---
base_model: microsoft/unixcoder-base
library_name: peft
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: CodeGenDetect-Unixcoder_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-Unixcoder_Lora

This model is a fine-tuned version of [microsoft/unixcoder-base](https://huggingface.co/microsoft/unixcoder-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0266
- Accuracy: 0.9927
- F1: 0.9927
- Precision: 0.9927
- Recall: 0.9927

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0349        | 1.02  | 4000  | 0.0342          | 0.9887   | 0.9887 | 0.9887    | 0.9887 |
| 0.0244        | 2.05  | 8000  | 0.0279          | 0.9916   | 0.9916 | 0.9916    | 0.9916 |
| 0.0234        | 3.07  | 12000 | 0.0260          | 0.9923   | 0.9923 | 0.9923    | 0.9923 |
| 0.0249        | 4.1   | 16000 | 0.0266          | 0.9927   | 0.9927 | 0.9927    | 0.9927 |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.5.1+rocm6.2
- Datasets 2.21.0
- Tokenizers 0.15.2