---
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-71
  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-71

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: 0.1854
- F1: 0.9373

## 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     |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4424        | 0.2527 | 100  | 0.2132          | 0.9171 |
| 0.196         | 0.5054 | 200  | 0.1630          | 0.9390 |
| 0.157         | 0.7581 | 300  | 0.1354          | 0.9455 |
| 0.1504        | 1.0107 | 400  | 0.1332          | 0.9526 |
| 0.1062        | 1.2634 | 500  | 0.1283          | 0.9530 |
| 0.1089        | 1.5161 | 600  | 0.1226          | 0.9571 |
| 0.1171        | 1.7688 | 700  | 0.1329          | 0.9537 |
| 0.1136        | 2.0215 | 800  | 0.1429          | 0.9550 |
| 0.0799        | 2.2742 | 900  | 0.1543          | 0.9501 |
| 0.0929        | 2.5268 | 1000 | 0.1456          | 0.9488 |
| 0.0915        | 2.7795 | 1100 | 0.1518          | 0.9499 |
| 0.1065        | 3.0322 | 1200 | 0.1714          | 0.9471 |
| 0.067         | 3.2849 | 1300 | 0.1334          | 0.9582 |
| 0.0702        | 3.5376 | 1400 | 0.1472          | 0.9508 |
| 0.0714        | 3.7903 | 1500 | 0.1852          | 0.9495 |
| 0.0698        | 4.0430 | 1600 | 0.2459          | 0.9453 |
| 0.0518        | 4.2956 | 1700 | 0.2273          | 0.9477 |
| 0.0565        | 4.5483 | 1800 | 0.1717          | 0.9527 |
| 0.0543        | 4.8010 | 1900 | 0.1749          | 0.9538 |
| 0.0516        | 5.0537 | 2000 | 0.1736          | 0.9545 |
| 0.0395        | 5.3064 | 2100 | 0.2381          | 0.9469 |
| 0.0447        | 5.5591 | 2200 | 0.2138          | 0.9444 |
| 0.0515        | 5.8117 | 2300 | 0.1854          | 0.9373 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3