Text / Consumer & Lifestyle
Chatbot Training Dataset | OpenChat Corpus | 10M+ Real User–AI Multi-Turn Conversations | ChatGPT, Claude, Gemini | Training Dialogue Corpus
Advertised by providerLicensing rights not independently verifiedPrice on request
Overview
Chatbot Training Dataset | OpenChat Corpus | 10M+ Real User–AI Multi-Turn Conversations | ChatGPT, Claude, Gemini | Training Dialogue Corpus is a text dataset. Typical applications include LLM pre-training, fine-tuning & instruction data, alignment & RLHF and 1 more. Relevant sectors: Consumer & Lifestyle, Financial Services, Marketing & Advertising and Retail & E-commerce. The listing mentions timestamps. Pricing is not published and is provided on request.
Primary applications
What the listing offers
- Listing mentions timestamps.
The provider OpenChat Corpus is a curated collection of 44.9 million conversational turns between real users and leading AI assistants. Unlike raw scrapes, the corpus is processed through the provider Active Redaction to remove PII and standardize structure—so teams can train, evaluate, and analyze at enterprise scale. Why OpenChat Corpus? Real-world conversation data is messy (multiple languages, diverse intent, adversarial prompts) and often unsafe (emails, phone numbers, IPs, identifiers). This dataset preserves real usage patterns while delivering commercial-grade safety and consistency. Key Features Massive Scale: 6.8M conversations and 44.9M turns (≈45M). PII Redaction: Emails, phone numbers, IP addresses, and identifiers scrubbed via semantic tagging/redaction. Analytics-Ready Parquet: Snappy-compressed Apache Parquet, optimized for fast queries and ML pipelines. Hive Partitioning: Organized for zero-ETL ingestion (e.g., source/split/lang). Multi-Source Diversity: Harmonized from 10+ major open conversation datasets, including WildChat (4.8M), UltraChat, LMSYS Chat 1M, and Chatbot Arena. Rich Metadata: Language detection, model identifiers, toxicity signals, and role labels (user/assistant). Technical Specifications File Format: Apache Parquet (Snappy) Text Encoding: UTF-8-SIG Core Schema: conversation_id, role, text, model, pii_detected, timestamp License: the provider Commercial Data License (v1) Ideal Use Cases LLM Fine-Tuning / Instruction Tuning: Train chat models on real
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