This project provides a sentiment analysis neural network, fine-tuned from BERT, ALBERT, or DistilBERT models on the Stanford Sentiment Treebank. It's designed for developers and researchers looking to implement or experiment with transformer-based sentiment analysis. The key benefit is the ability to leverage pre-trained language models for accurate sentiment classification.

How It Works

The core approach involves fine-tuning established transformer architectures (BERT, ALBERT, DistilBERT) on a specific dataset (Stanford Sentiment Treebank). This method leverages the contextual understanding capabilities of these large language models, adapting them to the nuances of sentiment expression within the target dataset for improved accuracy.

Quick Start & Requirements

• Install: pip install -r requirements.txt after cloning the repository and setting up a Python 3.9 virtual environment.
• Prerequisites: Python 3.9.10, pytest for testing.
• Setup: Requires cloning the repository, installing dependencies, and potentially downloading model weights.
• Links: GitHub Repository

Highlighted Details

• Supports fine-tuning of BERT, ALBERT, and DistilBERT models.
• Includes scripts for evaluation, analysis of custom inputs, and running a local server.
• Offers functionality to train (fine-tune) custom models.

Maintenance & Community

No specific information on contributors, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The README does not specify a license.

Limitations & Caveats

The project requires a specific Python version (3.9.10) and relies on external model weights which are not explicitly managed or linked within the README. No information on performance benchmarks or limitations of the fine-tuned models is provided.