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.
21 repositories · updated October 7, 2026
python-nameparser: Parse Human Names into Components
A Python library that splits human names into structured fields such as given name, family name, title, suffix, and nickname. It suits applications that need to organize, format, or compare names while accounting for varied naming conventions.
ActiveHealth 70/100
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.
MaintainedHealth 37/100
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.
InactiveHealth 24/100
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.
QuietHealth 28/100
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.
InactiveHealth 19/100
EasyInstruct: Generate, Select, and Prompt LLM Instructions
EasyInstruct is a Python framework for preparing instruction data and prompts for large language model research. It combines instruction generation and dataset selection tools with prompt and local-model execution modules.
InactiveHealth 29/100
langtest: Test Language Models for Safety and Quality
LangTest is a Python library for generating and running tests that assess language-model quality, including robustness, bias, fairness, and accuracy. It helps NLP and AI teams identify issues and, for select models, augment training data based on results.
ActiveHealth 58/100
spacy-llm: Add LLM Tasks to spaCy NLP Pipelines
spacy-llm connects large language models to spaCy pipelines, turning model responses into structured NLP outputs without training data. It suits teams prototyping NLP tasks or combining LLM components with conventional spaCy processing.
ActiveHealth 49/100
trafilatura: Extract Text and Metadata from Web Pages
Trafilatura is a Python library and CLI for discovering web content and extracting readable text and metadata from HTML. It suits developers and researchers building crawlers, web corpora, or content-processing pipelines that need structured output.
ActiveHealth 53/100
jieba: Segment Chinese Text in Python
jieba is a Python library for segmenting Chinese text into words, including text that does not use spaces as word boundaries. It offers configurable segmentation modes, custom dictionaries, part-of-speech tagging, and keyword extraction.
InactiveHealth 29/100
insanely-fast-whisper: Transcribe Audio with Whisper
A command-line tool for running Whisper speech recognition on your own NVIDIA GPU or Apple Silicon Mac. It combines batching and optional Flash Attention to speed up transcription, with options for timestamps, translation, and speaker diarization.
QuietHealth 26/100
unstructured: Turn Documents Into Structured Data
Unstructured is a Python library for parsing and preprocessing documents into structured elements for downstream applications, including LLM workflows. It supports many file types, with format-specific dependencies for some inputs.
ActiveHealth 63/100