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---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_baseline_mechanics_task2_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_baseline_mechanics_task2_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: 0.8879
- Qwk: 0.5758
- Mse: 0.8784
## 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 | 3.5975 | -0.0382 | 3.6807 |
| No log | 0.6667 | 4 | 1.4679 | 0.1796 | 1.5092 |
| No log | 1.0 | 6 | 1.0780 | 0.0 | 1.0882 |
| No log | 1.3333 | 8 | 1.2308 | 0.1695 | 1.2193 |
| No log | 1.6667 | 10 | 1.2253 | 0.1695 | 1.2102 |
| No log | 2.0 | 12 | 0.8911 | 0.2451 | 0.8886 |
| No log | 2.3333 | 14 | 0.8137 | 0.3099 | 0.8139 |
| No log | 2.6667 | 16 | 0.8083 | 0.3099 | 0.8044 |
| No log | 3.0 | 18 | 0.9159 | 0.5185 | 0.9080 |
| No log | 3.3333 | 20 | 1.0465 | 0.4556 | 1.0353 |
| No log | 3.6667 | 22 | 0.9742 | 0.4731 | 0.9635 |
| No log | 4.0 | 24 | 0.7724 | 0.3897 | 0.7631 |
| No log | 4.3333 | 26 | 0.7534 | 0.3636 | 0.7425 |
| No log | 4.6667 | 28 | 0.7201 | 0.3636 | 0.7079 |
| No log | 5.0 | 30 | 0.8574 | 0.5586 | 0.8468 |
| No log | 5.3333 | 32 | 0.9644 | 0.5130 | 0.9546 |
| No log | 5.6667 | 34 | 0.8585 | 0.608 | 0.8487 |
| No log | 6.0 | 36 | 0.7248 | 0.5405 | 0.7112 |
| No log | 6.3333 | 38 | 0.7385 | 0.5405 | 0.7257 |
| No log | 6.6667 | 40 | 0.8072 | 0.5758 | 0.7967 |
| No log | 7.0 | 42 | 0.9702 | 0.6513 | 0.9631 |
| No log | 7.3333 | 44 | 0.9882 | 0.6513 | 0.9815 |
| No log | 7.6667 | 46 | 0.9719 | 0.6513 | 0.9651 |
| No log | 8.0 | 48 | 0.9498 | 0.6513 | 0.9425 |
| No log | 8.3333 | 50 | 0.9073 | 0.6513 | 0.8988 |
| No log | 8.6667 | 52 | 0.8907 | 0.5758 | 0.8816 |
| No log | 9.0 | 54 | 0.8763 | 0.5758 | 0.8665 |
| No log | 9.3333 | 56 | 0.8701 | 0.5758 | 0.8602 |
| No log | 9.6667 | 58 | 0.8850 | 0.5758 | 0.8754 |
| No log | 10.0 | 60 | 0.8879 | 0.5758 | 0.8784 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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