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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 |
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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-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-arabic-fp16 |
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This model is a fine-tuned version of [intfloat/e5-base](https://huggingface.co/intfloat/e5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7482 |
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- Accuracy: 0.6909 |
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- Precision: 0.6879 |
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- Recall: 0.6909 |
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- F1: 0.6881 |
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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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| 1.0832 | 0.3636 | 50 | 1.0122 | 0.49 | 0.6672 | 0.49 | 0.3741 | |
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| 0.9697 | 0.7273 | 100 | 0.8935 | 0.6073 | 0.5817 | 0.6073 | 0.5493 | |
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| 0.8744 | 1.0873 | 150 | 0.8016 | 0.6636 | 0.6552 | 0.6636 | 0.6272 | |
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| 0.8115 | 1.4509 | 200 | 0.7482 | 0.6909 | 0.6879 | 0.6909 | 0.6881 | |
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| 0.7757 | 1.8145 | 250 | 0.8217 | 0.6482 | 0.6747 | 0.6482 | 0.6500 | |
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| 0.7566 | 2.1745 | 300 | 0.7877 | 0.6518 | 0.6874 | 0.6518 | 0.6610 | |
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| 0.7325 | 2.5382 | 350 | 0.8127 | 0.6436 | 0.6968 | 0.6436 | 0.6553 | |
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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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