relevance_task1_fold4
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_task1_fold4
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: 0.2814
- Qwk: 0.1577
- Mse: 0.2814
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|
| No log | 0.0328 | 2 | 0.5567 | -0.0320 | 0.5567 |
| No log | 0.0656 | 4 | 0.5564 | -0.0102 | 0.5564 |
| No log | 0.0984 | 6 | 0.5081 | 0.0304 | 0.5081 |
| No log | 0.1311 | 8 | 0.4677 | 0.0304 | 0.4677 |
| No log | 0.1639 | 10 | 0.4282 | 0.0400 | 0.4282 |
| No log | 0.1967 | 12 | 0.4239 | 0.0496 | 0.4239 |
| No log | 0.2295 | 14 | 0.4171 | 0.0384 | 0.4171 |
| No log | 0.2623 | 16 | 0.3868 | 0.0496 | 0.3868 |
| No log | 0.2951 | 18 | 0.3678 | 0.0690 | 0.3678 |
| No log | 0.3279 | 20 | 0.3448 | 0.0596 | 0.3448 |
| No log | 0.3607 | 22 | 0.3295 | 0.0877 | 0.3295 |
| No log | 0.3934 | 24 | 0.3190 | 0.0877 | 0.3190 |
| No log | 0.4262 | 26 | 0.3166 | 0.0969 | 0.3166 |
| No log | 0.4590 | 28 | 0.3135 | 0.0969 | 0.3135 |
| No log | 0.4918 | 30 | 0.3075 | 0.0969 | 0.3075 |
| No log | 0.5246 | 32 | 0.3014 | 0.1139 | 0.3014 |
| No log | 0.5574 | 34 | 0.2971 | 0.1139 | 0.2971 |
| No log | 0.5902 | 36 | 0.2957 | 0.1048 | 0.2957 |
| No log | 0.6230 | 38 | 0.2937 | 0.1048 | 0.2937 |
| No log | 0.6557 | 40 | 0.2888 | 0.1139 | 0.2888 |
| No log | 0.6885 | 42 | 0.2867 | 0.1139 | 0.2867 |
| No log | 0.7213 | 44 | 0.2857 | 0.1139 | 0.2857 |
| No log | 0.7541 | 46 | 0.2847 | 0.1321 | 0.2847 |
| No log | 0.7869 | 48 | 0.2833 | 0.1577 | 0.2833 |
| No log | 0.8197 | 50 | 0.2826 | 0.1577 | 0.2826 |
| No log | 0.8525 | 52 | 0.2822 | 0.1577 | 0.2822 |
| No log | 0.8852 | 54 | 0.2805 | 0.1577 | 0.2805 |
| No log | 0.9180 | 56 | 0.2810 | 0.1667 | 0.2810 |
| No log | 0.9508 | 58 | 0.2816 | 0.1667 | 0.2816 |
| No log | 0.9836 | 60 | 0.2814 | 0.1577 | 0.2814 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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