4 个仓库
Grouping tasks into isolated namespaces based on topics to organize workflows and project isolation.
Distinct from Pipeline Task Grouping: Distinct from Pipeline Task Grouping: focuses on logical topic-based namespaces for organization rather than execution sequence wrapping.
Explore 4 awesome GitHub repositories matching software engineering & architecture · Topic-Based Partitioning. Refine with filters or upvote what's useful.
KnowledgeGraphData 是一个结构化数据集与语料库集合,旨在为认知智能与人工智能系统提供基础层。它主要由大规模中文知识图谱数据集组成,包括用于驱动语义理解与自动问答的实体关系数据与 NLP 训练集。 该项目专注于海量实体-属性-值图谱的构建与导出,将知识组织为可移植的格式。它提供专门的领域划分,以针对医疗、军事与公共安全等专业领域定制信息检索。 该仓库涵盖了广泛的能力,包括中文自然语言处理、语义搜索与认知对话系统。其工具集涵盖了语言分析、实体提取、情感检测与文本摘要,以及用于网站审计的视觉内容分析与语音转文字转换。
Organizes entity data into specialized professional domains to tailor information retrieval for specific industries.
Corpora 是一个精选的、特定领域的文本语料库和结构化数据集库。它提供 JSON 格式的分类名词、形容词和动词集合,旨在用作对话代理和自动化系统的静态训练或测试数据。 该项目在科学、艺术和地理等多个领域组织语言数据。这些数据集作为独立文件分发,使用语言中立的模式和静态 JSON 数据模型,无需 API 层即可直接导入应用程序。 该库通过提供结构化数据集成,支持聊天机器人内容生成和快速原型开发。它利用词性分类和特定领域的数据分区,促进对话创建和机器学习模型的测试。
Organizes linguistic datasets into topical categories to ensure broad knowledge coverage for conversational agents.
Dooit is a terminal-based task manager that utilizes a text user interface to organize todo lists and project workflows. It functions as a topic-based todo list, grouping items into separate topics with branching support to ensure organized project isolation. The application is designed for a keyboard-driven workflow, employing Vim-inspired shortcuts for the navigation and manipulation of categorized task lists. It is a configurable TUI application that allows users to define operational behavior and visual themes through external configuration files. The system includes capabilities for tas
Implements topic-based task partitioning to group distinct sets of tasks into isolated namespaces for organized project workflows.
UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and evaluating generative AI models. It provides structured JSON data consisting of complex, multi-round dialogue sequences intended to refine the performance of large language models in chat tasks. The project focuses on improving reasoning and response quality through a diverse set of interactions across multiple sectors. These datasets are used for supervised fine-tuning and instruction tuning workflows to improve how models follow complex directions and maintain context acros
Partitions training data into distinct industry and topical sectors to ensure broad knowledge coverage.