---
library_name: peft
base_model: microsoft/codebert-base
tags:
- base_model:adapter:microsoft/codebert-base
- lora
- transformers
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: CodeGenDetect-CodeBert_Lora
  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. -->

# CodeGenDetect-CodeBert_Lora

This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0384
- Accuracy: 0.9907
- F1: 0.9907
- Precision: 0.9907
- Recall: 0.9907

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Accuracy | F1     | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.1381        | 0.128  | 4000  | 0.9586   | 0.9586 | 0.1627          | 0.9599    | 0.9586 |
| 0.0821        | 0.256  | 8000  | 0.9761   | 0.9761 | 0.1081          | 0.9761    | 0.9761 |
| 0.0667        | 0.384  | 12000 | 0.9786   | 0.9786 | 0.1008          | 0.9787    | 0.9786 |
| 0.0754        | 0.512  | 16000 | 0.9820   | 0.9820 | 0.0779          | 0.9821    | 0.9820 |
| 0.0776        | 0.64   | 20000 | 0.9846   | 0.9846 | 0.0617          | 0.9847    | 0.9846 |
| 0.0643        | 0.768  | 24000 | 0.9831   | 0.9831 | 0.0761          | 0.9832    | 0.9831 |
| 0.064         | 0.896  | 28000 | 0.9878   | 0.9878 | 0.0495          | 0.9878    | 0.9878 |
| 0.0477        | 1.024  | 32000 | 0.9879   | 0.9879 | 0.0480          | 0.9880    | 0.9879 |
| 0.0427        | 1.152  | 36000 | 0.9894   | 0.9894 | 0.0424          | 0.9894    | 0.9894 |
| 0.0381        | 1.28   | 40000 | 0.9880   | 0.9880 | 0.0484          | 0.9880    | 0.9880 |
| 0.0423        | 1.408  | 44000 | 0.9901   | 0.9901 | 0.0399          | 0.9901    | 0.9901 |
| 0.0389        | 1.536  | 48000 | 0.9888   | 0.9888 | 0.0513          | 0.9889    | 0.9888 |
| 0.0416        | 1.6640 | 52000 | 0.9908   | 0.9908 | 0.0358          | 0.9908    | 0.9908 |
| 0.0374        | 1.792  | 56000 | 0.0370   | 0.9905 | 0.9905          | 0.9905    | 0.9905 |
| 0.0441        | 1.92   | 60000 | 0.0355   | 0.9905 | 0.9905          | 0.9905    | 0.9905 |
| 0.0358        | 2.048  | 64000 | 0.0384   | 0.9907 | 0.9907          | 0.9907    | 0.9907 |


### Framework versions

- PEFT 0.18.0
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1