HuggingTweets enables users to fine-tune pre-trained language models on their Twitter data to generate personalized tweets. This project targets individuals interested in exploring AI-driven text generation and offers a quick way to create a custom tweet generator.

How It Works

The project leverages HuggingFace Transformers to fine-tune a pre-trained neural network on a user's tweet history. This approach allows the model to learn specific writing styles and vocabulary, enabling it to generate new tweets that mimic the user's persona. Training progress and results are automatically logged using Weights & Biases (W&B) for easy tracking and analysis.

Quick Start & Requirements

• Install dependencies using pipenv or pip.
• Requires Python and access to a GPU for training.
• Demo available via Google Colab for free GPU access.
• W&B report
• Development notebook

Highlighted Details

• Fine-tunes models on user-specific tweet data.
• Automatic logging of training and results to Weights & Biases.
• Demo accessible via Google Colab for ease of use.
• Explores future research directions like merging personalities and few-shot learning.

Maintenance & Community

• Built by Boris Dayma.
• Questions can be directed to the W&B Slack community.

Licensing & Compatibility

• The README does not explicitly state a license.

Limitations & Caveats

The project is intended for research and ethical use, with a disclaimer against generating false information or misuse for disinformation. Users must explicitly reference generated content and cannot publish it against individuals unwilling to have their data used.