README.md

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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# microsoft-codebert-base-finetuned-defect-cwe-group-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: 0.6195
- Accuracy: 0.7490
- Precision: 0.5725
- Recall: 0.5159

## 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: 8
- eval_batch_size: 8
- seed: 4711
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| No log        | 1.0   | 462  | 0.6077          | 0.7288   | 0.6350    | 0.4460 |
| 0.7284        | 2.0   | 925  | 0.5435          | 0.7485   | 0.6418    | 0.4633 |
| 0.5295        | 3.0   | 1387 | 0.5937          | 0.7209   | 0.5285    | 0.5098 |
| 0.4242        | 4.0   | 1850 | 0.6071          | 0.7400   | 0.5543    | 0.5354 |
| 0.3509        | 4.99  | 2310 | 0.6195          | 0.7490   | 0.5725    | 0.5159 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2