---
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-47
  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-47

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: 0.1953
- F1: 0.9364

## 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     |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4457        | 0.2527 | 100  | 0.2245          | 0.9043 |
| 0.2007        | 0.5054 | 200  | 0.1694          | 0.9379 |
| 0.1703        | 0.7581 | 300  | 0.1438          | 0.9499 |
| 0.1545        | 1.0107 | 400  | 0.1219          | 0.9546 |
| 0.1136        | 1.2634 | 500  | 0.1342          | 0.9517 |
| 0.1126        | 1.5161 | 600  | 0.1351          | 0.9532 |
| 0.1213        | 1.7688 | 700  | 0.1521          | 0.9498 |
| 0.1163        | 2.0215 | 800  | 0.1389          | 0.9560 |
| 0.0783        | 2.2742 | 900  | 0.1614          | 0.9502 |
| 0.095         | 2.5268 | 1000 | 0.1486          | 0.9452 |
| 0.1057        | 2.7795 | 1100 | 0.1298          | 0.9598 |
| 0.1091        | 3.0322 | 1200 | 0.2374          | 0.9442 |
| 0.0796        | 3.2849 | 1300 | 0.1545          | 0.9469 |
| 0.0921        | 3.5376 | 1400 | 0.1869          | 0.9274 |
| 0.1033        | 3.7903 | 1500 | 0.1674          | 0.9425 |
| 0.1051        | 4.0430 | 1600 | 0.1710          | 0.9406 |
| 0.0772        | 4.2956 | 1700 | 0.1968          | 0.9370 |
| 0.0889        | 4.5483 | 1800 | 0.1904          | 0.9387 |
| 0.1065        | 4.8010 | 1900 | 0.2306          | 0.9281 |
| 0.0915        | 5.0537 | 2000 | 0.1637          | 0.9409 |
| 0.0701        | 5.3064 | 2100 | 0.1953          | 0.9364 |


### Framework versions

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