This project generates Hacker News comments based solely on submission titles, aiming to replicate the phenomenon of comments disregarding the linked article. It's designed for users interested in AI-generated text, natural language processing experiments, or simply for amusement.

How It Works

The project utilizes a Transformer encoder-decoder architecture trained on Hacker News data, with an optional inclusion of Wikipedia data. This approach allows the model to learn the relationship between a title and a relevant, albeit often tangential or nonsensical, comment.

Quick Start & Requirements

• Install/Run: Download the pretrained model from https://hncynic.leod.org/hncynic-trained-model-v1.tar.gz. Further instructions for training and serving are available in the repository.
• Prerequisites: TensorFlow and OpenNMT-tf are required for training.
• Setup: Model download is immediate. Training requires significant computational resources and time.

Highlighted Details

• Transformer encoder-decoder model trained on Hacker News and Wikipedia data.
• Focuses on generating comments from titles only, excluding replies from training data.
• Offers options for serving the model and hosting a web interface.

Maintenance & Community

The project was last updated in 2019. No community links or active maintenance signals are present.

Licensing & Compatibility

The license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is undetermined.

Limitations & Caveats

The generated comments are often meaningless or contradictory. The author notes that an encoder-decoder model might not be ideal and suggests a language model approach (like GPT-2) could be more suitable.