datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mo-customer-support-tweets-945k
Customer Support on Twitter Dataset 945k
Dataset Description
Context
This dataset provides a large corpus of real-world English conversations between consumers and customer support agents on Twitter, designed to drive innovation in Natural Language Processing (NLP) by providing data that better matches the actual language used in contemporary customer support interactions.
Content
Initially, the data included complex threads of conversations… See the full description on the dataset page: https://huggingface.co/datasets/MohammadOthman/mo-customer-support-tweets-945k.SPADE-customer-service-dialogue
SPADE: Structured Prompting Augmentation for Dialogue Enhancement in Machine-Generated Text Detection
Paper | Code
SPADE contains a repository of customer service line synthetic user dialogues with goals, augmented from MultiWOZ 2.1 using GPT-3.5 and Llama 70B.
The datasets are intended for training and evaluating machine generated text detectors in dialogue settings.
There are 15 English datasets generated using 5 different augmentation methods and 2 large language models… See the full description on the dataset page: https://huggingface.co/datasets/AngieYYF/SPADE-customer-service-dialogue.brazilian-customer-service-conversations
Brazilian Customer Service Conversations
Dataset de conversas de atendimento ao cliente em portugues brasileiro (PT-BR).
De um like me apoie em manter esse dataset!
Descricao
Conversas sinteticas de alta qualidade simulando interacoes reais entre clientes e atendentes em diversos setores da economia brasileira. Util para treinar e avaliar modelos de:
Chatbots de atendimento
Classificacao de intencao (intent classification)
Analise de sentimento em conversas
Geracao de… See the full description on the dataset page: https://huggingface.co/datasets/RichardSakaguchiMS/brazilian-customer-service-conversations.customer-ticket-resolution
CUSTOMER_TICKET_RESOLUTION
A preference dataset for CUSTOMER_TICKET_RESOLUTION, harvested from real, human-labelled sources and curated by an automated harvesting harness with an LLM quality gate.
Format
Standard preference / DPO schema — each row:
column
meaning
prompt
the request (originally ticket)
chosen
the human-preferred response
rejected
a worse response to the same prompt
source
the dataset/URL the row was harvested from… See the full description on the dataset page: https://huggingface.co/datasets/316usman/customer-ticket-resolution.customer-service-sft-50k
Customer Service SFT (50K)
50,000 ShareGPT-format customer service conversations across 8 industries and 18 issue types. Each conversation includes a system prompt establishing the agent's role, authority limits, and policy constraints — training models to operate within defined boundaries while resolving issues empathetically and effectively.
Motivation
Customer service is one of the highest-volume LLM deployment contexts. Models need to balance:
Empathy with… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/customer-service-sft-50k.customer-support-th-26.9k
customer-support-th-26.9k
Thai customer-support instruction dataset (~26.9k examples). Thai-localized version of the Bitext customer-support dataset — instruction templates, intent/category labels, and response templates in Thai.
Format
Field
Description
instruction
Customer question template in Thai (may contain {{placeholders}})
response
Support response template in Thai
category
Coarse category (e.g. ORDER)
intent
Fine-grained intent (e.g.… See the full description on the dataset page: https://huggingface.co/datasets/Porameht/customer-support-th-26.9k.customer-support
Description
Topic: Customer Support Interactions
Domains: E-commerce, Telecommunications, Software Services
Number of Entries: 1,000
Dataset Type: Raw Dataset
Model Used: Meta Llama4 Maverick 17B Instruct V1
Language: English
customer-support-chatml
Customer Support ChatML Dataset
This dataset is a curated and preprocessed version of the
Bitext Customer Support Dataset.
Dataset Description
The dataset has been converted to ChatML format for fine-tuning conversational AI models.
Format
Each example contains:
text: The complete conversation in ChatML format
messages: JSON string of the conversation as a list of messages
instruction: The original user query
response: The original assistant response… See the full description on the dataset page: https://huggingface.co/datasets/Shivam271089/customer-support-chatml.Customer-Churn-Dataset-V2
Customer Churn Conversation Dataset - 500 (Benchmark-Anchored)
Free 500-record sample. Licensed CC BY-NC 4.0. Commercial use requires a license.
The generator is the product
This sample was produced by our synthetic customer-churn dialogue generator. The generator is what we license: it produces a labeled 10,000-record dataset anchored to published subscription-industry benchmarks, with a cleaner and refiner pipeline built in. Real churn conversations are locked… See the full description on the dataset page: https://huggingface.co/datasets/ConsumerDividends/Customer-Churn-Dataset-V2.CustomerPersonas
Synthetic Customer Experience Persona
Overview
The Synthetic Customer Experience Persona Dataset is a large-scale synthetic corpus of customer service personas, designed to aid in the development and evaluation of AI models for customer service applications. Inspired by Tencent AI Labs' Persona Hub, this dataset provides a diverse array of customer profiles across multiple industries.
Dataset Statistics
Total Personas: 250,000
Industries Covered: 6 (Retail… See the full description on the dataset page: https://huggingface.co/datasets/CordwainerSmith/CustomerPersonas.algerian-darija-customer-service-sample
Algerian Darija customer messages — stratified sample
500 spontaneous Algerian Darija messages, written by real customers, drawn from a
first-party corpus of 869,166 customer messages. Every message here is unique
after normalization, de-identified, and typed by a human — nothing elicited, translated, scraped or
generated.
Algerian Darija (ISO 639-3 arq) is spoken by around 45 million people and is one of the worst-covered
varieties in current language models. For scale: PADIC… See the full description on the dataset page: https://huggingface.co/datasets/dzcorpora/algerian-darija-customer-service-sample.Frames-synthetic-customer-service-dialogue
Frames Synthetic Customer Service Dialogues
This contains a repository of customer service line synthetic user dialogues with goals, augmented from Frames using Qwen2.5-32B.
The datasets are intended for training and evaluating machine generated text detectors in dialogue settings.
Dataset Structure
The datasets are of parquet file format and contain the following columns:
Column
Description
dia_no
Unique ID for each dialogue. Dialogues with the same ID… See the full description on the dataset page: https://huggingface.co/datasets/AngieYYF/Frames-synthetic-customer-service-dialogue.customer-transcript-long-dialog
Customer Transcript Long Dialogue
Curated customer-support and transcript-analytics prompts mapped to a single fixed "analyze this transcript -> compact JSON" prompt, for benchmarking batched offline LLM inference on realistic workloads.
Motivation and intended use
This dataset provides a realistic transcript-analytics workload for batched offline-inference experiments: throughput benchmarking and predicted-vs-observed throughput validation. Rows carry token… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-systalyze/customer-transcript-long-dialog.synthetic-customer-support-sft-smoke
Synthetic customer-support SFT dataset
Synthetically generated with HuggingFaceTB/SmolLM2-135M-Instruct from seeded scenario prompts (18 products x 16 issue types x 5 customer tones).
Rows: 4 kept after validation (4 completions failed parsing and were dropped)
Format: messages column (system/user/assistant), ready for TRL SFTTrainer
Metadata: category (issue type), tone (customer tone)
Seed: 42
Generated on 2026-09-02. Model-generated content: review before production use.
customer-transcript-analytics
Customer Transcript Analytics
Curated customer-support and meeting transcripts mapped to a single fixed
"analyze this transcript → compact JSON" prompt, for benchmarking batched
offline LLM inference on realistic workloads.
Motivation and intended use
This dataset provides a realistic transcript-analytics workload for batched
offline-inference experiments: throughput benchmarking and
predicted-vs-observed throughput validation. Rows range from short support
chats… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-systalyze/customer-transcript-analytics.CallAgentAI-Hinglish-Customer-Service
CallAgent AI: Hinglish Business Conversations Dataset
This dataset contains synthetic, high-quality "Hinglish" (Hindi + English code-switching) customer service interactions. It was generated by CallAgent AI (callagentai.in) — India's leading AI voice receptionist platform designed specifically for Indian SMBs.
Why this dataset exists
Global voice AI models often fail to capture the unique nuances of Indian business calls, which heavily rely on fluid language… See the full description on the dataset page: https://huggingface.co/datasets/Ghanashyaam/CallAgentAI-Hinglish-Customer-Service.customer_service_chatbotnaija-customer-call-code-switch
Naija Customer-Call Code-Switch Corpus (Orinode-CCS)
Hand-written customer-service sentences with natural code-switching between Nigerian English and three indigenous Nigerian languages — Hausa, Yoruba, and Igbo. Covers 30+ business sectors typical of real customer-service calls in Nigeria.
Released by Orinode under CC-BY 4.0 to support research on multilingual ASR, NLU, and conversational AI for low-resource African languages.
Why this dataset exists
Global voice-AI… See the full description on the dataset page: https://huggingface.co/datasets/Orinode/naija-customer-call-code-switch.Customer-service-tickets-qwen-qa
Customer Support Tickets QA (English) — Qwen SFT Dataset
This dataset is formatted for supervised fine-tuning (SFT) of Qwen-style chat models on customer support email tasks. source dataset: Tobi-Bueck/customer-support-tickets
It is designed for training models to read a customer ticket, understand its context, and generate an appropriate support response. Depending on the prompt design, the same data can also support auxiliary tasks such as queue prediction, priority prediction… See the full description on the dataset page: https://huggingface.co/datasets/W-L/Customer-service-tickets-qwen-qa.sea-ecommerce-customer-support-sample
SEA Multilingual E-commerce Customer Support Sample
This public sample contains 1,000 synthetic, AI-generated customer-support
conversations for Southeast Asian e-commerce scenarios.
Languages
English
Chinese
Malay
Indonesian
Formats
CSV
JSONL
Intended Use
Use this sample for inspection, evaluation, prototyping, multilingual testing,
and intent-classification experiments.
Important Limitations
This is synthetic… See the full description on the dataset page: https://huggingface.co/datasets/nwchang/sea-ecommerce-customer-support-sample.customer_support_auto_completioncustomer-support-dpo-100k
Customer Support DPO 100K
A synthetic Direct Preference Optimization (DPO) dataset of 100,000 customer support interactions with chosen (high-quality) and rejected (poor-quality) response pairs. Designed to train AI models to provide genuinely helpful, specific, and empathetic customer support.
Dataset Description
This dataset covers 23 real-world customer support scenarios across B2B and B2C contexts. Each record includes a customer message, a high-quality chosen… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/customer-support-dpo-100k.multireward-grpo-fintech-customer-comms
Multi-Reward GRPO — Synthetic Fintech Customer Communications
Synthetic multi-turn customer-service conversations for a fictional bank
("Bank of XYZ"), generated for the empirical Section of "Conditioned
Multi-Reward Advantage Estimation: A Finite-Sample Analysis".
Each conversation ends with m parallel sampled bot replies, each scored
on three verifiable reward channels designed for fintech customer service.
This is the multi-reward GRPO group structure on a real generation… See the full description on the dataset page: https://huggingface.co/datasets/eagle0504/multireward-grpo-fintech-customer-comms.customer-transcript-short-control
Customer Transcript Short Control
Curated customer-support and transcript-analytics prompts mapped to a single fixed "analyze this transcript -> compact JSON" prompt, for benchmarking batched offline LLM inference on realistic workloads.
Motivation and intended use
This dataset provides a realistic transcript-analytics workload for batched offline-inference experiments: throughput benchmarking and predicted-vs-observed throughput validation. Rows carry token… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-systalyze/customer-transcript-short-control.kazakh-customer-support-qa
Dataset Card for kazakh-customer-support-qa
Maintained by: Mäñgi ÜTM (mangi-llm)
Dataset Summary
kazakh-customer-support-qa is a small, hand-curated question–answer dataset in the Kazakh language, built to represent realistic customer-support conversations across several industries (banking, telecom, retail/service centers, sales, and general support). Each record pairs a short customer question with a concise, policy-safe answer, and many answers include… See the full description on the dataset page: https://huggingface.co/datasets/mangi-llm/kazakh-customer-support-qa.spotless-customer-service-training
Spotless Bin Co Customer Service Training Data
Training data for a customer service AI model for Spotless Bin Co, a residential trash can cleaning service.
Dataset Description
This dataset contains 8,776 conversational examples across 5 categories:
Category
Count
Description
FAQs
1,951
Frequently asked questions
Service
1,925
Service explanation dialogues
Objections
1,925
Objection handling examples
Booking
1,975
Booking flow conversations
Brand
1,000… See the full description on the dataset page: https://huggingface.co/datasets/rileyseaburg/spotless-customer-service-training.uzbek-customer-support-dialogs
Uzbek Customer Support Dialogs 🇺🇿
A high-quality dataset of 990 customer support conversations in Uzbek (Latin script), designed for training and fine-tuning conversational AI models.
This is one of the first large-scale customer support datasets in Uzbek, created to address the gap of low-resource NLP for Central Asian languages.
📋 Dataset Description
990 conversational dialogs in natural Uzbek (Latin script)
11 customer support categories: Order, Shipping, Cancel… See the full description on the dataset page: https://huggingface.co/datasets/AsrorAsr/uzbek-customer-support-dialogs.synth-customer-support-expanded-R
Expanded E-Commerce & Subscription Customer Support Dataset
Dataset Summary
This high-quality synthetic dataset contains 438 realistic customer support interactions focused on e-commerce, shipping, delivery, and subscription management. It was created to provide edge-case scenarios and varied support policies (like Hazmat battery returns, subscription cancellations, tracking loops, and incorrect SKU deliveries).
This dataset is ideal for Supervised Fine-Tuning (SFT) or… See the full description on the dataset page: https://huggingface.co/datasets/KazKozDev/synth-customer-support-expanded-R.customer-transcript-holdout-eval
Customer Transcript Holdout Eval
Curated customer-support and transcript-analytics prompts mapped to a single fixed "analyze this transcript -> compact JSON" prompt, for benchmarking batched offline LLM inference on realistic workloads.
Motivation and intended use
This dataset provides a realistic transcript-analytics workload for batched offline-inference experiments: throughput benchmarking and predicted-vs-observed throughput validation. Rows carry token… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-systalyze/customer-transcript-holdout-eval.TR-ECommerce-CustomerSupport-Instructions
TR E-Commerce Customer Support Instructions 🇹🇷
A high-quality Turkish E-Commerce Customer Support dataset designed for fine-tuning large language models (LLMs) on instruction-following customer support tasks.
Dataset Summary
Feature
Value
Language
Turkish (tr)
Domain
E-Commerce Customer Support
Format
Conversation (Conversational/Chat)
Chain-of-Thought (CoT)
✅ Natural paragraph reasoning (thinking field)
Total Categories
20
Total Rows
186… See the full description on the dataset page: https://huggingface.co/datasets/Mer1Alii/TR-ECommerce-CustomerSupport-Instructions.