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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-large |
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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-large-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-large-arabic-fp16 |
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This model is a fine-tuned version of [intfloat/e5-large](https://huggingface.co/intfloat/e5-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6571 |
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- Accuracy: 0.7295 |
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- Precision: 0.7252 |
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- Recall: 0.7295 |
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- F1: 0.7229 |
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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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| 1.1453 | 0.3636 | 50 | 0.9382 | 0.5823 | 0.4522 | 0.5823 | 0.5088 | |
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| 0.9116 | 0.7273 | 100 | 0.8151 | 0.6568 | 0.6543 | 0.6568 | 0.6162 | |
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| 0.8321 | 1.0873 | 150 | 0.8027 | 0.6645 | 0.6610 | 0.6645 | 0.6379 | |
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| 0.8035 | 1.4509 | 200 | 0.7924 | 0.6777 | 0.6807 | 0.6777 | 0.6628 | |
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| 0.7746 | 1.8145 | 250 | 0.9196 | 0.6141 | 0.6605 | 0.6141 | 0.6040 | |
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| 0.7751 | 2.1745 | 300 | 0.7843 | 0.6677 | 0.6741 | 0.6677 | 0.6650 | |
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| 0.753 | 2.5382 | 350 | 0.7799 | 0.6568 | 0.6968 | 0.6568 | 0.6672 | |
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| 0.731 | 2.9018 | 400 | 0.7178 | 0.7123 | 0.7160 | 0.7123 | 0.6950 | |
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| 0.7133 | 3.2618 | 450 | 0.6932 | 0.71 | 0.7151 | 0.71 | 0.7117 | |
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| 0.7057 | 3.6255 | 500 | 0.7281 | 0.6986 | 0.7044 | 0.6986 | 0.6988 | |
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| 0.6831 | 3.9891 | 550 | 0.6745 | 0.7309 | 0.7296 | 0.7309 | 0.7195 | |
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| 0.6486 | 4.3491 | 600 | 0.6571 | 0.7295 | 0.7252 | 0.7295 | 0.7229 | |
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| 0.6378 | 4.7127 | 650 | 0.6701 | 0.7232 | 0.7217 | 0.7232 | 0.7223 | |
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| 0.6281 | 5.0727 | 700 | 0.6627 | 0.7386 | 0.7350 | 0.7386 | 0.7360 | |
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| 0.5938 | 5.4364 | 750 | 0.6814 | 0.7155 | 0.7229 | 0.7155 | 0.7181 | |
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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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