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README.md

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-Bitext - Customer Service Tagged Training Dataset for LLM-based Chatbots
-========================================================================
+# Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
 
-Overview
---------
+## Overview
 
-This dataset can be used to train chatbots on Large Language Models such as GPT, Llama2 and Falcon.
+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 is parallel to our Evaluation dataset (see [Customer Service Tagged Evaluation Dataset for Intent Detection](https://github.com/bitext/customer-support-intent-detection-evaluation-dataset)). Both datasets can be used in conjunction to first train and then evaluate the accuracy provided by training. The main difference between the two datasets is the number of utterances:
+The dataset has the following specs:
 
-  - The training dataset contains 4,269 utterances (around 200 per intent) 
-  - The evaluation dataset contains around 270,000 utterances (around 10,000 per intent)
+- 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
 
-Both datasets share the rest of the specifications, so they can be used in conjunction. The training dataset has the following specs, shared with the evaluation dataset:
+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:
 
-  - Customer Service domain 
-  - 10 categories or intent groups
-  - 20 intents assigned to one of the 10 categories
-  - 6 entity/slot types
+- 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
 
-Each utterance is tagged with entities/slots when applicable. Additionally, each utterance is enriched with tags that indicate the type of language variation that the utterance expresses. Examples include:
+For a full list of verticals and its intents see [https://www.bitext.com/chatbot-verticals/](https://www.bitext.com/chatbot-verticals/).
 
-  - The tag “COLLOQUIAL” indicates that the utterance contains informal expressions: “can u close my account”
-  - The tag “INTERROGATIVE” indicates that the utterance is a question: “how do I open an account”
-  - The tag “OFFENSIVE” indicates that the utterance contains offensive expressions: “open my f****** account”
+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.
 
-There are a total of 12 tags. See below for a full list of tags, categories and intents.
+## Dataset Token Count
 
-The purpose of these tags is to customize the dataset so the trained bot can easily adapt to different user language profiles. A bot that sells sneakers and targets a younger population should be proficient in colloquial language; while a classical retail banking bot should be able to handle more formal or polite language.
+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.
 
-These intents have been selected from Bitext's collection of 20 domain-specific datasets (banking, retail, utilities...), covering the intents that are common across all 20 domains. For a full list of domains see https://www.bitext.com/chatbot-verticals/.
+## Fields of the Dataset
 
-Utterances and Linguistic Tags
-------------------------------------
-The dataset contains 4,269 training utterances, with 200 utterances per intent. It has been split into training (80%), validation (10%) and testing (10%) sets, preserving the distribution of intents and linguistic phenomena.
+Each entry in the dataset contains the following fields:
 
-The dataset also reflects commonly occurring linguistic phenomena of real-life chatbots, such as spelling mistakes, run-on words, punctuation errors…
+- 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
 
-Each entry in the dataset contains the following four fields:
+## Categories and Intents
 
-  - context: the domain to which the entry applies
-  - role: the role (virtual assistant, user...) that the model should adopt
-  - instruction: a user request from the Customer Service domain
-  - intent: the intent corresponding to the user instruction
-  - entity_type: the type of entity contained in the utterance
-  - entity_value: the entity contained in the utterance
-  - start_offset: the starting position of the entity
-  - end_offset: the ending position of the entity
-  - category: the high-level semantic category for the intent
-  - tags: different tags that reflect the types of language variations expressed in the utterance
-  - response_type: identifier for tracking the composition, and version of chatbot responses.
-  - response: an example expected response from the chatbot
+The categories and intents covered by the dataset are:
 
-The dataset contains tags that reflect different language 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”
+- 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
 
-Each utterance is enriched with one or more of these tags:
- - Register tags: colloquial language, polite language…
-    - Q - Colloquial variation
-    - P - Politeness variation
- - Content tags: offensive language, keyword language…
-    - W - Offensive language
-    - K - Keyword language
- - Linguistic tags: syntactic and morphological tags (interrogative sentence, coordinated sentence…) 
-    - B - Basic syntactic structure
-    - C - Coordinated syntactic structure
-    - I - Interrogative structure
-    - N - negation (don't, can't…)
-    - M - Morphological variation (plurals, tenses…)
-    - L - Lexical variation (synonyms)
-    - E - Expanded abbreviations (I'm -> I am, I'd -> I would…)
- - Real-life errors: spelling errors, punctuation errors…
-    - Z - Noise phenomena like spelling or punctuation errors
+## Entities
 
-These tags indicate the type of language variation that the utterance expresses. When associated to each utterance, 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 entities covered by the dataset are:
 
-You can find more details about the linguistic tags [here](TAGS.md)
+- {{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..."
 
-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
-  - NEWSLETTER: newsletter_subscription, 
-  - 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
-  
-Entities
---------
-The entities covered by the dataset are:
+Y - Code switching
+“activer ma SIM card”
 
-  - account_type
-    - Intents: create_account, delete_account, edit_account, switch_account
-    - Values: Free, Freemium, Gold, Platinum, Premium, Pro, Standard
-  - delivery_city
-    - Intent: delivery_options
-  - delivery_country
-    - Intent: delivery_options
-  - invoice_id
-    - Intents: check_invoice, get_invoice
-  - order_id
-    - Intents: cancel_order, change_order, track_order
-  - person_name
-    - Intents: check_invoice, get_invoice
-  - refund_amount
-    - Intents: get_refund, track_refund
+---
 
 (c) Bitext Innovations, 2023