metadata
library_name: transformers
base_model: huggingface/CodeBERTa-small-v1
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: CodeBERTa-small-v1-sourcecode-detection-clf
results: []
CodeBERTa-small-v1-sourcecode-detection-clf
This model is a fine-tuned version of huggingface/CodeBERTa-small-v1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0171
- F1: 0.9975
- Accuracy: 0.9975
- Precision: 0.9975
- Recall: 0.9975
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: 0.0003
- train_batch_size: 320
- eval_batch_size: 320
- seed: 2024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 0 | 0 | 0.6981 | 0.3337 | 0.5001 | 0.6162 | 0.5001 |
| 0.0294 | 0.1420 | 1000 | 0.0398 | 0.9947 | 0.9947 | 0.9947 | 0.9947 |
| 0.0076 | 0.2841 | 2000 | 0.0211 | 0.9968 | 0.9968 | 0.9968 | 0.9968 |
| 0.0053 | 0.4261 | 3000 | 0.0188 | 0.9973 | 0.9973 | 0.9973 | 0.9973 |
| 0.0056 | 0.5681 | 4000 | 0.0166 | 0.9976 | 0.9976 | 0.9976 | 0.9976 |
| 0.0044 | 0.7101 | 5000 | 0.0172 | 0.9975 | 0.9975 | 0.9975 | 0.9975 |
| 0.0009 | 0.8522 | 6000 | 0.0171 | 0.9975 | 0.9975 | 0.9975 | 0.9975 |
| 0.0052 | 0.9942 | 7000 | 0.0171 | 0.9975 | 0.9975 | 0.9975 | 0.9975 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3