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---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv2
tags:
- generated_from_trainer
model-index:
- name: arabert_armis_multitask_hard
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_armis_multitask_hard
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7896
- Disagreement Accuracy: 0.5035
- Disagreement F1: 0.4167
- Target Accuracy: 0.6525
- Target F1: 0.6202
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Disagreement Accuracy | Disagreement F1 | Target Accuracy | Target F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:---------------:|:---------------:|:---------:|
| 0.8457 | 1.0 | 21 | 0.8400 | 0.5390 | 0.4961 | 0.4610 | 0.5778 |
| 0.8372 | 2.0 | 42 | 0.8325 | 0.5461 | 0.4386 | 0.6241 | 0.5225 |
| 0.8341 | 3.0 | 63 | 0.8290 | 0.5532 | 0.4324 | 0.5390 | 0.5517 |
| 0.8153 | 4.0 | 84 | 0.8240 | 0.5532 | 0.496 | 0.5603 | 0.5634 |
| 0.809 | 5.0 | 105 | 0.8209 | 0.5319 | 0.3125 | 0.5816 | 0.5874 |
| 0.79 | 6.0 | 126 | 0.8150 | 0.5390 | 0.4882 | 0.6099 | 0.5669 |
| 0.7857 | 7.0 | 147 | 0.8119 | 0.5177 | 0.3462 | 0.6312 | 0.5806 |
| 0.7775 | 8.0 | 168 | 0.8090 | 0.5035 | 0.3137 | 0.6099 | 0.5926 |
| 0.7763 | 9.0 | 189 | 0.8041 | 0.5106 | 0.448 | 0.6383 | 0.5984 |
| 0.7685 | 10.0 | 210 | 0.8015 | 0.5106 | 0.3670 | 0.6454 | 0.6094 |
| 0.7652 | 11.0 | 231 | 0.7990 | 0.5035 | 0.4262 | 0.6383 | 0.6165 |
| 0.7597 | 12.0 | 252 | 0.7979 | 0.5106 | 0.4651 | 0.6241 | 0.6074 |
| 0.753 | 13.0 | 273 | 0.7955 | 0.4894 | 0.4286 | 0.6454 | 0.6212 |
| 0.7475 | 14.0 | 294 | 0.7930 | 0.5035 | 0.4167 | 0.6596 | 0.6190 |
| 0.7465 | 15.0 | 315 | 0.7923 | 0.4965 | 0.4132 | 0.6525 | 0.6202 |
| 0.7425 | 16.0 | 336 | 0.7914 | 0.5035 | 0.4167 | 0.6454 | 0.6032 |
| 0.7407 | 17.0 | 357 | 0.7905 | 0.4965 | 0.4132 | 0.6525 | 0.6202 |
| 0.7397 | 18.0 | 378 | 0.7901 | 0.5248 | 0.4174 | 0.6525 | 0.6202 |
| 0.7362 | 19.0 | 399 | 0.7897 | 0.5035 | 0.4167 | 0.6525 | 0.6202 |
| 0.7358 | 20.0 | 420 | 0.7896 | 0.5035 | 0.4167 | 0.6525 | 0.6202 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.5.1+cu121
- Datasets 4.3.0
- Tokenizers 0.22.1
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