FineTuneOptimizer is a Python library designed to streamline and optimize the fine-tuning process for pre-trained machine learning models.
- Fine-Tuning: Customize pre-trained models for task-specific datasets.
- Hyperparameter Optimization: Automate hyperparameter tuning.
- Evaluation: Measure model performance with ease.
- Hugging Face Integration: Deploy models effortlessly.
- Flexibility: Support for various models and optimization methods.
Install via pip:
pip install fine-tune-optimizer
Or install from source:
git clone https://github.com/yourusername/FineTuneOptimizer.git
cd FineTuneOptimizer
pip install -r requirements.txt
Usage
Fine-Tuning a Model
from fine_tune_optimizer import FineTuner
# Initialize FineTuner
fine_tuner = FineTuner(model_name='bert-base-uncased')
# Load dataset
train_dataset, test_dataset = load_data('your_dataset_path')
# Fine-tune the model
fine_tuner.train(train_dataset, epochs=3, batch_size=16)
# Evaluate the model
metrics = fine_tuner.evaluate(test_dataset)
print(metrics)
Hyperparameter Optimization
from fine_tune_optimizer import HyperOptimizer
# Initialize HyperOptimizer
hyper_optimizer = HyperOptimizer(model_name='bert-base-uncased')
# Optimize hyperparameters
best_params = hyper_optimizer.optimize(train_dataset)
# Apply the best parameters
fine_tuner.apply_best_params(best_params)
Model Evaluation
metrics = fine_tuner.evaluate(test_dataset)
print(metrics)
Contributing
Contributions are welcome! Please see the CONTRIBUTING.md file for details.
License
This project is licensed under the MIT License. See the LICENSE file for details.
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