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README.md
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---
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base_model: FacebookAI/roberta-base
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library_name: peft
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license: mit
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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tags:
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- generated_from_trainer
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model-index:
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- name: CodeGenDetect-Roberta_Lora
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# CodeGenDetect-Roberta_Lora
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0373
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- Accuracy: 0.9884
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- F1: 0.9884
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- Precision: 0.9884
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- Recall: 0.9884
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 128
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- eval_batch_size: 128
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 5
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.0526 | 1.02 | 4000 | 0.0545 | 0.9825 | 0.9825 | 0.9825 | 0.9825 |
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| 0.0402 | 2.05 | 8000 | 0.0401 | 0.9872 | 0.9872 | 0.9872 | 0.9872 |
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| 0.0349 | 3.07 | 12000 | 0.0416 | 0.9870 | 0.9871 | 0.9871 | 0.9870 |
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| 0.0404 | 4.1 | 16000 | 0.0373 | 0.9884 | 0.9884 | 0.9884 | 0.9884 |
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### Framework versions
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- PEFT 0.9.0
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- Transformers 4.38.2
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- Pytorch 2.5.1+rocm6.2
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- Datasets 2.21.0
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- Tokenizers 0.15.2
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