Hybrid Text Classification Model

The Hybrid Text Classification Model combines the powerful sentence embedding capabilities of Sentence Transformers with the simplicity and effectiveness of a Logistic Regression classifier from scikit-learn. This package is designed to offer an easy-to-use interface for training and predicting text classifications, ideal for a wide range of NLP tasks.

Features

• Sentence Embedding: Utilizes Sentence Transformers for generating rich text embeddings.
• Classification: Incorporates a Logistic Regression classifier for efficient text classification.
• Easy Integration: Designed for straightforward integration into existing Python projects.
• Logging: Includes logging for transparent monitoring of the training and prediction processes.
• Customizable: Supports customization of the underlying Sentence Transformer model and Logistic Regression classifier.

Installation

Ensure you have Python 3.6+ installed on your system. You can then install the package using the following steps:

1. Clone the repository:

git clone https://github.com/ameyachawlaggsipu/Hybrid_LLM_Classifier.git

2. Navigate to the cloned directory:

cd Hybrid_LLM_Classifier

3. Install the package:

pip install .

This will install the Hybrid Model package along with its dependencies.

Quick Start

Here's how to get started with the Hybrid Model package:

Initializing the Model

First, import and initialize the HybridModel with a pre-trained Sentence Transformer model ID.

from hybrid_model.model import HybridModel

model_id = "all-MiniLM-L6-v2"  # Example Sentence Transformer model ID
hybrid_model = HybridModel(model_id=model_id)

Training the Model

To train the model, provide a list of text samples and their corresponding labels.

text_list = ["This is a positive example.", "This is a negative example."]
label_list = [1, 0]  # Example binary labels

# Train the model
hybrid_model.train(text_list, label_list)

Making Predictions

Once trained, you can use the model to predict the classification of new text samples.

predictions = hybrid_model.predict(["This is a test."])
print(predictions)

Contributing

Contributions to improve the Hybrid Model package are welcome. Please feel free to fork the repository, make changes, and submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.