This repository serves as a comprehensive resource hub for Natural Language Processing (NLP) practitioners, particularly those involved in competitions. It aggregates competition strategies, baseline implementations, past and current competition insights, relevant learning materials, and practical information like conference schedules and hardware recommendations, aiming to accelerate learning and performance for NLP enthusiasts.

How It Works

The project is structured into several key components: a code repository for modular NLP competition strategies, tutorials with commented baselines for various NLP tasks (text classification, generation), a collection of past competition summaries with datasets and solutions, and a curated list of current competitions. It also includes resources like recommended media, computing power options, competition platforms, and NLP conference timelines.

Quick Start & Requirements

• Installation: Primarily through GitHub cloning and following individual component instructions.
• Prerequisites: Python environment, potentially specific libraries depending on the code modules used. No explicit system requirements are detailed beyond standard development tools.
• Resources: Varies by code module; some may require significant GPU resources for training.
• Links:
  • Code: here
  • Tutorials: here
  • Past Competitions: here

Highlighted Details

• Extensive lists of current and past NLP competitions, including deadlines and task descriptions.
• Curated resources for learning NLP, including tutorials, media recommendations, and computing power options.
• Detailed tracking of NLP conference dates and submission deadlines.
• A growing collection of competition strategies and baseline implementations.

Maintenance & Community

The project is actively maintained and welcomes community contributions via GitHub issues and email (hello@arsenal-ai.cn). It encourages users to cite the GitHub link when reposting content.

Licensing & Compatibility

Content is collected from public resources; copyright belongs to original authors. The repository itself does not specify a license, implying all rights are reserved by the collectors unless otherwise stated for specific code components. Compatibility for commercial use or closed-source linking is not explicitly addressed.

Limitations & Caveats

The project aggregates information from various sources, and the accuracy or completeness of all listed details (especially deadlines and specific competition requirements) may vary. Users should verify information with official sources. Some computing resource recommendations may be outdated or require careful vetting of third-party platforms.