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End of training
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metadata
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: []

microsoft-codebert-base-finetuned-defect-cwe-group-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: 0.6195
  • 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