This repository provides simple, foundational implementations of various Natural Language Processing (NLP) models and concepts, targeting students and developers looking to understand core NLP techniques. It offers clear code examples for algorithms like TF-IDF, Word2Vec, Seq2Seq, Attention, Transformer, ELMo, GPT, and BERT.

How It Works

The project breaks down complex NLP topics into digestible, single-concept code files. It focuses on straightforward implementations using common libraries, allowing users to grasp the underlying mechanics of each model without being overwhelmed by advanced frameworks or extensive configurations.

Quick Start & Requirements

• Primary install: git clone https://github.com/MorvanZhou/NLP-Tutorials followed by cd NLP-Tutorials/ and sudo pip3 install -r requirements.txt.
• Prerequisites: Python 3, pip. Specific model implementations may require additional libraries listed in requirements.txt.
• The project's primary documentation and tutorials are in Chinese on mofanpy.com.

Highlighted Details

• Covers foundational NLP techniques from TF-IDF to modern architectures like Transformers and BERT.
• Includes implementations for Word2Vec (CBOW, Skip-Gram), Seq2Seq, Attention, ELMo, GPT, and BERT.
• Offers both NumPy/Scikit-learn and PyTorch versions for many models.
• Features simplified Keras code in the simple_realize directory.

Maintenance & Community

The repository has contributions from users like @W1Fl and @ruifanxu, indicating community engagement. Further community interaction details are not readily available in the README.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should exercise caution regarding usage, especially for commercial or closed-source applications, until a license is clarified.

Limitations & Caveats

The primary tutorials are in Chinese, which may be a barrier for non-Chinese speakers. The focus is on simple implementations, meaning advanced optimizations, extensive error handling, or production-ready features are likely absent.