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library_name: transformers
base_model: aubmindlab/bert-base-arabertv02-twitter
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Arabertv2-fold5
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. -->
# Arabertv2-fold5
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6600
- Accuracy: 0.8155
- Macro F1: 0.8151
- Weighted F1: 0.8151
- F1 Pro: 0.8037
- F1 Against: 0.8226
- F1 Neutral: 0.8190
## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:------:|:----------:|:----------:|
| 0.9553 | 1.1628 | 50 | 0.7744 | 0.6667 | 0.6675 | 0.6667 | 0.6829 | 0.6387 | 0.6809 |
| 0.6022 | 2.3256 | 100 | 0.5448 | 0.7619 | 0.7617 | 0.7620 | 0.7748 | 0.7603 | 0.75 |
| 0.3637 | 3.4884 | 150 | 0.5355 | 0.7976 | 0.7961 | 0.7969 | 0.7961 | 0.8154 | 0.7767 |
| 0.2713 | 4.6512 | 200 | 0.5420 | 0.8036 | 0.8031 | 0.8035 | 0.8037 | 0.8130 | 0.7925 |
| 0.1627 | 5.8140 | 250 | 0.5662 | 0.7917 | 0.7914 | 0.7914 | 0.7748 | 0.8033 | 0.7961 |
| 0.1126 | 6.9767 | 300 | 0.6165 | 0.8095 | 0.8095 | 0.8092 | 0.7963 | 0.8130 | 0.8190 |
| 0.0886 | 8.1395 | 350 | 0.6597 | 0.8155 | 0.8151 | 0.8151 | 0.8037 | 0.8226 | 0.8190 |
| 0.0803 | 9.3023 | 400 | 0.6821 | 0.8095 | 0.8089 | 0.8091 | 0.7963 | 0.8226 | 0.8077 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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