Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

Overview

This dataset can be used to train Large Language Models such as GPT, Llama2 and Falcon, both for Fine Tuning and Domain Adaptation.

The dataset has the following specs:

• Use Case: Intent Detection
• Vertical: Customer Service
• 27 intents assigned to 10 categories
• 26872 question/answer pairs, around 1000 per intent
• 30 entity/slot types
• 12 different types of language generation tags

The categories and intents have been selected from Bitext's collection of 20 vertical-specific datasets, covering the intents that are common across all 20 verticals. The verticals are:

• Automotive, Retail Banking, Education, Events & Ticketing, Field Services, Healthcare, Hospitality, Insurance, Legal Services, Manufacturing, Media Streaming, Mortgages & Loans, Moving & Storage, Real Estate/Construction, Restaurant & Bar Chains, Retail/E-commerce, Telecommunications, Travel, Utilities, Wealth Management

For a full list of verticals and its intents see https://www.bitext.com/chatbot-verticals/.

The question/answer pairs have been generated using a hybrid methodology that uses natural texts as source text, NLP technology to extract seeds from these texts, and NLG technology to expand the seed texts. All steps in the process are curated by computational linguists.

Dataset Token Count

The dataset contains an extensive amount of text data across its 'instruction' and 'response' columns. After processing and tokenizing the dataset, we've identified a total of 3.57 million tokens. This rich set of tokens is essential for training advanced LLMs for AI Conversational, AI Generative, and Question and Answering (Q&A) models.

Fields of the Dataset

Each entry in the dataset contains the following fields:

• flags: tags (explained below in the Language Generation Tags section)
• instruction: a user request from the Customer Service domain
• category: the high-level semantic category for the intent
• intent: the intent corresponding to the user instruction
• response: an example expected response from the virtual assistant

Categories and Intents

The categories and intents covered by the dataset are:

• ACCOUNT: create_account, delete_account, edit_account, recover_password, registration_problems, switch_account
• CANCELLATION_FEE: check_cancellation_fee
• CONTACT: contact_customer_service, contact_human_agent
• DELIVERY: delivery_options, delivery_period
• FEEDBACK: complaint, review
• INVOICE: check_invoice, get_invoice
• ORDER: cancel_order, change_order, place_order, track_order
• PAYMENT: check_payment_methods, payment_issue
• REFUND: check_refund_policy, get_refund, track_refund
• SHIPPING_ADDRESS: change_shipping_address, set_up_shipping_address
• SUBSCRIPTION: newsletter_subscription

Entities

The entities covered by the dataset are:

• {{Order Number}}, typically present in:
• Intents: cancel_order, change_order, change_shipping_address, check_invoice, check_refund_policy, complaint, delivery_options, delivery_period, get_invoice, get_refund, place_order, track_order, track_refund
• {{Invoice Number}}, typically present in:
  • Intents: check_invoice, get_invoice
• {{Online Order Interaction}}, typically present in:
  • Intents: cancel_order, change_order, check_refund_policy, delivery_period, get_refund, review, track_order, track_refund
• {{Online Payment Interaction}}, typically present in:
  • Intents: cancel_order, check_payment_methods
• {{Online Navigation Step}}, typically present in:
  • Intents: complaint, delivery_options
• {{Online Customer Support Channel}}, typically present in:
  • Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, registration_problems, switch_account
• {{Profile}}, typically present in:
  • Intent: switch_account
• {{Profile Type}}, typically present in:
  • Intent: switch_account
• {{Settings}}, typically present in:
  • Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, contact_human_agent, delete_account, delivery_options, edit_account, get_invoice, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, set_up_shipping_address, switch_account, track_order, track_refund
• {{Online Company Portal Info}}, typically present in:
  • Intents: cancel_order, edit_account
• {{Date}}, typically present in:
  • Intents: check_invoice, check_refund_policy, get_refund, track_order, track_refund
• {{Date Range}}, typically present in:
  • Intents: check_cancellation_fee, check_invoice, get_invoice
• {{Shipping Cut-off Time}}, typically present in:
  • Intent: delivery_options
• {{Delivery City}}, typically present in:
  • Inten...

Introduction

The Tamil Retail & E-Commerce Chat Dataset is a large-scale, high-quality collection of over 12,000 chat conversations between customers and call center agents, focused exclusively on Retail and E-Commerce domains. Designed to reflect real-world service interactions, this dataset supports the development of robust conversational AI and NLP models tailored for Tamil-speaking audiences.

Participant & Chat Overview

Topic Diversity

This dataset spans a wide range of Retail and E-Commerce conversation types:

This diversity enables training of models that handle varied intents, scenarios, and outcomes within customer service workflows.

Language Nuance & Realism

The dataset is rich in linguistic diversity and mirrors real conversational tone and structure used in Tamil-speaking regions:

This linguistic authenticity ensures the development of culturally fluent AI models for Tamil Retail & E-Commerce use cases.

Conversational Structure & Flow

The conversations reflect natural dialogue dynamics and are organized into various types of interaction styles:

Each conversation includes common dialogue stages such as:

This dataset offers real-world customer service call transcriptions, making it an ideal resource for training conversational AI, customer-facing virtual agents, and support automation systems. All calls are sourced from authentic support interactions across 160+ industries — including retail, finance, telecom, healthcare, and logistics.

What’s included:

• Verbatim call transcriptions of customer-agent dialogues
• Human-curated summaries of each call’s topic and resolution
• Sentiment classification per call: positive, neutral, or negative
• Call duration, timestamp, location, and industry tags
• Optional: company name and issue category

Use this AI training dataset to:

• Train large language models on real customer-service language and task flow
• Improve chatbot responses with exposure to actual customer concerns
• Model complaint escalation and frustration signals
• Support summarization pipelines for QA and operations tools
• Benchmark and test conversational agents on unseen, real-case inputs

With diverse industries and naturally spoken interactions, this dataset is ideal for AI teams that require reliable, human-language training material grounded in real-world support scenarios.

Introduction

The English Telecom Chat Dataset is a comprehensive collection of over 12,000 text-based conversations between telecom customers and call center agents. This dataset captures real-world service interactions and domain-specific language in English, enabling the development of intelligent conversational AI and NLP systems tailored for the telecommunications sector.Participant & Chat Overview

Topic Diversity

This dataset spans a wide range of telecom customer service scenarios:

Language Nuance & Realism

The conversations reflect real-life telecom interactions in English, incorporating:

Conversational Flow & Structure

Conversations follow the natural flow of telecom customer service exchanges, including:

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.

Mental Health Chatbot Pairs

AI-based Tailored Support for Mental Health Conversation

By Huggingface Hub [source]

About this dataset

This dataset contains a compilation of carefully-crafted Q&A pairs which are designed to provide AI-based tailored support for mental health. These carefully chosen questions and answers offer an avenue for those looking for help to gain the assistance they need. With these pre-processed conversations, Artificial Intelligence (AI) solutions can be developed and deployed to better understand and respond appropriately to individual needs based on their input. This comprehensive dataset is crafted by experts in the mental health field, providing insightful content that will further research in this growing area. These data points will be invaluable for developing the next generation of personalized AI-based mental health chatbots capable of truly understanding what people need

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How to use the dataset

This dataset contains pre-processed Q&A pairs for AI-based tailored support for mental health. As such, it represents an excellent starting point in building a conversational model which can handle conversations about mental health issues. Here are some tips on how to use this dataset to its fullest potential:

• Understand your data: Spend time getting to know the text of the conversation between the user and the chatbot and familiarize yourself with what type of questions and answers are included in this specific dataset. This will help you better formulate queries for your own conversational model or develop new ones you can add yourself.

• Refine your language processing models: By studying the patterns in syntax, grammar, tone, voice, etc., within this conversational data set you can hone your natural language processing capabilities - such as keyword extractions or entity extraction – prior to implementing them into a larger bot system .

• Test assumptions: Have an idea of what you think may work best with a particular audience or context? See if these assumptions pan out by applying different variations of text to this dataset to see if it works before rolling out changes across other channels or programs that utilize AI/chatbot services

• Research & Analyze Results : After testing out different scenarios on real-world users by using various forms of q&a within this chatbot pair data set , analyze & record any relevant results pertaining towards understanding user behavior better through further analysis after being exposed to tailored texted conversations about Mental Health topics both passively & actively . The more information you collect here , leads us closer towards creating effective AI powered conversations that bring our desired outcomes from our customer base .

Research Ideas

• Developing a chatbot for personalized mental health advice and guidance tailored to individuals' unique needs, experiences, and struggles.
• Creating an AI-driven diagnostic system that can interpret mental health conversations and provide targeted recommendations for interventions or treatments based on clinical expertise.
• Designing an AI-powered recommendation engine to suggest relevant content such as articles, videos, or podcasts based on users’ questions or topics of discussion during their conversation with the chatbot

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | text | The text of the conversation between the user and the chatbot. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

Description This dataset consists of 400 text-only fine-tuned versions of multi-turn conversations in the English language based on 10 categories and 19 use cases. It has been generated with ethically sourced human-in-the-loop data methods and aligned with supervised fine-tuning, direct preference optimization, and reinforcement learning through human feedback.

The human-annotated data is focused on data quality and precision to enhance the generative response of models used for AI chatbots, thereby improving their recall memory and recognition ability for continued assistance.

Key Features Prompts focused on user intent and were devised using natural language processing techniques. Multi-turn prompts with up to 5 turns to enhance responsive memory of large language models for pretraining. Conversational interactions for queries related to varied aspects of writing, coding, knowledge assistance, data manipulation, reasoning, and classification.

Dataset Source Subject matter expert annotators @SoftAgeAI have annotated the data at simple and complex levels, focusing on quality factors such as content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness.

Structure & Fields The dataset is organized into different columns, which are detailed below:

P1, R1, P2, R2, P3, R3, P4, R4, P5 (object): These columns represent the sequence of prompts (P) and responses (R) within a single interaction. Each interaction can have up to 5 prompts and 5 corresponding responses, capturing the flow of a conversation. The prompts are user inputs, and the responses are the model's outputs. Use Case (object): Specifies the primary application or scenario for which the interaction is designed, such as "Q&A helper" or "Writing assistant." This classification helps in identifying the purpose of the dialogue. Type (object): Indicates the complexity of the interaction, with entries labeled as "Complex" in this dataset. This denotes that the dialogues involve more intricate and multi-layered exchanges. Category (object): Broadly categorizes the interaction type, such as "Open-ended QA" or "Writing." This provides context on the nature of the conversation, whether it is for generating creative content, providing detailed answers, or engaging in complex problem-solving. Intended Use Cases

The dataset can enhance query assistance model functioning related to shopping, coding, creative writing, travel assistance, marketing, citation, academic writing, language assistance, research topics, specialized knowledge, reasoning, and STEM-based. The dataset intends to aid generative models for e-commerce, customer assistance, marketing, education, suggestive user queries, and generic chatbots. It can pre-train large language models with supervision-based fine-tuned annotated data and for retrieval-augmented generative models. The dataset stands free of violence-based interactions that can lead to harm, conflict, discrimination, brutality, or misinformation. Potential Limitations & Biases This is a static dataset, so the information is dated May 2024.

Note If you have any questions related to our data annotation and human review services for large language model training and fine-tuning, please contact us at SoftAge Information Technology Limited at info@softage.ai.

Introduction

The English Healthcare Chat Dataset is a rich collection of over 12,000 text-based conversations between customers and call center agents, focused on real-world healthcare interactions. Designed to reflect authentic language use and domain-specific dialogue patterns, this dataset supports the development of conversational AI, chatbots, and NLP models tailored for healthcare applications in English-speaking regions.

Participant & Chat Overview

Topic Diversity

The dataset captures a wide spectrum of healthcare-related chat scenarios, ensuring comprehensive coverage for training robust AI systems:

This variety helps simulate realistic healthcare support workflows and patient-agent dynamics.

Language Diversity & Realism

This dataset reflects the natural flow of English healthcare communication and includes:

These elements ensure the dataset is contextually relevant and linguistically rich for real-world use cases.

Conversational Flow & Structure

Conversations range from simple inquiries to complex advisory sessions, including:

Each conversation typically includes these structural components:

This structured flow mirrors actual healthcare support conversations and is ideal for training advanced dialogue systems.

Data Format & Structure

Available in JSON, CSV, and TXT formats, each conversation includes:

Applications

This dataset is designed for building and improving speech recognition systems. It features transcribed customer service calls from real interactions across 160+ industries, including retail, banking, telecom, logistics, healthcare, and entertainment. Calls are natural, unscripted, and emotion-rich — making the data especially valuable for training models that must interpret speech under real-world conditions.

Each dataset entry includes:

• Full call transcription (agent + customer dialogue)
• Human-written call summary
• Overall sentiment label: positive, neutral, or negative
• Metadata: call duration, caller location (city, state, country), timestamp
• Optional: company name and industry tag

Use this dataset to:

• Train speech-to-text models on real customer language patterns. -Benchmark or evaluate speech recognition tools in support settings
• Improve voice interfaces, chatbots, and IVR systems.
• Model tone, frustration cues, and escalation behaviors
• Support LLM fine-tuning for tasks involving spoken input.s

This dataset provides your speech recognition models with exposure to genuine customer conversations, helping you build tools that can listen, understand, and act in line with how people actually speak.

The larger the volume you purchase, the lower the price will be.

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag. User consent is obtained through the "Terms of use"… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m.

Call Center Audio Recordings Dataset: 100,000+ Hours of Customer-Agent Conversations in Multiple Languages

FileMarket AI Data Labs presents an extensive call center audio recordings dataset with over 100,000 hours of customer-agent conversations across a diverse range of topics. This dataset is designed to support AI, machine learning, and speech analytics projects requiring high-quality, real-world customer interaction data. Whether you're working on speech recognition, natural language processing (NLP), sentiment analysis, or any other conversational AI task, our dataset offers the breadth and quality you need to build, train, and refine cutting-edge models.

Our dataset includes a multilingual collection of customer service interactions, recorded across various industries and service sectors. These recordings cover different call center topics such as customer support, sales and telemarketing, technical helpdesk, complaint handling, and information services, ensuring that the dataset provides rich context and variety. With support for a broad spectrum of languages including English, Spanish, French, German, Chinese, Arabic, and more, this dataset allows for training models that cater to global customer bases.

In addition to the audio recordings, our dataset includes detailed metadata such as call duration, region, language, and call type, ensuring that data is easily usable for targeted applications. All recordings are carefully annotated for speaker separation and high fidelity to meet the highest standards for audio data.

Our dataset is fully compliant with data protection and privacy regulations, offering consented and ethically sourced data. You can be assured that every data point meets the highest standards for legal compliance, making it safe for use in your commercial, academic, or research projects.

At FileMarket AI Data Labs, we offer flexibility in terms of data scaling. Whether you need a small sample or a full-scale dataset, we can cater to your requirements. We also provide sample data for evaluation to help you assess quality before committing to the full dataset. Our pricing structure is competitive, with custom pricing options available for large-scale acquisitions.

We invite you to explore this versatile dataset, which can help accelerate your AI and machine learning initiatives, whether for training conversational models, improving customer service tools, or enhancing multi-language support systems.

Introduction

The English Travel Chat Dataset is a comprehensive collection of over 12,000 text-based conversations between customers and call center agents. Focused on real-life travel and tourism interactions, this dataset captures the language, tone, and service dynamics essential for building robust conversational AI, chatbots, and NLP solutions for the travel industry in English-speaking markets.

Participant & Chat Overview

Topic Diversity

The dataset encompasses a wide range of travel and tourism use cases across both customer-initiated and agent-initiated conversations:

This variety ensures wide applicability in both sales enablement and customer support automation.

Language Diversity & Realism

Conversations are crafted to reflect the everyday language and nuances of English-speaking travelers:

These linguistic and cultural cues enable the development of context-aware, natural-sounding AI systems.

Conversational Structure & Flow

The dataset captures a variety of interaction types, including:

1. Specifications Format : 16kHz 16bit, uncompressed wav, mono channel;

Environment : quiet indoor environment, without echo;

Recording content : No preset linguistic data，dozens of topics are specified, and the speakers make dialogue under those topics while the recording is performed;

Demographics : Speakers are evenly distributed across all age groups, covering children, teenagers, middle-aged, elderly, etc.

Annotation : annotating for the transcription text, speaker identification, gender and noise symbols;

Device : Android mobile phone, iPhone;

Language : 100+ Languages;

Application scenarios : speech recognition; voiceprint recognition;

Accuracy rate : the word accuracy rate is not less than 98%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of computer vision data. These ready-to-go Machine Learning (ML) Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/speechrecog?source=Datarade

Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.

Multilingual NLP AI Market Outlook

According to our latest research, the global multilingual NLP AI market size in 2024 stands at USD 7.4 billion, with a robust CAGR of 18.2% expected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 37.6 billion, reflecting the surging demand for advanced natural language processing solutions capable of handling multiple languages across diverse industries. This remarkable growth is primarily driven by the proliferation of digital transformation initiatives, the exponential rise in global content creation, and the increasing need for real-time, accurate, and context-aware language processing in an interconnected world.

One of the most significant growth factors for the multilingual NLP AI market is the rapid globalization of businesses and the need for seamless cross-border communication. Enterprises are increasingly operating on a global scale, necessitating robust multilingual support for customer engagement, product localization, and regulatory compliance. As companies strive to enhance customer experience and expand their reach, the integration of multilingual NLP AI technologies becomes indispensable. These solutions empower organizations to bridge language barriers, deliver personalized content, and provide support in native languages, which in turn drives customer loyalty and satisfaction. Furthermore, the rise of e-commerce, international trade, and remote work environments has further amplified the necessity for scalable, accurate, and context-sensitive multilingual AI solutions.

Another critical driver fueling the expansion of the multilingual NLP AI market is the evolution of artificial intelligence and deep learning algorithms. Significant advancements in neural machine translation, contextual language modeling, and sentiment analysis have enabled NLP systems to process and understand complex linguistic nuances across numerous languages. The availability of large-scale multilingual datasets, coupled with the continuous improvement of pre-trained language models like BERT, GPT, and their multilingual variants, has significantly improved the accuracy and reliability of NLP applications. As a result, industries such as healthcare, BFSI, education, and media are leveraging these sophisticated AI-driven platforms to automate processes, extract actionable insights, and enhance decision-making capabilities in multilingual environments.

The increasing adoption of cloud computing and API-based NLP services is also a pivotal factor in the market's upward trajectory. Cloud deployment models offer unparalleled scalability, cost-effectiveness, and accessibility, allowing organizations of all sizes to implement state-of-the-art multilingual NLP solutions without substantial upfront investments in infrastructure. This democratization of AI technology has opened new avenues for small and medium enterprises (SMEs) to compete on a global scale, driving further market penetration. Additionally, the integration of NLP AI with emerging technologies such as conversational AI, voice assistants, and intelligent chatbots is driving innovation and expanding the application landscape, particularly in customer service, healthcare diagnostics, and digital marketing.

From a regional perspective, North America currently leads the multilingual NLP AI market, accounting for the largest revenue share in 2024, due to its advanced technological ecosystem, high AI adoption rates, and presence of major industry players. However, the Asia Pacific region is poised for the fastest growth during the forecast period, propelled by rapid digitalization, expanding internet penetration, and increasing investments in AI research and development. Europe also represents a significant market, driven by stringent regulatory frameworks promoting language inclusivity and a diverse linguistic landscape. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and government initiatives aimed at fostering digital transformation.

Component Analysis

The component segment of the multilingual NLP AI market is categorized into software, hardware, and services, each playing a crucial role in the overall ecosystem. Software remains the dominant segment, accounting for the largest share of the market in 2024. This dominance is attributed to the widespread deployment of NLP platforms, APIs, and pre-trained lang

Introduction

The Spanish Travel Chat Dataset is a comprehensive collection of over 10,000 text-based conversations between customers and call center agents. Focused on real-life travel and tourism interactions, this dataset captures the language, tone, and service dynamics essential for building robust conversational AI, chatbots, and NLP solutions for the travel industry in Spanish-speaking markets.

Participant & Chat Overview

Topic Diversity

The dataset encompasses a wide range of travel and tourism use cases across both customer-initiated and agent-initiated conversations:

This variety ensures wide applicability in both sales enablement and customer support automation.

Language Diversity & Realism

Conversations are crafted to reflect the everyday language and nuances of Spanish-speaking travelers:

These linguistic and cultural cues enable the development of context-aware, natural-sounding AI systems.

Conversational Structure & Flow

The dataset captures a variety of interaction types, including:

Introduction

This Russian Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Russian -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Russian speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

Introduction

The Vietnamese Healthcare Chat Dataset is a rich collection of over 10,000 text-based conversations between customers and call center agents, focused on real-world healthcare interactions. Designed to reflect authentic language use and domain-specific dialogue patterns, this dataset supports the development of conversational AI, chatbots, and NLP models tailored for healthcare applications in Vietnamese-speaking regions.

Participant & Chat Overview

Topic Diversity

The dataset captures a wide spectrum of healthcare-related chat scenarios, ensuring comprehensive coverage for training robust AI systems:

This variety helps simulate realistic healthcare support workflows and patient-agent dynamics.

Language Diversity & Realism

This dataset reflects the natural flow of Vietnamese healthcare communication and includes:

These elements ensure the dataset is contextually relevant and linguistically rich for real-world use cases.

Conversational Flow & Structure

Conversations range from simple inquiries to complex advisory sessions, including:

Each conversation typically includes these structural components:

This structured flow mirrors actual healthcare support conversations and is ideal for training advanced dialogue systems.

Data Format & Structure

Available in JSON, CSV, and TXT formats, each conversation includes:

Introduction

The Punjabi BFSI Chat Dataset is a comprehensive collection of over 12,000 text-based chat conversations between customers and call center agents. Focused on Banking, Financial Services, and Insurance (BFSI) interactions, this dataset captures real-world service dialogues, complete with domain-specific language, customer intents, and varied conversational flows.

Participant & Chat Overview

Topic Diversity

This dataset reflects the wide range of customer interactions typically encountered in the BFSI sector:

This topic spread ensures applicability across customer service automation, intent classification, and domain-specific model training.

Language Nuance & Cultural Relevance

Conversations capture natural Punjabi as spoken in BFSI contexts, incorporating:

This linguistic richness enables the training of models that can understand real-world customer queries in culturally relevant contexts.

Conversational Structure & Flow

The dataset reflects structured dialogue flow and interaction dynamics seen in BFSI customer service environments: