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---
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 |