---
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: microsoft-codebert-base-finetuned-defect-detection
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# microsoft-codebert-base-finetuned-defect-detection

This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5498
- Accuracy: 0.7026
- F1: 0.7299
- Precision: 0.6559
- Recall: 0.8227

## 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: 32
- eval_batch_size: 8
- seed: 4711
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6584        | 1.0   | 997  | 0.5554          | 0.6827   | 0.6347 | 0.7252    | 0.5642 |
| 0.5304        | 2.0   | 1994 | 0.5229          | 0.6975   | 0.7269 | 0.6502    | 0.8243 |
| 0.4572        | 3.0   | 2991 | 0.5498          | 0.7026   | 0.7299 | 0.6559    | 0.8227 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0