|
--- |
|
base_model: aubmindlab/bert-base-arabertv02 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: arabert_cross_organization_task1_fold3 |
|
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_organization_task1_fold3 |
|
|
|
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: 0.5415 |
|
- Qwk: 0.7472 |
|
- Mse: 0.5415 |
|
|
|
## 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: 64 |
|
- eval_batch_size: 64 |
|
- 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 | 0.1333 | 2 | 1.8315 | 0.1530 | 1.8315 | |
|
| No log | 0.2667 | 4 | 1.4232 | 0.1303 | 1.4232 | |
|
| No log | 0.4 | 6 | 1.3057 | 0.4221 | 1.3057 | |
|
| No log | 0.5333 | 8 | 0.9837 | 0.5056 | 0.9837 | |
|
| No log | 0.6667 | 10 | 0.9513 | 0.7091 | 0.9513 | |
|
| No log | 0.8 | 12 | 0.8371 | 0.7415 | 0.8371 | |
|
| No log | 0.9333 | 14 | 0.7233 | 0.6918 | 0.7233 | |
|
| No log | 1.0667 | 16 | 0.6922 | 0.7196 | 0.6922 | |
|
| No log | 1.2 | 18 | 0.6535 | 0.7225 | 0.6535 | |
|
| No log | 1.3333 | 20 | 0.6214 | 0.6382 | 0.6214 | |
|
| No log | 1.4667 | 22 | 0.6214 | 0.6481 | 0.6214 | |
|
| No log | 1.6 | 24 | 0.6577 | 0.6929 | 0.6577 | |
|
| No log | 1.7333 | 26 | 0.6144 | 0.6652 | 0.6144 | |
|
| No log | 1.8667 | 28 | 0.7474 | 0.5722 | 0.7474 | |
|
| No log | 2.0 | 30 | 0.6810 | 0.6294 | 0.6810 | |
|
| No log | 2.1333 | 32 | 0.6202 | 0.7358 | 0.6202 | |
|
| No log | 2.2667 | 34 | 0.6151 | 0.6751 | 0.6151 | |
|
| No log | 2.4 | 36 | 0.6002 | 0.6738 | 0.6002 | |
|
| No log | 2.5333 | 38 | 0.5749 | 0.7000 | 0.5749 | |
|
| No log | 2.6667 | 40 | 0.5666 | 0.7675 | 0.5666 | |
|
| No log | 2.8 | 42 | 0.6051 | 0.7937 | 0.6051 | |
|
| No log | 2.9333 | 44 | 0.5435 | 0.7839 | 0.5435 | |
|
| No log | 3.0667 | 46 | 0.5462 | 0.6799 | 0.5462 | |
|
| No log | 3.2 | 48 | 0.5199 | 0.7618 | 0.5199 | |
|
| No log | 3.3333 | 50 | 0.5686 | 0.7827 | 0.5686 | |
|
| No log | 3.4667 | 52 | 0.5681 | 0.7750 | 0.5681 | |
|
| No log | 3.6 | 54 | 0.5202 | 0.7756 | 0.5202 | |
|
| No log | 3.7333 | 56 | 0.5295 | 0.7412 | 0.5295 | |
|
| No log | 3.8667 | 58 | 0.5478 | 0.7632 | 0.5478 | |
|
| No log | 4.0 | 60 | 0.5733 | 0.7573 | 0.5733 | |
|
| No log | 4.1333 | 62 | 0.6257 | 0.7566 | 0.6257 | |
|
| No log | 4.2667 | 64 | 0.5977 | 0.7451 | 0.5977 | |
|
| No log | 4.4 | 66 | 0.6316 | 0.6778 | 0.6316 | |
|
| No log | 4.5333 | 68 | 0.5693 | 0.7216 | 0.5693 | |
|
| No log | 4.6667 | 70 | 0.5614 | 0.7730 | 0.5614 | |
|
| No log | 4.8 | 72 | 0.5561 | 0.7771 | 0.5561 | |
|
| No log | 4.9333 | 74 | 0.5439 | 0.7692 | 0.5439 | |
|
| No log | 5.0667 | 76 | 0.5722 | 0.7788 | 0.5722 | |
|
| No log | 5.2 | 78 | 0.5816 | 0.7777 | 0.5816 | |
|
| No log | 5.3333 | 80 | 0.5828 | 0.7436 | 0.5828 | |
|
| No log | 5.4667 | 82 | 0.5916 | 0.6944 | 0.5916 | |
|
| No log | 5.6 | 84 | 0.5655 | 0.7296 | 0.5655 | |
|
| No log | 5.7333 | 86 | 0.5767 | 0.7728 | 0.5767 | |
|
| No log | 5.8667 | 88 | 0.5590 | 0.7795 | 0.5590 | |
|
| No log | 6.0 | 90 | 0.5207 | 0.7385 | 0.5207 | |
|
| No log | 6.1333 | 92 | 0.5177 | 0.7374 | 0.5177 | |
|
| No log | 6.2667 | 94 | 0.5230 | 0.7756 | 0.5230 | |
|
| No log | 6.4 | 96 | 0.5599 | 0.7771 | 0.5599 | |
|
| No log | 6.5333 | 98 | 0.5687 | 0.7802 | 0.5687 | |
|
| No log | 6.6667 | 100 | 0.5450 | 0.7589 | 0.5450 | |
|
| No log | 6.8 | 102 | 0.5470 | 0.7329 | 0.5470 | |
|
| No log | 6.9333 | 104 | 0.5539 | 0.7073 | 0.5539 | |
|
| No log | 7.0667 | 106 | 0.5586 | 0.7035 | 0.5586 | |
|
| No log | 7.2 | 108 | 0.5647 | 0.7449 | 0.5647 | |
|
| No log | 7.3333 | 110 | 0.5807 | 0.7533 | 0.5807 | |
|
| No log | 7.4667 | 112 | 0.5667 | 0.7369 | 0.5667 | |
|
| No log | 7.6 | 114 | 0.5602 | 0.7153 | 0.5602 | |
|
| No log | 7.7333 | 116 | 0.5570 | 0.7251 | 0.5570 | |
|
| No log | 7.8667 | 118 | 0.5524 | 0.7335 | 0.5524 | |
|
| No log | 8.0 | 120 | 0.5563 | 0.7569 | 0.5563 | |
|
| No log | 8.1333 | 122 | 0.5503 | 0.7474 | 0.5503 | |
|
| No log | 8.2667 | 124 | 0.5434 | 0.7544 | 0.5434 | |
|
| No log | 8.4 | 126 | 0.5579 | 0.7695 | 0.5579 | |
|
| No log | 8.5333 | 128 | 0.5668 | 0.7794 | 0.5668 | |
|
| No log | 8.6667 | 130 | 0.5537 | 0.7567 | 0.5537 | |
|
| No log | 8.8 | 132 | 0.5391 | 0.7511 | 0.5391 | |
|
| No log | 8.9333 | 134 | 0.5365 | 0.7457 | 0.5365 | |
|
| No log | 9.0667 | 136 | 0.5367 | 0.7452 | 0.5367 | |
|
| No log | 9.2 | 138 | 0.5391 | 0.7439 | 0.5391 | |
|
| No log | 9.3333 | 140 | 0.5431 | 0.7438 | 0.5431 | |
|
| No log | 9.4667 | 142 | 0.5435 | 0.7438 | 0.5435 | |
|
| No log | 9.6 | 144 | 0.5455 | 0.7477 | 0.5455 | |
|
| No log | 9.7333 | 146 | 0.5440 | 0.7438 | 0.5440 | |
|
| No log | 9.8667 | 148 | 0.5422 | 0.7427 | 0.5422 | |
|
| No log | 10.0 | 150 | 0.5415 | 0.7472 | 0.5415 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.0 |
|
- Pytorch 2.4.0 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
