---
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