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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-14 |
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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-14 |
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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: 0.1507 |
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- F1: 0.9470 |
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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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| 0.4531 | 0.2527 | 100 | 0.2315 | 0.9007 | |
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| 0.201 | 0.5054 | 200 | 0.1689 | 0.9390 | |
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| 0.1611 | 0.7581 | 300 | 0.1620 | 0.9456 | |
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| 0.152 | 1.0107 | 400 | 0.1230 | 0.9560 | |
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| 0.1046 | 1.2634 | 500 | 0.1297 | 0.9510 | |
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| 0.1138 | 1.5161 | 600 | 0.1385 | 0.9515 | |
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| 0.1212 | 1.7688 | 700 | 0.1365 | 0.9497 | |
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| 0.1203 | 2.0215 | 800 | 0.1339 | 0.9555 | |
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| 0.0946 | 2.2742 | 900 | 0.1253 | 0.9538 | |
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| 0.0974 | 2.5268 | 1000 | 0.1891 | 0.9287 | |
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| 0.1191 | 2.7795 | 1100 | 0.1375 | 0.9531 | |
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| 0.1078 | 3.0322 | 1200 | 0.1806 | 0.9504 | |
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| 0.0751 | 3.2849 | 1300 | 0.1466 | 0.9550 | |
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| 0.0799 | 3.5376 | 1400 | 0.1507 | 0.9470 | |
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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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