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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-74
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-74
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: 0.7279
- F1: 0.0
## 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 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4479 | 0.2527 | 100 | 0.2253 | 0.9079 |
| 0.2005 | 0.5054 | 200 | 0.1644 | 0.9391 |
| 0.158 | 0.7581 | 300 | 0.1301 | 0.9485 |
| 0.1496 | 1.0107 | 400 | 0.1212 | 0.9536 |
| 0.1074 | 1.2634 | 500 | 0.1263 | 0.9536 |
| 0.1133 | 1.5161 | 600 | 0.1246 | 0.9589 |
| 0.1262 | 1.7688 | 700 | 0.1395 | 0.9534 |
| 0.1205 | 2.0215 | 800 | 0.1602 | 0.9488 |
| 0.0872 | 2.2742 | 900 | 0.1634 | 0.9453 |
| 0.6378 | 2.5268 | 1000 | 0.6986 | 0.7087 |
| 0.6962 | 2.7795 | 1100 | 0.6891 | 0.7087 |
| 0.6933 | 3.0322 | 1200 | 0.7122 | 0.0 |
| 0.6803 | 3.2849 | 1300 | 0.6952 | 0.0 |
| 0.6742 | 3.5376 | 1400 | 0.6953 | 0.0 |
| 0.6711 | 3.7903 | 1500 | 0.6956 | 0.0 |
| 0.6658 | 4.0430 | 1600 | 0.7279 | 0.0 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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