Natural language processing (NLP) is a field of computer science that studies how computers and humans interact. In the 1950s, Alan Turing published an article that proposed a measure of intelligence, now called the Turing test. More modern techniques, such as deep learning, have produced results in the fields of language modeling, parsing, and natural-language tasks.
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
An open-source online reverse dictionary.
Officially maintained, supported by PaddlePaddle, including CV, NLP, Speech, Rec, TS, big models and so on.
Reading list for research topics in multimodal machine learning
Mycroft Core, the Mycroft Artificial Intelligence platform.
An NLP library for building bots, with entity extraction, sentiment analysis, automatic language identify, and so more
AI suite powered by state-of-the-art models and providing advanced AI/AGI functions. It features AI personas, AGI functions, multi-model chats, text-t...
🎓 Sharing machine learning course / lecture notes.
Natural Language Processing Best Practices & Examples
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
A curated list of awesome self-supervised methods
AI算法岗求职攻略(涵盖准备攻略、刷题指南、内推和AI公司清单等资料)
:alarm_clock: AI conference deadline countdowns
Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understan...
Python & Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML
A large-scale 7B pretraining language model developed by BaiChuan-Inc.
:pencil: A markup-aware linter for prose built with speed and extensibility in mind.
Language Technology Platform
This repository is a compilation of free resources for learning Data Science.
Ecommerce Search and Discovery - marqo.ai
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
🚀 Efficient implementations for emerging model architectures
An Open-Source Framework for Prompt-Learning.
A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.
Data augmentation for NLP
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
🤗 AutoTrain Advanced
Go to https://github.com/pytorch/tutorials - this repo is deprecated and no longer maintained
extract text from any document. no muss. no fuss.
《大语言模型》作者:赵鑫,李军毅,周昆,唐天一,文继荣
🏝️ OASIS: Open Agent Social Interaction Simulations with One Million Agents.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Code examples in pyTorch and Tensorflow for CS230
Collection of useful data science topics along with articles, videos, and code
State of the Art Natural Language Processing
A series of large language models developed by Baichuan Intelligent Technology
🌟100+ 原创 LLM / RL 原理图📚,《大模型算法》作者巨献!💥(100+ LLM/RL Algorithm Maps )
repository to research & share the machine learning articles
Facilitating the design, comparison and sharing of deep text matching models.
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
💁♀️Your new best friend powered by an artificial neural network
A collection of scientific methods, processes, algorithms, and systems to build stories & models.
The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
【三年面试五年模拟】AIGC算法工程师面试秘籍。涵盖AIGC、LLM大模型、AI Agent、传统深度学习、自动驾驶、机器学习、计算机视觉、自然语言处理、强化学习、大数...
The official Python client for the Hugging Face Hub.
This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Langua...
Open Machine Learning course
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master...
Accelerated deep learning R&D
Learn how to design, develop, deploy and iterate on production-grade ML applications.