|
--- |
|
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 |
