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--- |
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library_name: transformers |
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license: mit |
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base_model: intfloat/e5-base-v2 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: intfloat-e5-base-v2-arabic-fp16 |
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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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# intfloat-e5-base-v2-arabic-fp16 |
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This model is a fine-tuned version of [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6556 |
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- Accuracy: 0.7373 |
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- Precision: 0.7334 |
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- Recall: 0.7373 |
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- F1: 0.7326 |
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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: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH 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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- lr_scheduler_warmup_ratio: 0.3 |
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- num_epochs: 10 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0839 | 0.3636 | 50 | 0.9608 | 0.5914 | 0.6871 | 0.5914 | 0.5136 | |
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| 0.9231 | 0.7273 | 100 | 0.8409 | 0.6418 | 0.6989 | 0.6418 | 0.5666 | |
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| 0.842 | 1.0873 | 150 | 0.7770 | 0.6877 | 0.6719 | 0.6877 | 0.6606 | |
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| 0.7936 | 1.4509 | 200 | 0.7662 | 0.6836 | 0.6748 | 0.6836 | 0.6608 | |
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| 0.7691 | 1.8145 | 250 | 0.7656 | 0.6809 | 0.6841 | 0.6809 | 0.6780 | |
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| 0.7528 | 2.1745 | 300 | 0.7134 | 0.7091 | 0.7059 | 0.7091 | 0.7005 | |
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| 0.7215 | 2.5382 | 350 | 0.7003 | 0.7068 | 0.7161 | 0.7068 | 0.7093 | |
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| 0.7101 | 2.9018 | 400 | 0.6866 | 0.7227 | 0.7182 | 0.7227 | 0.7128 | |
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| 0.69 | 3.2618 | 450 | 0.6877 | 0.7164 | 0.7201 | 0.7164 | 0.7167 | |
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| 0.6578 | 3.6255 | 500 | 0.7134 | 0.6991 | 0.7178 | 0.6991 | 0.7041 | |
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| 0.6521 | 3.9891 | 550 | 0.6563 | 0.7377 | 0.7346 | 0.7377 | 0.7341 | |
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| 0.6031 | 4.3491 | 600 | 0.6556 | 0.7373 | 0.7334 | 0.7373 | 0.7326 | |
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| 0.6007 | 4.7127 | 650 | 0.6590 | 0.7341 | 0.7361 | 0.7341 | 0.7350 | |
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| 0.5876 | 5.0727 | 700 | 0.6783 | 0.7268 | 0.7324 | 0.7268 | 0.7285 | |
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| 0.5533 | 5.4364 | 750 | 0.6912 | 0.7205 | 0.7354 | 0.7205 | 0.7217 | |
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### Framework versions |
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- Transformers 4.51.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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