metadata
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: microsoft-codebert-base-finetuned-defect-detection
results: []
microsoft-codebert-base-finetuned-defect-detection
This model is a fine-tuned version of 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