Finance-Curriculum-Edu-Uzbek

# Finance Curriculum Edu Uzbek


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## Dataset Summary

**English:**
Finance Curriculum Edu Uzbek is an expansive, curated Q&A dataset covering the full range of practical and professional finance, with a special focus on the Uzbek language and context. Part of a larger multilingual project, this dataset is designed for language model training, benchmarking, and financial education. It features over 2,200 unique, carefully categorized seed questions mapped to a multi-level finance curriculum. Every entry is structured to encourage deep, transparent reasoning—incorporating global standards, professional practices, and practical, scenario-driven finance.

Seed data originally from: [finance_curriculum_topics](https://huggingface.co/datasets/Josephgflowers/finance_curriculum_topics)

**Uzbekcha:**
Finance Curriculum Edu Uzbek — amaliy va professional moliya sohasining barcha asosiy yo‘nalishlarini qamrab oluvchi, katta va saralangan savol-javoblar to‘plamidir. Ushbu dataset maxsus o‘zbek tili va moliyaviy kontekstga yo‘naltirilgan bo‘lib, ko‘p tilli yirik loyiha doirasida yaratilgan. LLMlarni o‘qitish, baholash va moliyaviy ta’lim uchun mo‘ljallangan. Unda 2200 dan ortiq noyob, aniq kategoriyalangan boshlang‘ich savollar mavjud bo‘lib, ularning har biri ko‘p bosqichli moliyaviy ta’lim dasturi xaritasiga moslab tuzilgan. Har bir yozuv puxta va oshkora mulohaza yuritishni rag‘batlantiradi, zamonaviy xalqaro standartlar va amaliyotlarni, shuningdek, haqiqiy holatlarni o‘z ichiga oladi.

Asosiy savol-yo‘nalishlar: [finance_curriculum_topics](https://huggingface.co/datasets/Josephgflowers/finance_curriculum_topics)

---

## Key Features / Asosiy xususiyatlar

**English:**
- **Comprehensive Coverage:**
Spans core finance, personal finance, business/enterprise, modeling/simulation, marketing, digital assets, crypto, legal, risk, tax, and more.

- **Scenario-Based & Modern:**
Goes beyond theory—covers platform finance (Shopify, ERP, SaaS), digital business, gig economy, crypto, datasets, influencer commerce, and real-world edge cases.

- **Instructional System Prompts:**
Each entry includes expert system prompts requiring step-by-step, transparent, and advanced reasoning.

- **Well-Organized Taxonomy:**
All questions are tagged with their curriculum position (e.g., “Personal Finance - Vehicles - Car loan refinancing”) for filtering and curriculum-building.

- **Designed for LLMs and Education:**
Ideal for fine-tuning, benchmarking, RAG, tutoring agents, or as a reference for educators and knowledge engineers.

**Uzbekcha:**
- **To‘liq qamrov:**
Asosiy moliya, shaxsiy moliya, biznes, modellashtirish, marketing, raqamli aktivlar, kripto, huquqiy masalalar, risk, soliq va boshqa sohalarni qamrab oladi.

- **Zamonaviy va holatga asoslangan:**
Faqat nazariya emas — Shopify, ERP, SaaS, raqamli biznes, gig-ekonomika, kripto, ma’lumotlar to‘plamlari, influencerlar savdosi va haqiqiy edge-case holatlarni o‘z ichiga oladi.

- **Instruksion sistem promptlar:**
Har bir yozuv puxta ishlab chiqilgan tizimli system prompt bilan birga bo‘lib, bosqichma-bosqich, oshkora tahlil va yuqori darajadagi mulohazani talab qiladi.

- **Yaxshi tuzilgan tasnif:**
Barcha savollar o‘z mavzusi va o‘quv dasturi bo‘yicha teglangan (masalan, “Shaxsiy moliya - Avtotransport - Avtokreditni qayta moliyalashtirish”).

- **LLMlar va ta’lim uchun:**
Modelni fine-tune qilish, baholash, RAG, tutor-agentlar yoki o‘qituvchilar uchun bilim bazasi sifatida mos.

---

## Intended Uses / Foydalanish maqsadlari

**English:**
- Training and evaluating LLMs for financial literacy, professional reasoning, and business Q&A tasks.
- Curriculum and resource design for finance educators and trainers.
- Knowledge base expansion, semantic search, and chatbot/assistant training (including chain-of-thought).
- Financial tutoring and digital literacy agents in Uzbek and multilingual settings.

**Uzbekcha:**
- LLMlarni moliyaviy savodxonlik, professional fikrlash va biznes-savollarga javob berish bo‘yicha o‘qitish va baholash.
- Moliya sohasida o‘qituvchilar va trenerlar uchun o‘quv dasturlari va resurslarni ishlab chiqish.
- Bilim bazasini kengaytirish, semantik qidiruv va chatbot/tutor-agentlarni tayyorlash (jumladan, chain-of-thought ko‘rinishida).
- O‘zbek va boshqa tillarda moliyaviy ta’lim va raqamli savodxonlik uchun.

---

## Dataset Structure / Ma’lumotlar tuzilmasi

| Field | Description (EN) | Tavsif (UZ) |
|-----------------|---------------------------------------------------------------|--------------------------------------------------------------------|
| task_type | General instruction type (e.g., general_finance_qa) | Umumiy instruksiya turi (masalan, general_finance_qa) |
| language | Language of entry (Uzbek for this version) | Yozuv tili (bu versiyada faqat o‘zbek tili) |
| instruction_type| Question style/intent (e.g., conceptual, code, math) | Savol turi yoki niyati (masalan, konseptual, kod, matematik) |
| reasoning_tags | Reasoning tags/xml tags (optional, may be empty) | Mulohaza yuritish teglari/xml teglari (ixtiyoriy) |
| contains_code | Indicates if the answer contains code | Javobda kod bor-yo‘qligini ko‘rsatadi |
| topic | Hierarchical curriculum topic | Hierarxik o‘quv mavzusi |
| system | System prompt (model’s reasoning style) | Sistem prompt (modelga xos tahlil uslubi) |
| user | Input question (realistic, scenario-rich) | Kirish savoli (real holatlar asosida, dolzarb) |
| assistant | High-level answer with step-by-step reasoning | Batafsil, bosqichma-bosqich mulohazali javob |

---

## Citation

**English:**
If you use this dataset, please cite as:

```bibtex
@misc{finance-curriculum-edu-uzbek,
author = {Joseph Flowers},
title = {Finance Curriculum Edu Uzbek},
year = {2025},
howpublished = {\url{https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_Uzbek}}
}
````

**Uzbekcha:**
Agar siz ushbu datasetdan foydalansangiz, iltimos, quyidagicha keltiring:

```bibtex
@misc{finance-curriculum-edu-uzbek,
author = {Joseph Flowers},
title = {Finance Curriculum Edu Uzbek},
year = {2025},
howpublished = {\url{https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_Uzbek}}
}
```

---

## Contact

Questions, feedback, or collaboration?

* Contact: [Josephgflowers](https://huggingface.co/Josephgflowers)
* See also: [Finance Curriculum Edu English](https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_English)

---

*Dataset card last updated: 2025-07-29. Community improvements welcome!*

# 金融课程教育乌兹别克语版（Finance Curriculum Edu Uzbek）


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## 数据集概览

Finance Curriculum Edu Uzbek是一个规模庞大、经过精心甄选的问答数据集，覆盖实用与专业金融的全领域范畴，尤其聚焦乌兹别克语语境与相关场景。本数据集作为大型多语言项目的组成部分，专为大语言模型（LLM）训练、评测与金融教育场景设计。数据集包含超过2200条经过精准分类的独特种子问题，均映射至多级金融课程体系。每条数据均旨在引导深度且可追溯的推理过程，融合全球行业标准、专业实践与场景化金融知识。

种子数据源自：[finance_curriculum_topics](https://huggingface.co/datasets/Josephgflowers/finance_curriculum_topics)

---

## 核心特性

- **全面覆盖**：涵盖核心金融、个人金融、企业商务、建模仿真、营销、数字资产、加密货币、法律合规、风险管理、税务筹划等多个细分领域。
- **场景化与前沿性**：跳出纯理论框架，覆盖平台金融（Shopify、ERP、SaaS）、数字商务、零工经济、加密货币、数据集、网红电商等真实边缘场景。
- **标准化系统提示**：每条数据均配备专业系统提示，要求模型执行分步、可追溯且高阶的推理流程。
- **结构化分类体系**：所有问题均标注其课程归属层级（例如"个人金融 - 交通工具 - 汽车贷款再融资"），便于筛选与课程体系搭建。
- **适配多场景需求**：适用于大语言模型微调、基准测试、检索增强生成（RAG）、家教AI智能体开发，亦可作为教育工作者与知识工程师的参考知识库。

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## 预期用途

- 针对金融素养培养、专业推理能力与商务问答任务的大语言模型训练与评测。
- 为金融教育工作者提供课程设计与资源开发的参考。
- 支持知识库扩容、语义搜索与聊天机器人/智能助手训练（含思维链（Chain-of-Thought）技术）。
- 服务乌兹别克语及多语言场景下的金融家教与数字素养AI智能体。

---

## 数据集结构

| 字段名 | 英文描述 | 中文说明 |
|-----------------|-----------------------------------------------------------------------|--------------------------------------------------------------------|
| task_type | General instruction type (e.g., general_finance_qa) | 通用指令类型（例如 general_finance_qa） |
| language | Language of entry (Uzbek for this version) | 本数据集版本的条目语言（本版本为乌兹别克语） |
| instruction_type| Question style/intent (e.g., conceptual, code, math) | 问题风格与意图（例如 conceptual、code、math） |
| reasoning_tags | Reasoning tags/xml tags (optional, may be empty) | 推理标签/XML标签（可选，可为空） |
| contains_code | Indicates if the answer contains code | 标识回答是否包含代码 |
| topic | Hierarchical curriculum topic | 层级化课程主题 |
| system | System prompt (model’s reasoning style) | 系统提示（模型推理风格） |
| user | Input question (realistic, scenario-rich) | 输入问题（贴合真实场景，场景化设计） |
| assistant | High-level answer with step-by-step reasoning | 包含分步推理过程的高质量回答 |

---

## 引用格式

若使用本数据集，请按以下格式引用：
bibtex
@misc{finance-curriculum-edu-uzbek,
author = {Joseph Flowers},
title = {Finance Curriculum Edu Uzbek},
year = {2025},
howpublished = {url{https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_Uzbek}}
}

---

## 联系方式

如有疑问、反馈或合作意向，请通过以下渠道联系：
* 联系人：[Josephgflowers](https://huggingface.co/Josephgflowers)
* 扩展参考：[金融课程教育英文版（Finance Curriculum Edu English）](https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_English)

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*本数据集卡片最后更新于2025-07-29，欢迎社区贡献改进！*