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base_model: FacebookAI/roberta-base
library_name: peft
license: mit
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: CodeGenDetect-Roberta_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-Roberta_Lora
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0373
- Accuracy: 0.9884
- F1: 0.9884
- Precision: 0.9884
- Recall: 0.9884
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0526 | 1.02 | 4000 | 0.0545 | 0.9825 | 0.9825 | 0.9825 | 0.9825 |
| 0.0402 | 2.05 | 8000 | 0.0401 | 0.9872 | 0.9872 | 0.9872 | 0.9872 |
| 0.0349 | 3.07 | 12000 | 0.0416 | 0.9870 | 0.9871 | 0.9871 | 0.9870 |
| 0.0404 | 4.1 | 16000 | 0.0373 | 0.9884 | 0.9884 | 0.9884 | 0.9884 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.5.1+rocm6.2
- Datasets 2.21.0
- Tokenizers 0.15.2 |