Ukraine General Conversational Text Dataset

This dataset features natural text-based conversations in Ukrainian between native speakers. Covering a wide range of everyday topics such as health, food, family, education, and entertainment, the dataset is rich in colloquial expressions, real-world references, and linguistic diversity. Ideal for training conversational AI, NLP models, chatbots, and smart assistants.

Category

Conversational Chat Dataset

Total volume

15K+ chats

Last Updated

July 2025

Number of participants

200 people

General Multilingual chat dataset in Ukrainian

About This OTS Dataset

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Introduction

The Ukrainian General Domain Chat Dataset is a high-quality, text-based dataset designed to train and evaluate conversational AI, NLP models, and smart assistants in real-world Ukrainian usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Ukrainian conversations covering a broad spectrum of everyday topics.

Conversational Text Data

This dataset includes over 15000 chat transcripts, each featuring free-flowing dialogue between two native Ukrainian speakers. The conversations are spontaneous, context-rich, and mimic informal, real-life texting behavior.

  • Words per Chat: 300–700
  • Turns per Chat: Up to 50 dialogue turns
  • Contributors: 200 native Ukrainian speakers from the FutureBeeAI Crowd Community
  • Format: TXT, DOCS, JSON or CSV (customizable)
  • Structure: Each record contains the full chat, topic tag, and metadata block
  • Diversity and Domain Coverage

    Conversations span a wide variety of general-domain topics to ensure comprehensive model exposure:

  • Music, books, and movies
  • Health and wellness
  • Children and parenting
  • Family life and relationships
  • Food and cooking
  • Education and studying
  • Festivals and traditions
  • Environment and daily life
  • Internet and tech usage
  • Childhood memories and casual chatting
  • This diversity ensures the dataset is useful across multiple NLP and language understanding applications.

    Linguistic Authenticity

    Chats reflect informal, native-level Ukrainian usage with:

  • Colloquial expressions and local dialect influence
  • Domain-relevant terminology
  • Language-specific grammar, phrasing, and sentence flow
  • Inclusion of realistic details such as names, phone numbers, email addresses, locations, dates, times, local currencies, and culturally grounded references
  • Representation of different writing styles and input quirks to ensure training data realism
  • Metadata

    Every chat instance is accompanied by structured metadata, which includes:

  • Participant Age
  • Gender
  • Country/Region
  • Chat Domain
  • Chat Topic
  • Dialect
  • This metadata supports model filtering, demographic-specific evaluation, and more controlled fine-tuning workflows.

    Data Quality Assurance

    All chat records pass through a rigorous QA process to maintain consistency and accuracy:

  • Manual review for content completeness
  • Format checks for chat turns and metadata
  • Linguistic verification by native speakers
  • Removal of inappropriate or unusable samples
  • This ensures a clean, reliable dataset ready for high-performance AI model training.

    Applications

    This dataset is ideal for training and evaluating a wide range of text-based AI systems:

  • Conversational AI / Chatbots
  • Smart assistants and voicebots
  • Natural Language Understanding (NLU)
  • Text classification and clustering
  • Named entity recognition (NER)
  • Text prediction and auto-completion
  • Intent detection and response generation
  • Sentiment analysis (with additional annotation)
  • Ethical and Responsible Collection

  • Consent-Driven: All contributors provided informed, written consent.
  • Privacy-Preserved: Personally identifiable data is either anonymized or simulated.
  • Ethical Compliance: Collected in accordance with ethical AI practices and data protection guidelines.
  • FutureBeeAI Platform: All data was securely captured, reviewed, and stored through FutureBeeAI’s internal data pipeline, ensuring end-to-end control and security.
  • Bias Mitigation Strategy

    To promote fairness and reduce bias in AI training:

  • Contributors were selected from multiple Ukrainian-speaking countries
  • Balanced gender representation
  • Topic distribution covers varied cultural and social contexts
  • This allows your models to perform consistently across user groups and reduce regional skew.

    Annotation & Customization Options

    While this version does not include labeled annotations, FutureBeeAI supports customized enhancements such as:

  • Named Entity Annotations (names, emails, locations, numbers, etc.)
  • Sentiment or Intent Labels
  • Dialect Tagging
  • Topic Expansion (finance, customer support, etc.)
  • Additional metadata such as chat tone, message type, or chat purpose
  • Custom collection is available in Ukrainian or other languages on request.
  • Scalability & Continuous Expansion

    This dataset is continuously expanded with new chat data to enrich domain coverage and linguistic diversity. Custom chat data collection services are available for enterprise-grade requirements.

    Licensing

    This dataset is developed and owned by FutureBeeAI and is available under a commercial license. Custom licensing terms can be provided for academic, research, or enterprise clients.

    Use Cases

    Use of conversational chat dataset in Chatbot

    Chatbot

    Use of conversational chat dataset in Text Analytics

    Text analytics

    Use of conversational chat dataset in Text Recognition

    Text recognition

    Use of conversational chat dataset in Text Prediction

    Text prediction

    Use of conversational chat dataset in Smart Assistant

    Smart assistants

    Dataset Sample(s)

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    Dataset Details

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    Dataset type

    General domain conversational chats

    Volume

    15K+ chats

    Media type

    Text Only

    Language

    Ukrainian

    Topics

    50+

    File Details

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    Turn per Chat

    50

    Word Count

    300-700 words

    Format

    TXT, DOCS, JSON or CSV

    Annotation

    On Request

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