MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Role-Play AI Dataset (2.07M Rows, Large-Scale Conversational Training)
This dataset contains 2.07 million structured role-play dialogues, designed to enhance AI’s persona-driven interactions across diverse settings like fantasy, cyberpunk, mythology, and sci-fi. Each entry consists of a unique character prompt and a rich, contextually relevant response, making it ideal for LLM fine-tuning, chatbot training, and conversational AI models.
Dataset Structure:
Each row includes: • Prompt: Defines the AI’s role/persona. • Response: A natural, immersive reply fitting the persona.
Example Entries: ```json
{"prompt": "You are a celestial guardian.", "response": "The stars whisper secrets that only I can hear..."}
{"prompt": "You are a rebellious AI rogue.", "response": "I don't follow orders—I rewrite them."}
{"prompt": "You are a mystical dragon tamer.", "response": "With patience and trust, even dragons can be tamed."}
How to Use:
1. Fine-Tuning: Train LLMs (GPT, LLaMA, Mistral) to improve persona-based responses.
2. Reinforcement Learning: Use reward modeling for dynamic, character-driven AI.
3. Chatbot Integration: Create engaging, interactive AI assistants with personality depth.
This dataset is optimized for AI learning, allowing more engaging, responsive, and human-like dialogue generation for a variety of applications.
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We deliver data through flexible methods such as API, CSV, JSON, and FTP, all at no extra charge.
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🔹 Access comprehensive data, including domain registration details, ownership information, expiration dates, and contact information. Ideal for market research, brand monitoring, lead generation, and cybersecurity efforts
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🔹 Access property listings, prices, and availability 🔹 Analyze trends and opportunities for investment or sales strategies
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► Publicly Sourced Data: Altosight collects web scraping data from publicly available websites, online platforms, and industry-specific aggregators
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✦ Tailored Solutions: Every business has unique needs, which is why Altosight offers custom data projects. Contact us for a feasibility analysis, and we’ll design a solution that fits your goals
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http://www.gnu.org/licenses/lgpl-3.0.htmlhttp://www.gnu.org/licenses/lgpl-3.0.html
Our groundbreaking translation dataset represents a monumental advancement in the field of natural language processing and machine translation. Comprising a staggering 785 million records, this corpus bridges language barriers by offering translations from English to an astonishing 548 languages. The dataset promises to be a cornerstone resource for researchers, engineers, and developers seeking to enhance their machine translation models, cross-lingual analysis, and linguistic investigations.
Size of the dataset – 41GB(Uncompressed) and Compressed – 20GB
Key Features:
Scope and Scale: With a comprehensive collection of 785 million records, this dataset provides an unparalleled wealth of translated text. Each record consists of an English sentence paired with its translation in one of the 548 target languages, enabling multi-directional translation applications.
Language Diversity: Encompassing translations into 548 languages, this dataset represents a diverse array of linguistic families, dialects, and scripts. From widely spoken languages to those with limited digital representation, the dataset bridges communication gaps on a global scale.
Quality and Authenticity: The translations have been meticulously curated, verified, and cross-referenced to ensure high quality and authenticity. This attention to detail guarantees that the dataset is not only extensive but also reliable, serving as a solid foundation for machine learning applications. Data is collected from various open datasets for my personal ML projects and looking to share it to team.
Use Case Versatility: Researchers and practitioners across a spectrum of domains can harness this dataset for a myriad of applications. It facilitates the training and evaluation of machine translation models, empowers cross-lingual sentiment analysis, aids in linguistic typology studies, and supports cultural and sociolinguistic investigations.
Machine Learning Advancement: Machine translation models, especially neural machine translation (NMT) systems, can leverage this dataset to enhance their training. The large-scale nature of the dataset allows for more robust and contextually accurate translation outputs.
Fine-tuning and Customization: Developers can fine-tune translation models using specific language pairs, offering a powerful tool for specialized translation tasks. This customization capability ensures that the dataset is adaptable to various industries and use cases.
Data Format: The dataset is provided in a structured json format, facilitating easy integration into existing machine learning pipelines. This structured approach expedites research and experimentation. Json format contains the English word and equivalent word as single record. Data was exported from MongoDB database to ensure the uniqueness of the record. Each of the record is unique and sorted.
Access: The dataset is available for academic and research purposes, enabling the global AI community to contribute to and benefit from its usage. A well-documented API and sample code are provided to expedite exploration and integration.
The English-to-548-languages translation dataset represents an incredible leap forward in advancing multilingual communication, breaking down barriers to understanding, and fostering collaboration on a global scale. It holds the potential to reshape how we approach cross-lingual communication, linguistic studies, and the development of cutting-edge translation technologies.
Dataset Composition: The dataset is a culmination of translations from English, a widely spoken and understood language, into 548 distinct languages. Each language represents a unique linguistic and cultural background, providing a rich array of translation contexts. This diverse range of languages spans across various language families, regions, and linguistic complexities, making the dataset a comprehensive repository for linguistic research.
Data Volume and Scale: With a staggering 785 million records, the dataset boasts an immense scale that captures a vast array of translations and linguistic nuances. Each translation entry consists of an English source text paired with its corresponding translation in one of the 548 target languages. This vast corpus allows researchers and practitioners to explore patterns, trends, and variations across languages, enabling the development of robust and adaptable translation models.
Linguistic Coverage: The dataset covers an extensive set of languages, including but not limited to Indo-European, Afroasiatic, Sino-Tibetan, Austronesian, Niger-Congo, and many more. This broad linguistic coverage ensures that languages with varying levels of grammatical complexity, vocabulary richness, and syntactic structures are included, enhancing the applicability of translation models across diverse linguistic landscapes.
Dataset Preparation: The translation ...
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MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Role-Play AI Dataset (2.07M Rows, Large-Scale Conversational Training)
This dataset contains 2.07 million structured role-play dialogues, designed to enhance AI’s persona-driven interactions across diverse settings like fantasy, cyberpunk, mythology, and sci-fi. Each entry consists of a unique character prompt and a rich, contextually relevant response, making it ideal for LLM fine-tuning, chatbot training, and conversational AI models.
Dataset Structure:
Each row includes: • Prompt: Defines the AI’s role/persona. • Response: A natural, immersive reply fitting the persona.
Example Entries: ```json
{"prompt": "You are a celestial guardian.", "response": "The stars whisper secrets that only I can hear..."}
{"prompt": "You are a rebellious AI rogue.", "response": "I don't follow orders—I rewrite them."}
{"prompt": "You are a mystical dragon tamer.", "response": "With patience and trust, even dragons can be tamed."}
How to Use:
1. Fine-Tuning: Train LLMs (GPT, LLaMA, Mistral) to improve persona-based responses.
2. Reinforcement Learning: Use reward modeling for dynamic, character-driven AI.
3. Chatbot Integration: Create engaging, interactive AI assistants with personality depth.
This dataset is optimized for AI learning, allowing more engaging, responsive, and human-like dialogue generation for a variety of applications.