This repository provides a comprehensive tutorial for Natural Language Processing (NLP) tasks, targeting beginners and practitioners looking for practical PyTorch implementations. It covers fundamental concepts like word embeddings and lexical analysis, as well as advanced topics such as pre-trained language models, text classification, semantic matching, information extraction, machine translation, and dialogue systems, serving as a valuable learning resource and a baseline for real-world applications.

How It Works

The tutorial is structured into distinct directories, each focusing on a specific NLP task. It offers both conceptual explanations and practical code examples, often implemented from scratch or using popular libraries like PyTorch, Transformers, and Gensim. This approach allows users to understand the underlying mechanisms of various NLP models and techniques, from traditional methods like LSTMs and CRFs to state-of-the-art architectures like BERT and Transformers.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python >= 3.7. Anaconda is recommended for environment management.
• Usage: Run Jupyter Notebooks within the project directory. Colab links are provided for each notebook.
• Docs: https://github.com/shibing624/nlp-tutorial

Highlighted Details

• Covers a wide range of NLP tasks from basic word embeddings to complex dialogue systems.
• Provides implementations from scratch and fine-tuning examples using pre-trained models.
• Includes notebooks for training models like Skip-gram, LSTM, CRF, BERT, and Transformers.
• Offers practical applications such as text classification, semantic matching, and named entity recognition.

Maintenance & Community

• The project is maintained by Xu Ming.
• Contact: xuming624@qq.com. A WeChat group for Python-NLP discussion is available.
• Cite: The project can be cited using the provided LaTeX format.

Licensing & Compatibility

• Licensed under The Apache License 2.0.
• Permitted for commercial use, with attribution to the project and license required.

Limitations & Caveats

The project code is described as "rough," and contributions with passing unit tests are welcomed. While it covers many NLP tasks, specific performance benchmarks or comparisons between different implementations are not explicitly detailed.