Bitext - Travel 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 [Travel] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset.

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...

This JSON file contains a collection of conversational AI intents designed to motivate and interact with users. The intents cover various topics, including greetings, weather inquiries, hobbies, music, movies, farewells, informal and formal questions, math operations and formulas, prime numbers, geometry concepts, math puzzles, and even a Shakespearean poem.

The additional intents related to consolidating people and motivating them have been included to provide users with uplifting and encouraging responses. These intents aim to offer support during challenging times, foster teamwork, and provide words of motivation and inspiration to users seeking guidance and encouragement.

The JSON structure is organized into individual intent objects, each containing a tag to identify the intent, a set of patterns representing user inputs, and corresponding responses provided by the AI model. This dataset can be used to train a conversational AI system to engage in positive interactions with users and offer motivational messages.

The University Chatbot Dataset contains 38 intents covering general university-related inquiries, designed to train, fine-tune, and evaluate conversational AI models in the education sector.

Bitext - Retail Banking 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 Banking] sector can be easily achieved using our two-step approach to LLM Fine-Tuning.… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset.

We’ve developed another annotated dataset designed specifically for conversational AI and companion AI model training.

Watch: How To Use The Dataset

What you have here on Kaggle is our free sample - Think Salon Kitty meets AI

The 'Time Waster Identification & Retreat Model Dataset', enables AI handler agents to detect when users are likely to churn—saving valuable tokens and preventing wasted compute cycles in conversational models.

This batch has 167 entries annotated for sentiment, intent, user risk flagging (via behavioural tracking), user Recovery Potential per statement; among others. This dataset is designed to be a niche micro dataset for a specific use case: Time Waster Identification and Retreat.

👉 Buy the updated version: https://lifebricksglobal.gumroad.com/l/Time-WasterDetection-Dataset

This dataset is perfect for:

• Fine-tuning LLM routing logic
• Building intelligent AI agents for customer engagement
• Companion AI training + moderation modelling
• This is part of a broader series of human-agent interaction datasets we are releasing under our independent data licensing program.

It is designed for AI researchers and developers building:

• Conversational AI agents
• Companion AI models
• Human-agent interaction simulators
• LLM routing optimization models

Use case:

• Conversational AI
• Companion AI
• Defence & Aerospace
• Customer Support AI
• Gaming / Virtual Worlds
• LLM Safety Research
• AI Orchestration Platforms

This batch has 167 entries annotated for sentiment, intent, user risk flagging (via behavioural tracking), user Recovery Potential per statement; among others. This dataset is designed to be a niche micro dataset for a specific use case: Time Waster Identification and Retreat.

👉 Good for teams working on conversational AI, companion AI, fraud detectors and those integrating routing logic for voice/chat agents

👉 Buy the updated version: https://lifebricksglobal.gumroad.com/l/Time-WasterDetection-Dataset

Contact us on LinkedIn: Life Bricks Global.

License:

This dataset is provided under a custom license. By using the dataset, you agree to the following terms:

Usage: You are allowed to use the dataset for non-commercial purposes, including research, development, and machine learning model training.

Modification: You may modify the dataset for your own use.

Redistribution: Redistribution of the dataset in its original or modified form is not allowed without permission.

Attribution: Proper attribution must be given when using or referencing this dataset.

No Warranty: The dataset is provided "as-is" without any warranties, express or implied, regarding its accuracy, completeness, or fitness for a particular purpose.

Bitext Sample Pre-built Customer Support Dataset for English

Overview

This dataset contains example utterances and their corresponding intents from the Customer Support domain. The data can be used to train intent recognition models Natural Language Understanding (NLU) platforms.

The dataset covers the "Customer Support" domain and includes 27 intents grouped in 11 categories. These intents have been selected from Bitext's collection of 20 domain-specific datasets (banking, retail, utilities...), keeping the intents that are common across domains. See below for a full list of categories and intents.

Utterances

The dataset contains over 20,000 utterances, with a varying number of utterances per intent. These utterances have been extracted from a larger dataset of 288,000 utterances (approx. 10,000 per intent), including language register variations such as politeness, colloquial, swearing, indirect style... To select the utterances, we use stratified sampling to generate a dataset with a general user language register profile.

The dataset also reflects commonly ocurring linguistic phenomena of real-life chatbots, such as: - spelling mistakes - run-on words - missing punctuation

Contents

Each entry in the dataset contains an example utterance from the Customer Support domain, along with its corresponding intent, category and additional linguistic information. Each line contains the following four fields: - flags: the applicable linguistic flags - utterance: an example user utterance - category: the high-level intent category - intent: the intent corresponding to the user utterance

Linguistic flags

The dataset contains annotations for linguistic phenomena, which can be used to adapt bot training to different user language profiles. These flags are: B - Basic syntactic structure S - Syntactic structure L - Lexical variation (synonyms) M - Morphological variation (plurals, tenses…) I - Interrogative structure C - Complex/Coordinated syntactic structure P - Politeness variation Q - Colloquial variation W - Offensive language E - Expanded abbreviations (I'm -> I am, I'd -> I would…) D - Indirect speech (ask an agent to…) Z - Noise (spelling, punctuation…)

These phenomena make the training dataset more effective and make bots more accurate and robust.

Categories and Intents

The intent categories covered by the dataset are: ACCOUNT CANCELLATION_FEE CONTACT DELIVERY FEEDBACK INVOICES NEWSLETTER ORDER PAYMENT REFUNDS SHIPPING

The intents covered by the dataset are: cancel_order complaint contact_customer_service contact_human_agent create_account change_order change_shipping_address check_cancellation_fee check_invoices check_payment_methods check_refund_policy delete_account delivery_options delivery_period edit_account get_invoice get_refund newsletter_subscription payment_issue place_order recover_password registration_problems review set_up_shipping_address switch_account track_order track_refund

(c) Bitext Innovations, 2020

This dataset contains simple chatbot-style conversations focused primarily on greetings and basic introductory exchanges, such as:

"Hi" → "Hello 👋"

"Hello" → "Hi😊"

The dataset is useful for training lightweight AI chatbots or testing conversational flows.

📊 Features: prompt: (e.g., "Hi", "Hello")

response: (e.g., "Hello", "Hi")

Format: JSON

Language: English

💡 Use Cases: Basic chatbot training

🛠️ Example Entries: prompt response Hi Hello 👋 Hello Hi 😊

📌 License: This dataset is provided under the CCO: Public Domain License.

✨ Notes: This dataset is intentionally kept simple and lightweight to help in testing chatbot behaviors or creating quick prototypes.

Bitext - Restaurants 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 [restaurants] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-restaurants-llm-chatbot-training-dataset.

Dataset

This dataset was created by Abhishek Srivastava

Contents

Overview This dataset is designed to train and fine-tune chatbot models by mapping user queries (patterns) to predefined intents (tags) and generating contextually accurate responses. Each tag represents a unique conversational intent or topic (e.g., "climate_change," "crypto_regulation," "quantum_computing"), accompanied by multiple paraphrased user prompts (patterns) and a detailed, informative response. Ideal for building intent classification systems, dialogue management, or generative AI models.

{ "intents": [ { "tag": "tag_name", "patterns": ["user query 1", "user query 2", ...], "responses": ["detailed answer"] }, ... ] }

Possible Uses Intent Classification: Train models to categorize user inputs into predefined tags.

Response Generation: Fine-tune generative models (GPT, BERT) to produce context-aware answers.

Educational Chatbots: Power QA systems for topics like science, history, or technology.

Customer Support: Automate responses for FAQs or policy explanations.

Compatibility Frameworks: TensorFlow, PyTorch, spaCy, Rasa, Hugging Face Transformers.

Use Cases: Virtual assistants, customer service bots, trivia apps, educational tools.

Snapshot img

Chatbot Market Size 2025-2029

The chatbot market size is forecast to increase by USD 9.63 billion, at a CAGR of 42.9% between 2024 and 2029. Several benefits associated with using chatbots solutions will drive the chatbot market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 37% growth during the forecast period.
By End-user - Retail segment was valued at USD 210.60 billion in 2023
By Product - Solutions segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 billion
Market Future Opportunities: USD 9.63 billion 
CAGR : 42.9%
APAC: Largest market in 2023

Market Summary

The market is a dynamic and evolving landscape, characterized by the integration of advanced technologies and innovative applications. Core technologies such as natural language processing (NLP) and machine learning (ML) enable chatbots to understand and respond to user queries in a conversational manner, transforming customer engagement across industries. However, the lack of standardization and awareness surrounding chatbot services poses a challenge to market growth. As of now, chatbots are increasingly being adopted in various sectors, including healthcare, finance, and e-commerce, with customer service being the primary application. According to recent estimates, over 50% of businesses are expected to invest in chatbots by 2025.
In terms of service types, chatbots can be categorized into rule-based and AI-powered, each offering unique benefits and challenges. Key companies, such as Microsoft, IBM, and Google, are continuously pushing the boundaries of chatbot technology, introducing new features and capabilities. Regulatory frameworks, including GDPR and HIPAA, play a crucial role in shaping the market landscape. Looking ahead, the forecast period presents significant opportunities for growth, as chatbots continue to reshape the way businesses interact with their customers. Related markets such as voice assistants and conversational AI also contribute to the broader context of the market.
Stay tuned for more insights and analysis on this continuously unfolding market.

What will be the Size of the Chatbot Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Chatbot Market Segmented and what are the key trends of market segmentation?

The chatbot industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail
  BFSI
  Government
  Travel and hospitality
  Others


Product

  Solutions
  Services


Deployment

  Cloud-Based
  On-Premise
  Hybrid


Application

  Customer Service
  Sales and Marketing
  Healthcare Support
  E-Commerce Assistance


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with adoption in various sectors escalating at a remarkable pace. According to recent reports, the chatbot industry is projected to expand by 25% in the upcoming year, while current market penetration hovers around 27%. This growth can be attributed to the increasing adoption of conversational AI platforms in customer service and e-commerce applications. Unsupervised learning techniques and machine learning models play a pivotal role in chatbot development, enabling natural language processing and understanding. Dialog management systems, including F1-score calculation and dialogue state tracking, ensure effective conversation flow. Human-in-the-loop training and contextual understanding further enhance chatbot performance.

Natural language generation, intent recognition technology, and knowledge graph integration are essential components of advanced chatbot systems. Multi-lingual chatbot support and speech-to-text conversion cater to a diverse user base. Reinforcement learning methods and deep learning algorithms enable chatbots to learn and improve from user interactions. Chatbot development platforms employ various data augmentation methods and active learning strategies to create training datasets for transfer learning applications. Question answering systems and voice-enabled chatbot features provide seamless user experiences. Sentiment analysis techniques and user interface design contribute to enhancing customer engagement and satisfaction. Conversational flow design and response generation models ensure e

Chatbot are used by almost every tech based company and become trending these days I decided build chatbot so i find this, to get good hands on experience how to build chatbot this dataset is perfect

Contribute to this dataset and enjoy Kaggling !!!!!!!!!!!!!

This dataset provides detailed, synthetic healthcare chatbot conversations with annotated intent labels, message sequencing, and extracted entities. Designed for training and evaluating conversational AI, it supports intent classification, dialogue modeling, and entity recognition in healthcare virtual assistants. The dataset enables robust analysis of user-bot interactions for improved patient engagement and automation.

Get a high-quality chatbot dataset for AI/ML models in Hospitality Sector. Ideal for NLP training, improving chatbot responses, and enhancing conversational AI.

Get a high-quality chatbot dataset for AI/ML models in BFSI Sector. Train with diverse conversational data for accurate, efficient machine learning applications

Introduction

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.

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 English 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:

Given the importance of conversation practice in language learning, chatbots, especially ChatGPT, have attracted considerable attention for their ability to converse with learners using natural language. This review contributes to the literature by examining the currently unclear overall effect of using chatbots on language learning performance and comprehensively identifying important study characteristics that affect the overall effectiveness. We meta-analyzed 70 effect sizes from 28 studies, using robust variance estimation. The effects were assessed based on 18 study characteristics about learners, chatbots, learning objectives, context, communication/interaction, and methodological and pedagogical designs. Results indicated that using chatbots produced a positive overall effect on language learning performance (g = 0.486), compared to non-chatbot conditions. Moreover, four characteristics (i.e., educational level, language level, interface design, and interaction capability) affected the overall effectiveness. In an in-depth discussion on how the 18 characteristics are related to the effectiveness, future implications for practice and research are presented.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

Airport Digital Twin Chatbot Training Market Outlook

According to our latest research, the global Airport Digital Twin Chatbot Training market size in 2024 stands at USD 1.13 billion, reflecting the rapid adoption of advanced digital solutions in the aviation sector. The market is expected to witness a robust growth trajectory, registering a CAGR of 18.7% from 2025 to 2033. By 2033, the market is projected to reach USD 5.86 billion, driven by increasing investments in airport modernization, the proliferation of artificial intelligence (AI) technologies, and the pressing need for enhanced passenger experience and operational efficiency.

The key growth factor propelling the Airport Digital Twin Chatbot Training market is the escalating demand for real-time data-driven decision-making in airport operations. As airports grapple with growing passenger volumes and heightened security requirements, the integration of digital twin technology with AI-powered chatbots enables seamless simulation, monitoring, and management of complex airport environments. This convergence empowers stakeholders to predict potential bottlenecks, optimize resource allocation, and proactively address operational disruptions. Furthermore, the ability of digital twin chatbots to learn and adapt through continuous training ensures that airports remain agile and responsive to evolving operational challenges, thereby fostering a culture of innovation and continuous improvement.

Another significant driver is the imperative to elevate the passenger experience amid intensifying competition among airports globally. Digital twin chatbots, trained on vast datasets encompassing passenger behavior, flight schedules, and facility management, can deliver personalized assistance, streamline check-in processes, and provide real-time updates, thereby reducing wait times and enhancing overall satisfaction. The adoption of these technologies not only improves passenger engagement but also contributes to brand differentiation for airports and airlines. As customer expectations for seamless, contactless, and efficient services continue to rise, the deployment of intelligent chatbot solutions is becoming a strategic priority for airport operators aiming to secure a competitive edge.

The market’s expansion is further fueled by regulatory mandates and industry initiatives aimed at strengthening airport security and sustainability. Digital twin chatbots play a pivotal role in simulating security scenarios, monitoring compliance, and facilitating rapid response to incidents. Additionally, they support predictive maintenance and energy management, aligning with global efforts to reduce the carbon footprint of aviation infrastructure. The synergy between regulatory compliance, operational resilience, and environmental stewardship is accelerating the adoption of digital twin chatbot training solutions across airports of varying scales and complexities.

From a regional perspective, North America currently leads the market, underpinned by substantial investments in airport infrastructure, a mature digital ecosystem, and the presence of leading technology providers. However, Asia Pacific is poised for the fastest growth, driven by the surge in air travel, large-scale airport development projects, and government initiatives promoting smart airport technologies. Europe remains a significant contributor, with a focus on sustainability and passenger-centric innovations. Meanwhile, the Middle East & Africa and Latin America are emerging as promising markets, supported by strategic investments in aviation and digital transformation efforts.

Component Analysis

The Component segment of the Airport Digital Twin Chatbot Training market is bifurcated into Software and Services. The software sub-segment encompasses the core digital twin platforms, AI-powered chatbot engines, and integrated analytics tools that form the backbone of intelligent airport operations. These solutions are des