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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-77
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-77
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: 0.1865
- F1: 0.9553
## 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 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4603 | 0.2527 | 100 | 0.2376 | 0.9013 |
| 0.2019 | 0.5054 | 200 | 0.1611 | 0.9381 |
| 0.1572 | 0.7581 | 300 | 0.1362 | 0.9484 |
| 0.1526 | 1.0107 | 400 | 0.1196 | 0.9547 |
| 0.1094 | 1.2634 | 500 | 0.1384 | 0.9535 |
| 0.1133 | 1.5161 | 600 | 0.1448 | 0.9506 |
| 0.1144 | 1.7688 | 700 | 0.1269 | 0.9562 |
| 0.1151 | 2.0215 | 800 | 0.1227 | 0.9566 |
| 0.0791 | 2.2742 | 900 | 0.1458 | 0.9529 |
| 0.0973 | 2.5268 | 1000 | 0.1396 | 0.9553 |
| 0.0931 | 2.7795 | 1100 | 0.1455 | 0.9567 |
| 0.0928 | 3.0322 | 1200 | 0.1501 | 0.9534 |
| 0.063 | 3.2849 | 1300 | 0.1676 | 0.9445 |
| 0.0665 | 3.5376 | 1400 | 0.1598 | 0.9465 |
| 0.0683 | 3.7903 | 1500 | 0.1505 | 0.9557 |
| 0.0701 | 4.0430 | 1600 | 0.1657 | 0.9566 |
| 0.0569 | 4.2956 | 1700 | 0.2030 | 0.9433 |
| 0.052 | 4.5483 | 1800 | 0.1747 | 0.9503 |
| 0.0558 | 4.8010 | 1900 | 0.1994 | 0.9491 |
| 0.0555 | 5.0537 | 2000 | 0.1951 | 0.9303 |
| 0.0682 | 5.3064 | 2100 | 0.1865 | 0.9553 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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