datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.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.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.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.customer-transcript-source
Customer Transcript Source
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 accounting… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-systalyze/customer-transcript-source.