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--- |
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library_name: transformers |
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base_model: aubmindlab/bert-base-arabertv02-twitter |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: fold_4 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# fold_4 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4181 |
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- Accuracy: 0.8452 |
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- Macro F1: 0.8447 |
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- Weighted F1: 0.8452 |
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- F1 Pro: 0.8421 |
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- F1 Against: 0.8571 |
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- F1 Neutral: 0.8350 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:------:|:----------:|:----------:| |
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| 0.8777 | 1.1628 | 50 | 0.5462 | 0.8095 | 0.8101 | 0.8102 | 0.8468 | 0.7874 | 0.7959 | |
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| 0.4709 | 2.3256 | 100 | 0.4626 | 0.8274 | 0.8277 | 0.8275 | 0.8571 | 0.8034 | 0.8224 | |
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| 0.27 | 3.4884 | 150 | 0.4181 | 0.8452 | 0.8447 | 0.8452 | 0.8421 | 0.8571 | 0.8350 | |
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| 0.2101 | 4.6512 | 200 | 0.5017 | 0.8095 | 0.8088 | 0.8094 | 0.8421 | 0.8 | 0.7843 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.9.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.2 |
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