Popular GitHub Repositories for Stanford Parser

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

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

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

File Parser optimised for LLM Ingestion with no loss ? Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

A Unified Toolkit for Deep Learning Based Document Image Analysis

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

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

Snips Python library to extract meaning from text

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

A Repo For Document AI

Deep neural network to extract intelligent information from invoice documents.

All notes and materials for the CS229: Machine Learning course by Stanford University

文本挖掘和预处理工具（文本清洗、新词发现、情感分析、实体识别链接、关键词抽取、知识抽取、句法分析等），无监督或弱监督方法

A python module to repair invalid JSON from LLMs

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

The "official" unofficial DSPy framework. Build LLM powered agents and other workflows, based on the Stanford DSP paper.

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text