Popular GitHub Repositories for Measurement Parsing

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 80+ languages.

中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

Unified framework for building enterprise RAG pipelines with small, specialized models

CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.

中文 NLP 预处理、解析工具包，准确、高效、易用 A Chinese NLP Preprocessing & Parsing Package www.jionlp.com

fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

Using modified BiSeNet for face parsing in PyTorch

A large collection of system log datasets for AI-driven log analytics [ISSRE'23]

??? Advanced NLP with spaCy: A free online course

A large-scale face dataset for face parsing, recognition, generation and editing.

A machine learning toolkit for log parsing [ICSE'19, DSN'16]

A large annotated semantic parsing corpus for developing natural language interfaces.

Underthesea - Vietnamese NLP Toolkit

Semantic search and document parsing tools for the command line

ExtractThinker is a Document Intelligence library for LLMs, offering ORM-style interaction for flexible and powerful document workflows.

[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild

Introducing DeToxify – the ultimate toxicity buster. Armed with NLP superpowers, it sniffs out harmful comments, neutralizes negativity, and rewrites toxicity into pure class. Think of it as Jarvis for your online spaces—making the internet a kinder, smarter place, one comment at a time.