---
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-14
  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-14

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: 0.1507
- F1: 0.9470

## 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     |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4531        | 0.2527 | 100  | 0.2315          | 0.9007 |
| 0.201         | 0.5054 | 200  | 0.1689          | 0.9390 |
| 0.1611        | 0.7581 | 300  | 0.1620          | 0.9456 |
| 0.152         | 1.0107 | 400  | 0.1230          | 0.9560 |
| 0.1046        | 1.2634 | 500  | 0.1297          | 0.9510 |
| 0.1138        | 1.5161 | 600  | 0.1385          | 0.9515 |
| 0.1212        | 1.7688 | 700  | 0.1365          | 0.9497 |
| 0.1203        | 2.0215 | 800  | 0.1339          | 0.9555 |
| 0.0946        | 2.2742 | 900  | 0.1253          | 0.9538 |
| 0.0974        | 2.5268 | 1000 | 0.1891          | 0.9287 |
| 0.1191        | 2.7795 | 1100 | 0.1375          | 0.9531 |
| 0.1078        | 3.0322 | 1200 | 0.1806          | 0.9504 |
| 0.0751        | 3.2849 | 1300 | 0.1466          | 0.9550 |
| 0.0799        | 3.5376 | 1400 | 0.1507          | 0.9470 |


### Framework versions

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