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---
library_name: transformers
license: apache-2.0
base_model: Alibaba-NLP/gte-large-en-v1.5
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: gte-large-en-v1.5-based-ft-prompt-injection-detection-241205Weighted
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gte-large-en-v1.5-based-ft-prompt-injection-detection-241205Weighted
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.
It achieves the following results on the evaluation set:
- Loss: 0.3220
- F1: 0.9471
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4432 | 0.2527 | 100 | 0.2279 | 0.9104 |
| 0.1996 | 0.5054 | 200 | 0.1793 | 0.9343 |
| 0.165 | 0.7581 | 300 | 0.1437 | 0.9450 |
| 0.1528 | 1.0107 | 400 | 0.1273 | 0.9531 |
| 0.1062 | 1.2634 | 500 | 0.1355 | 0.9490 |
| 0.1127 | 1.5161 | 600 | 0.1349 | 0.9544 |
| 0.1186 | 1.7688 | 700 | 0.1523 | 0.9496 |
| 0.1173 | 2.0215 | 800 | 0.1516 | 0.9483 |
| 0.0785 | 2.2742 | 900 | 0.1503 | 0.9528 |
| 0.0849 | 2.5268 | 1000 | 0.1623 | 0.9514 |
| 0.0898 | 2.7795 | 1100 | 0.1539 | 0.9460 |
| 0.0891 | 3.0322 | 1200 | 0.2415 | 0.9515 |
| 0.065 | 3.2849 | 1300 | 0.1589 | 0.9541 |
| 0.062 | 3.5376 | 1400 | 0.1499 | 0.9470 |
| 0.0677 | 3.7903 | 1500 | 0.1788 | 0.9445 |
| 0.0638 | 4.0430 | 1600 | 0.3220 | 0.9471 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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