metadata
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: logs
results: []
logs
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: 0.0405
- Accuracy: 0.9950
- Precision: 0.9950
- Recall: 0.9950
- F1 Score: 0.9950
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|---|---|
| 0.1436 | 1.0 | 907 | 0.0851 | 0.9829 | 0.9829 | 0.9829 | 0.9829 |
| 0.0737 | 2.0 | 1814 | 0.0548 | 0.9915 | 0.9915 | 0.9915 | 0.9915 |
| 0.0216 | 3.0 | 2721 | 0.0469 | 0.9917 | 0.9918 | 0.9917 | 0.9917 |
| 0.0143 | 4.0 | 3628 | 0.0405 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1