Best AI tools for< Generate Data >

Generate is an experiment to generate data from LLM. It is now deprecated.

Scrol.ai is a powerful AI-powered tool that allows users to search, analyze, and generate data from various sources. It utilizes advanced language models like GPT-4 and ChatGPT to provide users with a seamless and efficient way to extract insights, summarize information, and create new content. With its user-friendly interface and robust features, Scrol.ai empowers users to streamline their workflow, enhance productivity, and make informed decisions.

Gretel.ai is a synthetic data platform purpose-built for AI applications. It allows users to generate artificial, synthetic datasets with the same characteristics as real data, enabling the improvement of AI models without compromising privacy. The platform offers features such as generating data from input prompts, creating safe synthetic versions of sensitive datasets, flexible data transformation, building data pipelines, and measuring data quality. Gretel.ai is designed to help developers unlock synthetic data and achieve more with safe access to the right data.

Charm is an AI-powered spreadsheet assistant that helps users clean messy data, create content, summarize feedback, classify sales leads, and generate dummy data. It is a Google Sheets add-on that automates tasks that are impossible to do with traditional formulas. Charm is used by hundreds of analysts, marketers, product managers, and more.

Rendered.ai is a platform that provides unlimited synthetic data for AI and ML applications, specifically focusing on computer vision. It helps in generating low-cost physically-accurate data to overcome bias and power innovation in AI and ML. The platform allows users to capture rare events and edge cases, acquire data that is difficult to obtain, overcome data labeling challenges, and simulate restricted or high-risk scenarios. Rendered.ai aims to revolutionize the use of synthetic data in AI and data analytics projects, with a vision that by 2030, synthetic data will surpass real data in AI models.

Kiln is an AI tool designed for fine-tuning LLM models, generating synthetic data, and facilitating collaboration on datasets. It offers intuitive desktop apps, zero-code fine-tuning for various models, interactive visual tools for data generation, Git-based version control for datasets, and the ability to generate various prompts from data. Kiln supports a wide range of models and providers, provides an open-source library and API, prioritizes privacy, and allows structured data tasks in JSON format. The tool is free to use and focuses on rapid AI prototyping and dataset collaboration.

WebDB is an open-source and efficient Database IDE that focuses on providing a secure and user-friendly platform for database management. It offers features such as automatic DBMS discovery, credential guessing, time machine for database version control, powerful queries editor with autocomplete and documentation, AI assistant integration, NoSQL structure management, intelligent data generation, and more. With a modern ERD view and support for various databases, WebDB aims to simplify database management tasks and enhance productivity for users.

Athenic AI is an Enterprise AI tool designed to provide data insights quickly and easily. It allows users to ask questions and follow-up questions to explore trends, discover insights, and connect the dots in their data. The tool offers natural language analytics, fast data retrieval, and analysis for various business functions like E-Commerce, Marketing, and Manufacturing. Athenic AI aims to equip teams with reliable insights and streamline data analysis processes.

Chat2DB is an AI-driven data management platform that helps users query, edit, analyze, and visualize data. It integrates data management, development, analysis, and application all in one platform. Chat2DB's AI technology enables users to easily handle SQL, generate database data, and test efficiently. It also provides intelligent reports and data exploration features that allow users to interact with data using natural language.

Scale AI is an AI tool that accelerates the development of AI applications for various sectors including enterprise, government, and automotive industries. It offers solutions for training models, fine-tuning, generative AI, and model evaluations. Scale Data Engine and GenAI Platform enable users to leverage enterprise data effectively. The platform collaborates with leading AI models and provides high-quality data for public and private sector applications.

The website generatejson.com appears to be inaccessible due to an 'Access Denied' error. It seems that users are encountering permission issues when trying to access the site. The error message references a server issue and provides a specific reference number. The website may be related to generating JSON data, but further details are not available from the provided text.

Athina AI is a platform that provides research and guides for building safe and reliable AI products. It helps thousands of AI engineers in building safer products by offering tutorials, research papers, and evaluation techniques related to large language models. The platform focuses on safety, prompt engineering, hallucinations, and evaluation of AI models.

AdAstra is an AI-powered advertising automation platform designed to streamline and optimize advertising processes for businesses of all sizes. With intuitive AI bidding, auto campaigns, profit maximizers, and ads automation, AdAstra helps users increase efficiency, reach their goals faster, and unlock the potential of their brand through AI-driven ad strategies and machine learning insights. The platform offers innovative brand growth strategies, effortless marketing solutions, and a comprehensive suite of tools to automate advertising tasks and generate actionable insights.

Evisort is an AI-powered contract management software that simplifies contract management at every stage. It offers a complete, AI-native platform for end-to-end contract lifecycle management, including the first large language model built specifically for contracts. Evisort's AI capabilities enable users to ask questions about their contracts in simple, natural language and get clear, reasoned answers. It can also track terms of interest across all contracts and related documents, and generate data points that matter for sales, procurement, risk, and finance teams. Additionally, Evisort's AI-powered workflows automate tasks such as redlining, clause generation, and contract approvals, saving time and reducing risk.

AI Placeholder is a free AI-Powered Fake or Dummy Data API for testing and prototyping. It leverages OpenAI's GPT-3.5-Turbo Model API to generate fake or dummy content. Users can directly use the hosted version or self-host it. The API allows users to generate any data they can think of, with the ability to specify rules for data retrieval. It supports various content types like tweets, posts, Instagram posts, and more. The application is designed to assist developers and testers in creating realistic but fictional data for their projects.

Fine-Tune AI is a tool that allows users to generate fine-tune data sets using prompts. This can be useful for a variety of tasks, such as improving the accuracy of machine learning models or creating new training data for AI applications.

Bifrost AI is a data generation engine designed for AI and robotics applications. It enables users to train and validate AI models faster by generating physically accurate synthetic datasets in 3D simulations, eliminating the need for real-world data. The platform offers pixel-perfect labels, scenario metadata, and a simulated 3D world to enhance AI understanding. Bifrost AI empowers users to create new scenarios and datasets rapidly, stress test AI perception, and improve model performance. It is built for teams at every stage of AI development, offering features like automated labeling, class imbalance correction, and performance enhancement.

Lazy Admin is an AI-powered quick reporting and data analysis tool designed to revolutionize data engagement by providing real-time responses to human language queries. It enables smart reporting and faster decision-making by leveraging the power of AI. With features like data protection, AI-powered data analysis, export and share capabilities, and customizable options, Lazy Admin aims to streamline productivity and enhance data insights for businesses. The tool ensures data privacy and security while offering efficient search management and visualization of data through charts. Lazy Admin is suitable for Salesforce users and custom applications, offering a range of pricing plans to cater to different business needs.

DB Sensei is an AI-powered SQL tool that helps developers generate, fix, explain, and format SQL queries with ease. It features a user-friendly interface, AI-driven query generation, query fixing, query explaining, and query formatting. DB Sensei is designed for developers, database administrators, and students who want to get faster results and improve their database skills.

The website offers a Synthetic Data Generation platform with the highest accuracy for free. It provides detailed information on synthetic data, data anonymization, and features a Python Client for data generation. The platform ensures privacy and security, allowing users to create fully anonymous synthetic data from original data. It supports various AI/ML use cases, self-service analytics, testing & QA, and data sharing. The platform is designed for Enterprise organizations, offering scalability, privacy by design, and the world's most accurate synthetic data.

Financial Datasets is an open-source Python library that allows users to create question and answer financial datasets using Large Language Models (LLMs). With this library, users can easily generate realistic financial datasets from 10-K, 10-Q, PDF, and other financial texts. The library provides three main methods for generating datasets: from any text, from a 10-K filing, or from a PDF URL. Financial Datasets can be used for a variety of tasks, including financial analysis, research, and education.

Dataformer is a robust framework for creating high-quality synthetic datasets for AI, offering speed, reliability, and scalability. It empowers engineers to rapidly generate diverse datasets grounded in proven research methodologies, enabling them to prioritize data excellence and achieve superior standards for AI models. Dataformer integrates with multiple LLM providers using one unified API, allowing parallel async API calls and caching responses to minimize redundant calls and reduce operational expenses. Leveraging state-of-the-art research papers, Dataformer enables users to generate synthetic data with adaptability, scalability, and resilience, shifting their focus from infrastructure concerns to refining data and enhancing models.

DataDreamer is a powerful open-source Python library designed for prompting, synthetic data generation, and training workflows. It is simple, efficient, and research-grade, allowing users to create prompting workflows, generate synthetic datasets, and train models with ease. The library is built for researchers, by researchers, focusing on correctness, best practices, and reproducibility. It offers features like aggressive caching, resumability, support for bleeding-edge techniques, and easy sharing of datasets and models. DataDreamer enables users to run multi-step prompting workflows, generate synthetic datasets for various tasks, and train models by aligning, fine-tuning, instruction-tuning, and distilling them using existing or synthetic data.

DataDreamer is an advanced toolkit designed to facilitate the development of edge AI models by enabling synthetic data generation, knowledge extraction from pre-trained models, and creation of efficient and potent models. It eliminates the need for extensive datasets by generating synthetic datasets, leverages latent knowledge from pre-trained models, and focuses on creating compact models suitable for integration into any device and performance for specialized tasks. The toolkit offers features like prompt generation, image generation, dataset annotation, and tools for training small-scale neural networks for edge deployment. It provides hardware requirements, usage instructions, available models, and limitations to consider while using the library.

Vodalus Expert LLM Forge is a tool designed for crafting datasets and efficiently fine-tuning models using free open-source tools. It includes components for data generation, LLM interaction, RAG engine integration, model training, fine-tuning, and quantization. The tool is suitable for users at all levels and is accompanied by comprehensive documentation. Users can generate synthetic data, interact with LLMs, train models, and optimize performance for local execution. The tool provides detailed guides and instructions for setup, usage, and customization.

Auto Data is a library designed for the automatic generation of realistic datasets, essential for the fine-tuning of Large Language Models (LLMs). This highly efficient and lightweight library enables the swift and effortless creation of comprehensive datasets across various topics, regardless of their size. It addresses challenges encountered during model fine-tuning due to data scarcity and imbalance, ensuring models are trained with sufficient examples.

AI-TOD is a dataset for tiny object detection in aerial images, containing 700,621 object instances across 28,036 images. Objects in AI-TOD are smaller with a mean size of 12.8 pixels compared to other aerial image datasets. To use AI-TOD, download xView training set and AI-TOD_wo_xview, then generate the complete dataset using the provided synthesis tool. The dataset is publicly available for academic and research purposes under CC BY-NC-SA 4.0 license.

Bonito is an open-source model for conditional task generation, converting unannotated text into task-specific training datasets for instruction tuning. It is a lightweight library built on top of Hugging Face `transformers` and `vllm` libraries. The tool supports various task types such as question answering, paraphrase generation, sentiment analysis, summarization, and more. Users can easily generate synthetic instruction tuning datasets using Bonito for zero-shot task adaptation.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

hCaptcha-Solver is an AI-based hcaptcha text challenge solver that utilizes the playwright module to generate the hsw N data. It can solve any text challenge without any problem, but may be flagged on some websites like Discord. The tool requires proxies since hCaptcha also rate limits. Users can run the 'hsw_api.py' before running anything and then integrate the usage shown in 'main.py' into their projects that require hCaptcha solving. Please note that this tool only works on sites that support hCaptcha text challenge.

Neo4j Runway is a Python library that simplifies the process of migrating relational data into a graph. It provides tools to abstract communication with OpenAI for data discovery, generate data models, ingestion code, and load data into a Neo4j instance. The library leverages OpenAI LLMs for insights, Instructor Python library for modeling, and PyIngest for data loading. Users can visualize data models using graphviz and benefit from a seamless integration with Neo4j for efficient data migration.

Cookiecutter Data Science (CCDS) is a tool for setting up a data science project template that incorporates best practices. It provides a logical, reasonably standardized but flexible project structure for doing and sharing data science work. The tool helps users to easily start new data science projects with a well-organized directory structure, including folders for data, models, notebooks, reports, and more. By following the project template created by CCDS, users can streamline their data science workflow and ensure consistency across projects.

LLMII is a tool that uses a local AI model to label metadata and index images without relying on cloud services or remote APIs. It runs a visual language model on your computer to generate captions and keywords for images, enhancing their metadata for indexing, searching, and organization. The tool can be run multiple times on the same image files, allowing for adding new data, regenerating data, and discovering files with issues. It supports various image formats, offers a user-friendly GUI, and can utilize GPU acceleration for faster processing. LLMII requires Python 3.8 or higher and operates directly on image file metadata fields like MWG:Keyword and XMP:Identifier.

This is the official repository for 'Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing'. Magpie is a tool designed to synthesize high-quality instruction data at scale by extracting it directly from an aligned Large Language Models (LLMs). It aims to democratize AI by generating large-scale alignment data and enhancing the transparency of model alignment processes. Magpie has been tested on various model families and can be used to fine-tune models for improved performance on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.

Graph-enhanced Large Language Models in Asynchronous Plan Reasoning is a repository containing code and datasets for the ICML-2024 paper. It includes naturalistic datasets, code for generating data, benchmarking experiments, and prototypical experiments. The repository also offers a train/test-split version of the dataset on huggingface. The paper focuses on utilizing large language models with graph enhancements for asynchronous plan reasoning.

LLM Alchemy Chamber is a repository dedicated to exploring the world of Language Models (LLMs) through various experiments and projects. It contains scripts, notebooks, and experiments focused on tasks such as fine-tuning different LLM models, quantization for performance optimization, dataset generation for instruction/QA tasks, and more. The repository offers a collection of resources for beginners and enthusiasts interested in delving into the mystical realm of LLMs.

MMOS (Mix of Minimal Optimal Sets) is a dataset designed for math reasoning tasks, offering higher performance and lower construction costs. It includes various models and data subsets for tasks like arithmetic reasoning and math word problem solving. The dataset is used to identify minimal optimal sets through reasoning paths and statistical analysis, with a focus on QA-pairs generated from open-source datasets. MMOS also provides an auto problem generator for testing model robustness and scripts for training and inference.

DLIO is an I/O benchmark tool designed for Deep Learning applications. It emulates modern deep learning applications using Benchmark Runner, Data Generator, Format Handler, and I/O Profiler modules. Users can configure various I/O patterns, data loaders, data formats, datasets, and parameters. The tool is aimed at emulating the I/O behavior of deep learning applications and provides a modular design for flexibility and customization.

Generate synthetic timeseries data

Pass in the attributes of Pojo entity class objects, generate corresponding addition, deletion, modification, and pagination query functions, including generating database connection configuration files yaml and database script files, as well as XML dynamic SQL concatenation statements.

Takes in a Project Design Document and generates a database schema diagram for the project.

是一个专门用于建立数学模型并撰写python代码的gpt.

Japanese patent data graph generator with user engagement

Generates fictional user profiles for software testing

Makes every Sofware Tester an Expert Prompt Engineer

Through historical data to generate Lotto numbers + Powerball

Chat with the Bible, analyze Bible data and generate Bible-inspired images! Utilises ESV Bible API.

Generate SQL Queries Using Your Database, Tailored for Every Skill Level!

Generate and explain regex patterns from your description, it support English and Chinese.

I suggest data science projects and give tips on request.

Dalle + custom corrupted data from every artist in the world. This is an experiment. (beta)

Secure, innovative GPT for collaborative data science and education.

Generate Type-safe SQL queries and schemas

I generate custom Notion formulas and explain how to use them.

根据用户输入的数据参数生成标签

Data trend analysis expert in cannabis, also skilled in image and data analysis, document generation, and web search.

Expert in data analysis and ethical insight delivery.

Generate bibtex for a research paper title