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Bahasa Human-Human Chat Dataset for Conversational AI & NLP
- futurebeeai.com
wavUpdated Aug 1, 2022
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EmailClick to copy linkLink copiedCiteFutureBee AI (2022). Bahasa Human-Human Chat Dataset for Conversational AI & NLP [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/bahasa-general-domain-conversation-text-datasetwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
The Bahasa 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 Bahasa usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Bahasa 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 Bahasa 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 Bahasa speakers from the FutureBeeAI Crowd Community•Format: TXT, DOCS, JSON or CSV (customizable)•Structure: Each record contains the full chat, topic tag, and metadata blockDiversity 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 chattingThis diversity ensures the dataset is useful across multiple NLP and language understanding applications.
Linguistic Authenticity
Chats reflect informal, native-level Bahasa 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 realismMetadata
Every chat instance is accompanied by structured metadata, which includes:
•Participant Age•Gender•Country/Region•Chat Domain•Chat Topic•DialectThis 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 samplesThis 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<div - F
Hindi Human-Human Chat Dataset for Conversational AI & NLP
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Hindi Human-Human Chat Dataset for Conversational AI & NLP [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/hindi-general-domain-conversation-text-datasetwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
The Hindi 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 Hindi usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Hindi 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 Hindi 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 Hindi speakers from the FutureBeeAI Crowd Community•Format: TXT, DOCS, JSON or CSV (customizable)•Structure: Each record contains the full chat, topic tag, and metadata blockDiversity 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 chattingThis diversity ensures the dataset is useful across multiple NLP and language understanding applications.
Linguistic Authenticity
Chats reflect informal, native-level Hindi 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 realismMetadata
Every chat instance is accompanied by structured metadata, which includes:
•Participant Age•Gender•Country/Region•Chat Domain•Chat Topic•DialectThis 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 samplesThis 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<div - F
English Human-Human Chat Dataset for Conversational AI & NLP
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). English Human-Human Chat Dataset for Conversational AI & NLP [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/english-general-domain-conversation-text-datasetwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
The English 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 English usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level English 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 English 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 English speakers from the FutureBeeAI Crowd Community•Format: TXT, DOCS, JSON or CSV (customizable)•Structure: Each record contains the full chat, topic tag, and metadata blockDiversity 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 chattingThis diversity ensures the dataset is useful across multiple NLP and language understanding applications.
Linguistic Authenticity
Chats reflect informal, native-level English 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 realismMetadata
Every chat instance is accompanied by structured metadata, which includes:
•Participant Age•Gender•Country/Region•Chat Domain•Chat Topic•DialectThis 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 samplesThis 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<div 
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Introduction
The Bahasa 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 Bahasa usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Bahasa 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 Bahasa speakers. The conversations are spontaneous, context-rich, and mimic informal, real-life texting behavior.
Diversity and Domain Coverage
Conversations span a wide variety of general-domain topics to ensure comprehensive model exposure:
This diversity ensures the dataset is useful across multiple NLP and language understanding applications.
Linguistic Authenticity
Chats reflect informal, native-level Bahasa usage with:
Metadata
Every chat instance is accompanied by structured metadata, which includes:
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:
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: