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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Alibaba-NLP/gte-large-en-v1.5 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: gte-large-en-v1.5-based-ft-prompt-injection-detection-241205Weighted |
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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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# gte-large-en-v1.5-based-ft-prompt-injection-detection-241205Weighted |
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This model is a fine-tuned version of [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3220 |
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- F1: 0.9471 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 50 |
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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 | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.4432 | 0.2527 | 100 | 0.2279 | 0.9104 | |
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| 0.1996 | 0.5054 | 200 | 0.1793 | 0.9343 | |
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| 0.165 | 0.7581 | 300 | 0.1437 | 0.9450 | |
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| 0.1528 | 1.0107 | 400 | 0.1273 | 0.9531 | |
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| 0.1062 | 1.2634 | 500 | 0.1355 | 0.9490 | |
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| 0.1127 | 1.5161 | 600 | 0.1349 | 0.9544 | |
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| 0.1186 | 1.7688 | 700 | 0.1523 | 0.9496 | |
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| 0.1173 | 2.0215 | 800 | 0.1516 | 0.9483 | |
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| 0.0785 | 2.2742 | 900 | 0.1503 | 0.9528 | |
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| 0.0849 | 2.5268 | 1000 | 0.1623 | 0.9514 | |
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| 0.0898 | 2.7795 | 1100 | 0.1539 | 0.9460 | |
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| 0.0891 | 3.0322 | 1200 | 0.2415 | 0.9515 | |
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| 0.065 | 3.2849 | 1300 | 0.1589 | 0.9541 | |
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| 0.062 | 3.5376 | 1400 | 0.1499 | 0.9470 | |
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| 0.0677 | 3.7903 | 1500 | 0.1788 | 0.9445 | |
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| 0.0638 | 4.0430 | 1600 | 0.3220 | 0.9471 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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