---
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