| --- |
| base_model: microsoft/codebert-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: codebert-code-clone-detector |
| results: [] |
| license: mit |
| pipeline_tag: sentence-similarity |
| --- |
| |
|
|
|
|
| # codebert-code-clone-detector |
|
|
| This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on a Code Clone Benchmark dataset. |
| See this [github repository](https://github.com/LucK1Y/CodeCloneBERT) for more information. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3452 |
| - Accuracy: 0.9525 |
| - Precision: 0.9544 |
| - Recall: 0.9496 |
| - F1: 0.9520 |
|
|
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.3416 | 0.49 | 33 | 0.1724 | 0.9417 | 0.9828 | 0.9048 | 0.9421 | |
| | 0.221 | 0.97 | 66 | 0.2768 | 0.925 | 1.0 | 0.8571 | 0.9231 | |
| | 0.0929 | 1.46 | 99 | 0.2469 | 0.9583 | 1.0 | 0.9206 | 0.9587 | |
| | 0.1696 | 1.94 | 132 | 0.2142 | 0.95 | 0.9524 | 0.9524 | 0.9524 | |
| | 0.0818 | 2.43 | 165 | 0.4142 | 0.925 | 1.0 | 0.8571 | 0.9231 | |
| | 0.0676 | 2.91 | 198 | 0.3539 | 0.9333 | 0.9508 | 0.9206 | 0.9355 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.39.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
