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--- |
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library_name: transformers |
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license: mit |
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base_model: intfloat/multilingual-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-multilingual-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-multilingual-e5-base-arabic-fp16 |
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This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4961 |
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- Accuracy: 0.7986 |
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- Precision: 0.7991 |
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- Recall: 0.7986 |
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- F1: 0.7988 |
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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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| 1.0686 | 0.3636 | 50 | 1.0146 | 0.5495 | 0.7252 | 0.5495 | 0.4582 | |
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| 0.9589 | 0.7273 | 100 | 0.8046 | 0.6777 | 0.7234 | 0.6777 | 0.6081 | |
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| 0.7431 | 1.0873 | 150 | 0.6238 | 0.7595 | 0.7565 | 0.7595 | 0.7530 | |
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| 0.6066 | 1.4509 | 200 | 0.5485 | 0.7945 | 0.7947 | 0.7945 | 0.7906 | |
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| 0.5558 | 1.8145 | 250 | 0.5530 | 0.7827 | 0.7860 | 0.7827 | 0.7837 | |
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| 0.5343 | 2.1745 | 300 | 0.5430 | 0.7973 | 0.8009 | 0.7973 | 0.7983 | |
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| 0.4965 | 2.5382 | 350 | 0.5178 | 0.7986 | 0.7993 | 0.7986 | 0.7988 | |
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| 0.5017 | 2.9018 | 400 | 0.4961 | 0.7986 | 0.7991 | 0.7986 | 0.7988 | |
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| 0.4525 | 3.2618 | 450 | 0.5441 | 0.7932 | 0.7991 | 0.7932 | 0.7950 | |
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| 0.4194 | 3.6255 | 500 | 0.5147 | 0.8027 | 0.8051 | 0.8027 | 0.8027 | |
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| 0.4353 | 3.9891 | 550 | 0.4918 | 0.8118 | 0.8109 | 0.8118 | 0.8110 | |
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| 0.3635 | 4.3491 | 600 | 0.5659 | 0.7977 | 0.8058 | 0.7977 | 0.7980 | |
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| 0.3529 | 4.7127 | 650 | 0.5493 | 0.8023 | 0.8066 | 0.8023 | 0.8029 | |
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| 0.3574 | 5.0727 | 700 | 0.5438 | 0.8023 | 0.8043 | 0.8023 | 0.8031 | |
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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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