---
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-41
  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-41

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: 0.1900
- F1: 0.9527

## 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     |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4552        | 0.2527 | 100  | 0.2210          | 0.9100 |
| 0.1965        | 0.5054 | 200  | 0.1912          | 0.9298 |
| 0.1597        | 0.7581 | 300  | 0.1320          | 0.9520 |
| 0.1509        | 1.0107 | 400  | 0.1212          | 0.9574 |
| 0.1062        | 1.2634 | 500  | 0.1348          | 0.9500 |
| 0.1109        | 1.5161 | 600  | 0.1812          | 0.9462 |
| 0.1172        | 1.7688 | 700  | 0.1335          | 0.9530 |
| 0.1127        | 2.0215 | 800  | 0.1414          | 0.9564 |
| 0.0754        | 2.2742 | 900  | 0.1359          | 0.9553 |
| 0.0837        | 2.5268 | 1000 | 0.1182          | 0.9603 |
| 0.093         | 2.7795 | 1100 | 0.1459          | 0.9511 |
| 0.0926        | 3.0322 | 1200 | 0.1386          | 0.9564 |
| 0.0669        | 3.2849 | 1300 | 0.1504          | 0.9584 |
| 0.0683        | 3.5376 | 1400 | 0.1682          | 0.9487 |
| 0.07          | 3.7903 | 1500 | 0.1494          | 0.9498 |
| 0.0663        | 4.0430 | 1600 | 0.1934          | 0.9497 |
| 0.0471        | 4.2956 | 1700 | 0.2087          | 0.9500 |
| 0.0546        | 4.5483 | 1800 | 0.1867          | 0.9477 |
| 0.1081        | 4.8010 | 1900 | 0.1720          | 0.9579 |
| 0.0505        | 5.0537 | 2000 | 0.1900          | 0.9527 |


### Framework versions

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