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Natural Language Processing (NLP) is a rapidly growing field that deals with the interaction between computers and human language. As NLP continues to advance, there is a growing need for skilled professionals to develop innovative solutions for various applications, such as chatbots, sentiment analysis, and machine translation.
To help you on your journey to mastering NLP, we’ve curated a list of 20 GitHub repositories that offer valuable resources, code examples, and pre-trained models.
Essential Repositories: These libraries are basic components for building NLP architecture.
- Transformers is a state-of-the-art library developed by Hugging Face that provides pre-trained models and tools for a wide range of natural language processing (NLP) tasks. It’s built on top of popular deep learning frameworks like PyTorch and TensorFlow, making it accessible to a broad audience of developers and researchers. Transformers offers a vast collection of pre-trained models for various NLP tasks, including Sequence Classification, Question Answering, and Named Entity Recognition. You can fine-tune the pre-trained models on your own datasets to adapt them to specific tasks or domains.
- spaCy is a popular open-source Python library designed for natural language processing (NLP) tasks. Known for its speed and efficiency, spaCy is particularly well-suited for production environments where performance is critical. It offers a variety of features, including tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and text categorization. spaCy is highly customizable and integrates well with other Python libraries and frameworks, making it a versatile tool for a wide range of NLP applications.
- NLP Progress is a valuable resource for staying updated on the latest advancements in natural language processing (NLP). This GitHub repository provides a comprehensive overview of the state-of-the-art for various NLP tasks, including machine translation, named entity recognition, part-of-speech tagging, question answering, and sentiment analysis. It offers links to the most recent and best-performing models and datasets, making it easy for researchers and practitioners to compare different approaches and identify the most promising techniques.
- NLP Tutorial is a comprehensive guide for deep learning researchers, providing implementations of various NLP models using PyTorch. This repository offers a hands-on approach to understanding the inner workings of NLP models, with most implementations consisting of less than 100 lines of code. The key feature of the repository is that it provides detailed explanations of the theory behind each model and concise and easy to understand code.
- Awesome NLP is a curated list of resources dedicated to natural language processing (NLP). It provides a comprehensive collection of libraries, tools, datasets, blogs, tutorials, and academic papers related to NLP. This valuable resource helps individuals explore the world of NLP by offering a wide range of high-quality and relevant content organized into categories for easy navigation.
Project-Based Learning: The next 5 repositories that consists of great projects that will help you to learn process of developing NLP.
- 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code is a vast repository offering a wide range of projects across various AI domains, including natural language processing (NLP). It is an excellent resource for those looking to explore practical implementations and gain hands-on experience with different NLP techniques. The projects are organized into categories based on their domain (e.g., machine learning, deep learning, computer vision, NLP), which make it easier for beginners to choose the right project.
- Best of ML Python is a ranked list of exceptional machine learning Python libraries, projects, datasets, tools, and utilities. It serves as a valuable resource for developers and researchers seeking the best tools for their machine learning projects, including those specifically designed for NLP tasks. The repository offers a comprehensive list of resources, organized by popularity and category, and is regularly updated to include new and emerging tools.
- ML YouTube Courses is a curated repository of the latest machine learning and AI courses available on YouTube. It offers a valuable resource for visual learners, providing access to engaging and informative content taught by renowned instructors from top institutions. It also includes a wide range of topics, from introductory concepts to advanced techniques, making it a valuable tool for learners at all levels.
- Oxford Deep NLP is a repository containing lectures and materials from a 2017 course on deep learning for natural language processing (NLP) offered by the University of Oxford. This comprehensive course covers both fundamental and advanced topics, providing a solid foundation in the field. The course features lectures from renowned experts and includes supplementary materials such as slides, assignments, and readings, making it a valuable resource for those seeking to learn about NLP.
- NVIDIA Deep Learning Examples offers state-of-the-art deep learning scripts for various models, including NLP. It is a great resource for learning how to build and train NLP models. These scripts are designed for easy training and deployment, providing reproducible accuracy and performance on enterprise-grade infrastructure. Ideal for those seeking to deploy NLP solutions into production, the repository includes pre-trained models, well-documented scripts, and optimization for high-performance computing environments.
Specialized Repositories: There are some libraries that are specially designed to make NLP tasks easier and available for wider applications.
- AllenNLP is a popular open-source research library for natural language processing (NLP) built on PyTorch. Its modular architecture allows researchers to easily experiment with different NLP models and components, making it a valuable tool for both research and production applications.
- Gensim is a Python library designed for topic modeling, document similarity, and word embedding. It provides efficient implementations of popular algorithms such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and word2vec. Gensim is a valuable tool for researchers and practitioners who need to analyze large datasets of text.
- NLTK (Natural Language Toolkit) is a leading platform for building Python programs that work with human language data. It offers a comprehensive set of tools and libraries for tasks such as tokenization, part-of-speech tagging, named entity recognition, chunking, and parsing. NLTK’s user-friendly API, extensive documentation, and large community make it a popular choice for both beginners and experienced NLP practitioners.
- TextBlob is a Python library that provides a simple API for common natural language processing (NLP) tasks. Built on top of NLTK and pattern, TextBlob offers a user-friendly interface for tasks like sentiment analysis, part-of-speech tagging, and named entity recognition. Its ease of use and versatility make it a great choice for those who are new to NLP or seeking a quick and efficient way to perform common NLP tasks.
- fastText is a Facebook AI Research project that offers a fast and efficient way to learn word representations. Known for its speed and accuracy, fastText is particularly effective for large datasets and can be used for various NLP tasks such as text classification, word vectors, and document similarity.
Additional Resources: Here are some repositories that provide a variety of resources to get you started with NLP.
- NLP Datasets is a repository that offers a collection of publicly available datasets for various natural language processing (NLP) tasks. These high-quality datasets cover a wide range of domains and languages, making it easy for researchers and practitioners to find suitable data for their projects.
- NLP Papers is a curated repository of influential research papers in the field of natural language processing (NLP). This valuable resource provides researchers and practitioners with access to the most important and influential papers in the field, organized by topic and easily accessible through links or direct downloads. By exploring NLP Papers, you can stay up-to-date with the latest advancements in NLP and discover groundbreaking research that can inform your own work.
- NLP Blogs is a collection of blogs and websites dedicated to natural language processing (NLP). This valuable resource provides a platform for staying up-to-date with the latest news, trends, and research in the field. With diverse content, regular updates, and opportunities for community engagement, NLP Blogs offer a valuable way to learn from experienced practitioners and connect with other NLP professionals.
- NLP Online Courses is a repository that provides a list of online courses that teach natural language processing (NLP) concepts and techniques. These courses offer a convenient and flexible way to learn NLP from experts in the field, with options for self-paced learning, certificate programs, and affordable pricing.
- Awesome Community-Curated NLP List is a repository that provides a list of online communities and forums where you can connect with other natural language processing (NLP) enthusiasts. By joining NLP Communities, you can expand your network, share ideas, learn from others, and stay up-to-date with the latest trends in the field.
By exploring these repositories and leveraging the resources they provide, you can gain a solid understanding of NLP and develop the skills necessary to build innovative applications. Remember, practice is key to mastering NLP. So, start experimenting with these repositories and see what you can create!
Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Kharagpur. She is a tech enthusiast and has a keen interest in the scope of software and data science applications. She is always reading about the developments in different field of AI and ML.
