This repository provides simplified code examples and explanations for fundamental Natural Language Processing (NLP) building blocks. It targets engineers and researchers seeking to understand and implement core NLP concepts like subword segmentation, sequence-to-sequence models, and attention mechanisms. The benefit is a clearer grasp of these techniques through practical, easy-to-follow code.

How It Works

The project breaks down complex NLP tasks into digestible code snippets and explanations. It covers various tokenization methods (NLTK, BPE, SentencePiece, BERT), offers a simplified batchified beam decoding implementation for seq2seq tasks, and demonstrates how to properly extract RNN hidden states in both TensorFlow and PyTorch. Seq2seq templates are provided using both frameworks, with the grapheme-to-phoneme (g2p) task serving as a practical example.

Quick Start & Requirements

• Installation: Primarily through cloning the repository and running Python scripts.
• Prerequisites: Python 3.x, TensorFlow, PyTorch, NLTK, SentencePiece, Hugging Face Transformers. Specific versions are not mandated but compatibility is expected.
• Setup: Minimal, focused on running individual scripts.

Highlighted Details

• Comparative analysis of various subword tokenization techniques.
• Simplified, batchified implementation of beam decoding for seq2seq.
• TensorFlow and PyTorch seq2seq templates demonstrated with the g2p task.
• Exploration of BERT for POS-tagging and dropout regularization.

Maintenance & Community

• The repository is maintained by Kyubyong.
• No explicit community channels or roadmap are linked in the README.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

The project is presented as "code notes" and may not represent production-ready, robust implementations. Some sections are marked as "WIP" (Work In Progress), indicating incomplete or experimental content. The lack of a specified license could pose compatibility issues for commercial or derivative works.