Instructions to use PoulaLabib/code_smell_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PoulaLabib/code_smell_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PoulaLabib/code_smell_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PoulaLabib/code_smell_model") model = AutoModelForSequenceClassification.from_pretrained("PoulaLabib/code_smell_model") - Notebooks
- Google Colab
- Kaggle
code_smell_model
This model is a fine-tuned version of huggingface/CodeBERTa-small-v1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1805
- Accuracy: 0.9542
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1297 | 1.0 | 552 | 0.1765 | 0.9533 |
| 0.1006 | 2.0 | 1104 | 0.1805 | 0.9542 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for PoulaLabib/code_smell_model
Base model
huggingface/CodeBERTa-small-v1