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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_cross_vocabulary_task4_fold1
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_vocabulary_task4_fold1
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: 0.5474
- Qwk: 0.4497
- Mse: 0.5474
## 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 | 0.0308 | 2 | 8.1440 | -0.0005 | 8.1440 |
| No log | 0.0615 | 4 | 4.7743 | 0.0021 | 4.7743 |
| No log | 0.0923 | 6 | 2.7118 | 0.0416 | 2.7118 |
| No log | 0.1231 | 8 | 1.7150 | 0.0799 | 1.7150 |
| No log | 0.1538 | 10 | 0.8541 | 0.1088 | 0.8541 |
| No log | 0.1846 | 12 | 0.8601 | 0.1094 | 0.8601 |
| No log | 0.2154 | 14 | 0.8470 | 0.1589 | 0.8470 |
| No log | 0.2462 | 16 | 1.0145 | 0.1789 | 1.0145 |
| No log | 0.2769 | 18 | 1.4913 | 0.1666 | 1.4913 |
| No log | 0.3077 | 20 | 1.4930 | 0.2073 | 1.4930 |
| No log | 0.3385 | 22 | 0.8055 | 0.3243 | 0.8055 |
| No log | 0.3692 | 24 | 0.5508 | 0.4354 | 0.5508 |
| No log | 0.4 | 26 | 0.6031 | 0.3790 | 0.6031 |
| No log | 0.4308 | 28 | 0.5860 | 0.4121 | 0.5860 |
| No log | 0.4615 | 30 | 0.6336 | 0.4190 | 0.6336 |
| No log | 0.4923 | 32 | 0.9905 | 0.3071 | 0.9905 |
| No log | 0.5231 | 34 | 1.0889 | 0.2932 | 1.0889 |
| No log | 0.5538 | 36 | 0.8745 | 0.3389 | 0.8745 |
| No log | 0.5846 | 38 | 0.7332 | 0.3681 | 0.7332 |
| No log | 0.6154 | 40 | 0.6486 | 0.3927 | 0.6486 |
| No log | 0.6462 | 42 | 0.6347 | 0.3914 | 0.6347 |
| No log | 0.6769 | 44 | 0.6439 | 0.3923 | 0.6439 |
| No log | 0.7077 | 46 | 0.6224 | 0.4042 | 0.6224 |
| No log | 0.7385 | 48 | 0.6224 | 0.4075 | 0.6224 |
| No log | 0.7692 | 50 | 0.5740 | 0.4350 | 0.5740 |
| No log | 0.8 | 52 | 0.5606 | 0.4443 | 0.5606 |
| No log | 0.8308 | 54 | 0.5580 | 0.4473 | 0.5580 |
| No log | 0.8615 | 56 | 0.5443 | 0.4451 | 0.5443 |
| No log | 0.8923 | 58 | 0.5430 | 0.4451 | 0.5430 |
| No log | 0.9231 | 60 | 0.5370 | 0.4575 | 0.5370 |
| No log | 0.9538 | 62 | 0.5412 | 0.4517 | 0.5412 |
| No log | 0.9846 | 64 | 0.5474 | 0.4497 | 0.5474 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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