Open Source NLP Projects
Natural language processing (NLP) is the area of computing focused on analyzing, understanding, and generating human language. It helps software work with text and speech for tasks such as search, translation, summarization, classification, information extraction, and conversational interfaces. NLP methods range from rule-based systems to statistical models and neural networks, and can be applied to many languages and kinds of data.
Open source NLP tools include libraries for text processing, language models, data preparation, evaluation, and model training or adaptation. When choosing one, consider its license, maintenance activity, documentation, supported languages, hardware and software requirements, and compatibility with your existing systems. NLP tools are useful to developers, researchers, organizations, and educators building language-aware applications or studying how computers process language.
32 repositories · updated October 3, 2026
Fuzzywuzzy: Python Library for Fuzzy String Matching
Fuzzywuzzy is a popular Python library designed for fuzzy string matching, enabling efficient comparison and similarity scoring between text strings. It leverages Levenshtein distance to help with tasks like data cleaning and record linkage. While widely used, this project has been superseded by TheFuzz, which continues its development under a new name.
Awesome-pytorch-list: Find PyTorch Libraries, Tutorials, and Papers
A categorized directory of PyTorch libraries, learning materials, and paper implementations. Use it to discover resources across NLP, computer vision, probabilistic modeling, and other deep-learning topics.
RL4LMs: Fine-Tune Language Models with Reinforcement Learning
RL4LMs is a Python library for training language models against custom reward functions using on-policy reinforcement learning. It suits NLP researchers and developers who need configurable training components for text-generation tasks.
rerankers: Use Diverse Reranking Models Through One Python API
rerankers provides a shared Python interface for reranking documents with cross-encoders, LLM-based methods, and hosted APIs. It suits developers building retrieval systems who want to compare or switch rerankers without adapting their application to each model's interface.
Docling: Streamline Document Processing for Generative AI Applications
Docling is a powerful Python library designed to simplify document processing, preparing diverse formats for generative AI applications. It offers advanced parsing capabilities, including sophisticated PDF understanding, and provides a unified document representation. With seamless integrations into the AI ecosystem, Docling empowers developers to build robust AI solutions.
DataDreamer: Generate Synthetic Data and Train LLMs
DataDreamer is a Python library for building LLM workflows, generating synthetic datasets, and training or aligning models. It suits researchers and developers who want reproducible, resumable workflows across open-source and API-based models.
EasyInstruct: An Easy-to-Use Instruction Processing Framework for LLMs
EasyInstruct is an open-source Python framework designed to simplify instruction processing for Large Language Models (LLMs). Accepted at ACL 2024, it offers modularized components for instruction generation, selection, and prompting, supporting various LLMs like GPT-4 and LLaMA. This framework is ideal for researchers and developers working on LLM-based experiments and applications.
LangTest: A Comprehensive Library for Safe & Effective Language Models
LangTest is an open-source Python library dedicated to ensuring the safety and effectiveness of language models. It offers a comprehensive framework for testing model quality, covering robustness, bias, fairness, and accuracy across various NLP tasks and LLM providers. With LangTest, developers can generate and execute over 60 distinct test types with just one line of code, promoting responsible AI development.
AuditNLG: Auditing Generative AI for Trustworthiness
AuditNLG is an open-source library from Salesforce designed to enhance the trustworthiness of generative AI language models. It provides state-of-the-art techniques to detect and improve factualness, safety, and constraint adherence in AI-generated text. This library simplifies the process of auditing AI outputs, offering explanations and alternative suggestions for problematic content.
spacy-llm: Integrating LLMs into Structured NLP Pipelines with spaCy
spacy-llm seamlessly integrates Large Language Models (LLMs) into spaCy, offering a modular system for rapid prototyping and transforming unstructured LLM responses into robust outputs for various NLP tasks. It supports a wide range of LLMs, including OpenAI, Cohere, Anthropic, and open-source models, enabling users to combine the power of LLMs with spaCy's production-ready capabilities. This package allows for quick experimentation and the creation of efficient, reliable, and controlled NLP systems.
Qwen3: Alibaba Cloud's Advanced Large Language Model Series
Qwen3 is a powerful series of large language models developed by the Qwen team at Alibaba Cloud. It offers advanced capabilities in reasoning, multilingual support, and long-context understanding, available in various sizes and modes for diverse applications. This repository provides comprehensive resources for running, deploying, and building with Qwen3 models.
Trafilatura: Advanced Web Scraping and Text Extraction in Python
Trafilatura is a robust Python package and command-line tool designed for gathering text and metadata from the web. It simplifies web crawling, scraping, and content extraction, transforming raw HTML into structured data. Widely adopted by major companies and institutions, it offers high efficiency and accuracy for various text processing needs.