Bitext customer support llm chatbot training dataset

Датасет Bitext Customer Support LLM Chatbot Training Dataset предназначен для обучения моделей машинного обучения, в частности чат-ботов для автоматизированной службы поддержки клиентов. Он содержит 26 000 синтетически сгенерированных диалогов между клиентами и агентами, охватывающих 26 различных отраслей, таких как розничная торговля, телекоммуникации, финансы, здравоохранение и другие. Датасет включает данные на 27 языках, что делает его подходящим для разработки многоязычных чат-ботов.

Каждый диалог включает намерения (intents), такие как запросы информации, жалобы, запросы на возврат, техническая поддержка и т.д., а также аннотации для классификации намерений и извлечения сущностей (entity extraction). Данные структурированы для поддержки задач обработки естественного языка, таких как классификация текста, вопросы и ответы, и генерация текста. Датасет создан с использованием синтетических методов, что обеспечивает высокую чистоту данных и отсутствие персональной информации, что делает его безопасным для использования в коммерческих и исследовательских целях.

Датасет предоставляется в формате JSON, где каждая запись содержит текст запроса клиента, ответ агента, метки намерений и сущностей. Он может быть использован для дообучения больших языковых моделей (LLM) или для создания специализированных чат-ботов для конкретных отраслей.

Особенности:

• Поддержка 27 языков, включая английский, испанский, французский, немецкий, китайский и другие.

• Покрытие 26 отраслей, что позволяет адаптировать чат-ботов для специфических доменов.

• Синтетические данные, исключающие проблемы с конфиденциальностью.

• Аннотации для классификации намерений и извлечения сущностей.

Датасет предназначен для задач выявления намерений (Intent Detection) в сфере обслуживания клиентов. Он включает следующие характеристики:

Сценарий использования: Выявление намерений (Intent Detection).

Отрасль: Обслуживание клиентов (Customer Service).

27 намерений, распределенных по 10 категориям.

26872 пары вопросов и ответов, примерно по 1000 пар на каждое намерение.

30 типов сущностей/слотов (entity/slot types).

12 различных тегов генерации языка (language generation tags).

Категории и намерения были отобраны из коллекции Bitext, состоящей из 20 датасетов, специфичных для различных отраслей. Включены намерения, которые являются общими для всех 20 отраслей. Отрасли, представленные в датасете:

• Автомобилестроение

• Розничный банкинг

• Образование

• Мероприятия и продажа билетов

• Полевые службы

• Здравоохранение

• Гостиничный бизнес

• Страхование

• Юридические услуги

• Производство

• Потоковые медиа

• Ипотека и кредиты

• Переезд и хранение

• Недвижимость и строительство

• Сети ресторанов и баров

• Розничная торговля и электронная коммерция

• Телекоммуникации

• Путешествия

• Коммунальные услуги

• Управление активами

Датасет подходит для обучения чат-ботов на основе языковых моделей, для задач обработки естественного языка, таких как классификация намерений, извлечение сущностей и генерация ответов.

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, switch_account

• CANCELLATION_FEE: check_cancellation_fee

• DELIVERY: delivery_options

• FEEDBACK: complaint, review

• INVOICE: check_invoice, get_invoice

• NEWSLETTER: newsletter_subscription

• ORDER: cancel_order, change_order, place_order

• PAYMENT: check_payment_methods, payment_issue

• REFUND: check_refund_policy, track_refund

• SHIPPING_ADDRESS: change_shipping_address, set_up_shipping_address

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:

  • Intent: delivery_options

• {{Delivery Country}}, typically present in:

  • Intents: check_payment_methods, check_refund_policy, delivery_options, review, switch_account

• {{Salutation}}, typically present in:

  • Intents: cancel_order, check_payment_methods, check_refund_policy, create_account, delete_account, delivery_options, get_refund, recover_password, review, set_up_shipping_address, switch_account, track_refund

• {{Client First Name}}, typically present in:

  • Intents: check_invoice, get_invoice

• {{Client Last Name}}, typically present in:

  • Intents: check_invoice, create_account, get_invoice

• {{Customer Support Phone Number}}, typically present in:

  • Intents: change_shipping_address, contact_customer_service, contact_human_agent, payment_issue

• {{Customer Support Email}}, typically present in:

  • Intents: cancel_order, change_shipping_address, check_invoice, check_refund_policy, complaint, contact_customer_service, contact_human_agent, get_invoice, get_refund, newsletter_subscription, payment_issue, recover_password, registration_problems, review, set_up_shipping_address, switch_account

• {{Live Chat Support}}, typically present in:

  • Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, recover_password, registration_problems, review, set_up_shipping_address, switch_account, track_order

• {{Website URL}}, typically present in:

  • Intents: check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, delete_account, delivery_options, get_refund, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, review, switch_account

• {{Upgrade Account}}, typically present in:

  • Intents: create_account, edit_account, switch_account

• {{Account Type}}, typically present in:

  • Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, 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

• {{Account Category}}, typically present in:

  • Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, 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

• {{Account Change}}, typically present in:

  • Intent: switch_account

• {{Program}}, typically present in:

  • Intent: place_order

• {{Refund Amount}}, typically present in:

  • Intent: track_refund

• {{Money Amount}}, typically present in:

  • Intents: check_refund_policy, complaint, get_refund, track_refund

• {{Store Location}}, typically present in:

  • Intents: complaint, delivery_options, place_order

Language Generation Tags

The dataset contains tags that reflect how language varies/changes across different linguistic phenomena like colloquial or offensive language. So if an utterance for intent “cancel_order” contains the “COLLOQUIAL” tag, the utterance will express an informal language variation like: “can u cancel my order”.

These tags indicate the type of language variation that the entry expresses. When associated to each entry, they allow Conversational Designers to customize training datasets to different user profiles with different uses of language. Through these tags, many different datasets can be created to make the resulting assistant more accurate and robust. A bot that sells sneakers should be mainly targeted to younger population that use a more colloquial language; while a classical retail banking bot should be able to handle more formal or polite language. The dataset also reflects commonly occurring linguistic phenomena of real-life virtual assistant, such as spelling mistakes, run-on words, punctuation errors…

The dataset contains tagging for all relevant linguistic phenomena that can be used to customize the dataset for different user profiles.

Tags for Lexical variation

M - Morphological variation: inflectional and derivational “is my SIM card active”, “is my SIM card activated”

L - Semantic variations: synonyms, use of hyphens, compounding… “what’s my billing date", “what’s my anniversary date”

Tags for Syntactic structure variation

B - Basic syntactic structure: “activate my SIM card”, “I need to activate my SIM card”

I - Interrogative structure “can you activate my SIM card?”, “how do I activate my SIM card?”

C - Coordinated syntactic structure “I have a new SIM card, what do I need to do to activate it?”

N - Negation “I do not want this item, where to cancel my order?”

Tags for language register variations

P - Politeness variation “could you help me activate my SIM card, please?”

Q - Colloquial variation “can u activ8 my SIM?”

W - Offensive language “I want to talk to a f*&%*g agent”

Tags for stylistic variations

K - Keyword mode "activate SIM", "new SIM"

E - Use of abbreviations: “I'm / I am interested in getting a new SIM”

Z - Errors and Typos: spelling issues, wrong punctuation… “how can i activaet my card”

Other tags not in use in this Dataset

D - Indirect speech “ask my agent to activate my SIM card”

G - Regional variations US English vs UK English: "truck" vs "lorry" France French vs Canadian French: "tchatter" vs "clavarder"

R - Respect structures - Language-dependent variations English: "may" vs "can…" French: "tu" vs "vous..." Spanish: "tú" vs "usted..."

Y - Code switching “activer ma SIM card”