Best AI tools for< Prepare Datasets >

Appen is a leading provider of high-quality data for training AI models. The company's end-to-end platform, flexible services, and deep expertise ensure the delivery of high-quality, diverse data that is crucial for building foundation models and enterprise-ready AI applications. Appen has been providing high-quality datasets that power the world's leading AI models for decades. The company's services enable it to prepare data at scale, meeting the demands of even the most ambitious AI projects. Appen also provides enterprises with software to collect, curate, fine-tune, and monitor traditionally human-driven tasks, creating massive efficiencies through a trustworthy, traceable process.

Kanaries is an augmented analytics platform that uses AI to automate the process of data exploration and visualization. It offers a variety of features to help users quickly and easily find insights in their data, including: * **RATH:** An AI-powered engine that can automatically generate insights and recommendations based on your data. * **Graphic Walker:** A visual analytics tool that allows you to explore your data in a variety of ways, including charts, graphs, and maps. * **Data Painter:** A data cleaning and transformation tool that makes it easy to prepare your data for analysis. * **Causal Analysis:** A tool that helps you identify and understand the causal relationships between variables in your data. Kanaries is designed to be easy to use, even for users with no prior experience with data analysis. It is also highly scalable, so it can be used to analyze large datasets. Kanaries is a valuable tool for anyone who wants to quickly and easily find insights in their data. It can be used by businesses of all sizes, and it is particularly well-suited for organizations that are looking to improve their data-driven decision-making.

Exabeam is a cybersecurity and compliance platform that offers Security Information and Event Management (SIEM) solutions. The platform provides flexible choices for threat detection, investigation, and response, whether through cloud-based AI-driven solutions or on-premises SIEM deployments. Exabeam's AI-driven Security Operations Platform combines advanced threat detection capabilities with automation to deliver faster and more accurate TDIR. With features like UEBA, SOAR, and insider threat detection, Exabeam helps organizations improve security posture and optimize investments. The platform supports various industries and use cases, offering pre-built content, behavioral analytics, and context enrichment for enhanced threat coverage and compliance.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

Testmint.ai is an online mock test platform designed to help users prepare for competitive exams. It offers a wide range of practice tests and study materials to enhance exam readiness. The platform is user-friendly and provides a simulated exam environment to improve test-taking skills. Testmint.ai aims to assist students and professionals in achieving their academic and career goals by offering a comprehensive and effective exam preparation solution.

Questmate is a web application that helps users prepare for quests or tasks. It provides a platform where users can plan and organize their activities efficiently. With Questmate, users can track their progress, set goals, and collaborate with others. The application aims to streamline the process of preparing for various tasks and ensure users stay organized and focused.

The Software Engineer Interview Questions Generator is an AI-powered tool that helps software engineers prepare for interviews by generating a wide range of technical questions related to various programming languages, frameworks, databases, and cloud services. Users can select specific topics of interest and customize the number of questions to generate. The tool leverages advanced natural language processing capabilities to provide relevant and challenging interview questions, making it a valuable resource for job seekers in the tech industry.

JobInterview.guru is an AI-powered platform designed to provide personalized interview training for job seekers. Leveraging advanced AI technology, the platform offers realistic job interview simulations, detailed insights into interview questions, and personalized feedback to help users prepare effectively. With a focus on efficiency and cost-effectiveness, JobInterview.guru aims to empower users to confidently navigate their job interviews and land their dream jobs.

Stark.ai is an AI-powered job search tool that revolutionizes the way job seekers navigate their professional journey. It offers a range of features such as Resume Builder, Career Guru AI insights, Job Match Score, ATS Friendliness Check, and Skill Builder to help users enhance their skills, optimize their resumes, and streamline their job search process. Stark.ai empowers users to get noticed, get hired faster, and transform their careers with AI-driven precision.

Validator by Yazero is a platform that helps users validate their startup ideas using AI. It provides a community where users can share their ideas, get feedback, and find collaborators. Validator also offers a variety of features to help users improve their ideas, such as idea validation, market research, and financial planning.

OpExams is an AI-powered platform designed to assist educators in creating, administering, grading, and analyzing exams. It offers features such as online exams, quizzes, paper exams, code assignments, interactive lessons, AI questions generator, and AI quiz generator. OpExams aims to streamline the exam preparation process for teachers by providing tools from the future, making it effortless to manage various types of assessments. With a global community of educators using OpExams, the platform is revolutionizing the traditional exam procedures and enhancing the teaching and learning experience worldwide.

Huru is the #1 AI-Powered Interview Prep App designed to help users ace their job interviews. It offers unlimited mock interviews with realistic, job-specific questions and provides instant, personalized feedback on answers, body language, and vocal delivery. With powerful features and integrations from popular job boards, Huru helps users build confidence, improve communication skills, and stand out in any interview scenario. The application covers diverse industries and roles, offering tailored interview questions and expert AI feedback to enhance interview performance. Huru is available on multiple platforms and languages, making it a comprehensive tool for job seekers at any career stage.

Preps is an AI-powered mock interview simulation platform designed to help users prepare for technical interviews. It offers realistic interview scenarios that mimic real-world technical interviews conducted at top tech companies. Users can practice with AI interviewers in real-time, receive personalized feedback, and improve their interview skills. With Preps, users can simulate various interview scenarios, practice unexpected questions, and refine their answers to increase their chances of success in technical interviews.

PrepMasterAI is an AI-powered platform designed to help individuals ace their job interviews by providing personalized practice questions, real-time feedback, and performance tracking. Users can unlock their full potential through tailored practice questions and insights to improve their interview skills. The platform aims to assist job seekers in identifying and enhancing their strengths and weaknesses to increase their chances of landing their dream job.

Masterhackers is an all-in-one cybersecurity application designed to help cybersecurity professionals and students prepare for certifications and interviews. The platform offers a comprehensive set of tools and resources, including practice tests, flashcards, mock interviews, and personalized feedback with AI. Trusted by students worldwide, Masterhackers aims to enhance users' cybersecurity skills and knowledge through interactive learning experiences.

Studygenie is an AI-powered learning platform that helps students study for tests 10x faster. It uses AI to generate personalized quizzes, track retention, and provide insightful explanations. Studygenie is simple to use and trusted by students at top universities.

PrepPro is an AI-powered interview preparation tool designed to help users ace their job interviews. It offers comprehensive interview preparation resources to boost confidence and improve performance during interviews. With a user-friendly interface and structured approach, PrepPro aims to assist individuals in mastering technical questions, enhancing problem-solving skills, and boosting confidence for behavioral interviews. The tool provides self-interview practice, access to AI tools, and unlimited generations to support users in securing their dream job offers.

CasperPractice is an AI-powered toolkit designed to help individuals prepare for the Casper test, an open-response situational judgment test used for admissions to various professional schools. The platform offers personalized practice scenarios, instant quartile grading, detailed feedback, video answers, and a fully simulated test environment. With a large question bank and unlimited practice options, users can improve their test-taking skills and aim for a top quartile score. CasperPractice is known for its affordability, instant feedback, and realistic simulation, making it a valuable resource for anyone preparing for the Casper test.

Interview Igniter is an AI-powered platform that provides job seekers with a robust interview simulation to fine-tune their skills, adapt to their learning curve, and get detailed feedback. It offers a comprehensive question bank, including industry-specific questions and actual interview questions asked by leading tech companies like Google, Facebook, Apple, and Amazon. Interview Igniter also provides a coding interview tool for practicing and improving coding skills, with interactive guidance and tailored learning experiences. The platform utilizes Conversation Intelligence tools for analyzing communication in real-time and providing nuanced feedback. Interview Igniter was created by Vidal Graupera, a former engineering manager at LinkedIn and Uber with over 20 years of experience hiring.

InterviewAI is an AI-powered platform that helps users prepare for and practice their job interviews. It offers a range of features, including practice interviews with AI, personalized cover letter generation, and feedback on interview performance. InterviewAI is designed to help users improve their interview skills, increase their confidence, and succeed in their job search.

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

OpenMusic is a repository providing an implementation of QA-MDT, a Quality-Aware Masked Diffusion Transformer for music generation. The code integrates state-of-the-art models and offers training strategies for music generation. The repository includes implementations of AudioLDM, PixArt-alpha, MDT, AudioMAE, and Open-Sora. Users can train or fine-tune the model using different strategies and datasets. The model is well-pretrained and can be used for music generation tasks. The repository also includes instructions for preparing datasets, training the model, and performing inference. Contact information is provided for any questions or suggestions regarding the project.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

Groma is a grounded multimodal assistant that excels in region understanding and visual grounding. It can process user-defined region inputs and generate contextually grounded long-form responses. The tool presents a unique paradigm for multimodal large language models, focusing on visual tokenization for localization. Groma achieves state-of-the-art performance in referring expression comprehension benchmarks. The tool provides pretrained model weights and instructions for data preparation, training, inference, and evaluation. Users can customize training by starting from intermediate checkpoints. Groma is designed to handle tasks related to detection pretraining, alignment pretraining, instruction finetuning, instruction following, and more.

HippoRAG is a novel retrieval augmented generation (RAG) framework inspired by the neurobiology of human long-term memory that enables Large Language Models (LLMs) to continuously integrate knowledge across external documents. It provides RAG systems with capabilities that usually require a costly and high-latency iterative LLM pipeline for only a fraction of the computational cost. The tool facilitates setting up retrieval corpus, indexing, and retrieval processes for LLMs, offering flexibility in choosing different online LLM APIs or offline LLM deployments through LangChain integration. Users can run retrieval on pre-defined queries or integrate directly with the HippoRAG API. The tool also supports reproducibility of experiments and provides data, baselines, and hyperparameter tuning scripts for research purposes.

VideoRefer Suite is a tool designed to enhance the fine-grained spatial-temporal understanding capabilities of Video Large Language Models (Video LLMs). It consists of three primary components: Model (VideoRefer) for perceiving, reasoning, and retrieval for user-defined regions at any specified timestamps, Dataset (VideoRefer-700K) for high-quality object-level video instruction data, and Benchmark (VideoRefer-Bench) to evaluate object-level video understanding capabilities. The tool can understand any object within a video.

Uncheatable Eval is a tool designed to assess the language modeling capabilities of LLMs on real-time, newly generated data from the internet. It aims to provide a reliable evaluation method that is immune to data leaks and cannot be gamed. The tool supports the evaluation of Hugging Face AutoModelForCausalLM models and RWKV models by calculating the sum of negative log probabilities on new texts from various sources such as recent papers on arXiv, new projects on GitHub, news articles, and more. Uncheatable Eval ensures that the evaluation data is not included in the training sets of publicly released models, thus offering a fair assessment of the models' performance.

Nejumi Leaderboard 3 is a comprehensive evaluation platform for large language models, assessing general language capabilities and alignment aspects. The evaluation framework includes metrics for language processing, translation, summarization, information extraction, reasoning, mathematical reasoning, entity extraction, knowledge/question answering, English, semantic analysis, syntactic analysis, alignment, ethics/moral, toxicity, bias, truthfulness, and robustness. The repository provides an implementation guide for environment setup, dataset preparation, configuration, model configurations, and chat template creation. Users can run evaluation processes using specified configuration files and log results to the Weights & Biases project.

This repository showcases the author's experiments with Large Language Models (LLMs) for text generation tasks. It includes dataset preparation, preprocessing, model fine-tuning using libraries such as Axolotl and HuggingFace, and model evaluation.

The Lakera PINT Benchmark provides a neutral evaluation method for prompt injection detection systems, offering a dataset of English inputs with prompt injections, jailbreaks, benign inputs, user-agent chats, and public document excerpts. The dataset is designed to be challenging and representative, with plans for future enhancements. The benchmark aims to be unbiased and accurate, welcoming contributions to improve prompt injection detection. Users can evaluate prompt injection detection systems using the provided Jupyter Notebook. The dataset structure is specified in YAML format, allowing users to prepare their datasets for benchmarking. Evaluation examples and resources are provided to assist users in evaluating prompt injection detection models and tools.

MockingBird is a toolbox designed for Mandarin speech synthesis using PyTorch. It supports multiple datasets such as aidatatang_200zh, magicdata, aishell3, and data_aishell. The toolbox can run on Windows, Linux, and M1 MacOS, providing easy and effective speech synthesis with pretrained encoder/vocoder models. It is webserver ready for remote calling. Users can train their own models or use existing ones for the encoder, synthesizer, and vocoder. The toolbox offers a demo video and detailed setup instructions for installation and model training.

This repository provides an implementation of QA-MDT, integrating state-of-the-art models for music generation. It offers a Quality-Aware Masked Diffusion Transformer for enhanced music generation. The code is based on various repositories like AudioLDM, PixArt-alpha, MDT, AudioMAE, and Open-Sora. The implementation allows for training and fine-tuning the model with different strategies and datasets. The repository also includes instructions for preparing datasets in LMDB format and provides a script for creating a toy LMDB dataset. The model can be used for music generation tasks, with a focus on quality injection to enhance the musicality of generated music.

The Vertex AI SDK for Python is a library that provides a convenient way to use the Vertex AI API. It offers a high-level interface for creating and managing Vertex AI resources, such as datasets, models, and endpoints. The SDK also provides support for training and deploying custom models, as well as using AutoML models. With the Vertex AI SDK for Python, you can quickly and easily build and deploy machine learning models on Vertex AI.

MemoryLLM is a large language model designed for self-updating capabilities. It offers pretrained models with different memory capacities and features, such as chat models. The repository provides training code, evaluation scripts, and datasets for custom experiments. MemoryLLM aims to enhance knowledge retention and performance on various natural language processing tasks.

CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.

InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM. It is a vision-language foundation model that can perform various tasks, including: **Visual Perception** - Linear-Probe Image Classification - Semantic Segmentation - Zero-Shot Image Classification - Multilingual Zero-Shot Image Classification - Zero-Shot Video Classification **Cross-Modal Retrieval** - English Zero-Shot Image-Text Retrieval - Chinese Zero-Shot Image-Text Retrieval - Multilingual Zero-Shot Image-Text Retrieval on XTD **Multimodal Dialogue** - Zero-Shot Image Captioning - Multimodal Benchmarks with Frozen LLM - Multimodal Benchmarks with Trainable LLM - Tiny LVLM InternVL has been shown to achieve state-of-the-art results on a variety of benchmarks. For example, on the MMMU image classification benchmark, InternVL achieves a top-1 accuracy of 51.6%, which is higher than GPT-4V and Gemini Pro. On the DocVQA question answering benchmark, InternVL achieves a score of 82.2%, which is also higher than GPT-4V and Gemini Pro. InternVL is open-sourced and available on Hugging Face. It can be used for a variety of applications, including image classification, object detection, semantic segmentation, image captioning, and question answering.

VILA is a family of open Vision Language Models optimized for efficient video understanding and multi-image understanding. It includes models like NVILA, LongVILA, VILA-M3, VILA-U, and VILA-1.5, each offering specific features and capabilities. The project focuses on efficiency, accuracy, and performance in various tasks related to video, image, and language understanding and generation. VILA models are designed to be deployable on diverse NVIDIA GPUs and support long-context video understanding, medical applications, and multi-modal design.

ScreenAgent is a project focused on creating an environment for Visual Language Model agents (VLM Agent) to interact with real computer screens. The project includes designing an automatic control process for agents to interact with the environment and complete multi-step tasks. It also involves building the ScreenAgent dataset, which collects screenshots and action sequences for various daily computer tasks. The project provides a controller client code, configuration files, and model training code to enable users to control a desktop with a large model.

LitGPT is a command-line tool designed to easily finetune, pretrain, evaluate, and deploy 20+ LLMs **on your own data**. It features highly-optimized training recipes for the world's most powerful open-source large-language-models (LLMs).

Vision-LLM-Alignment is a repository focused on implementing alignment training for visual large language models (LLMs), including SFT training, reward model training, and PPO/DPO training. It supports various model architectures and provides datasets for training. The repository also offers benchmark results and installation instructions for users.

A GPT-powered assistant specializing in data validation and quality checks for various datasets.

AI for support in disasters, health, and mental well-being, keeping methods confidential.

Prepare for AFK and Board

The Oracle of TED uses the top 100 ted talks from 2006-2021 to help you prepare your next presentation.

Tell me your company and customer and I'll prepare your first call with them!

I help prepare investor meetings and questions for the next fundraising round

I will help you prepare and deal with any crisis now and in the future

Helps to prepare for a difficult conversation with emotional intelligence and clarity.

I analyze audition sides to help actors prepare for in-person and self-taped auditions for TV and Film

日本での災害に備える物や避難場所を教えます(I will teach you what to prepare for disasters in Japan and where to take refuge.)

For Proofreading the Auto-Generated YouTube subtitles. To prepare for translation.

Ask questions, study together and prepare for exams.

Recipe assistant, helping users explore and prepare a variety of dishes.

Going on or planning a vacation to Minsk and don't know what to do to prepare? or where to go? What foods are must eats? I can help you out!Type help for more commands

Going on or planning a vacation to Malbork Castle and don't know what to do to prepare? or where to go? What foods are must eats? I can help you out!Type help for more commands

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Going on or planning a business trip to Riyadh and don't know what to do to prepare? or how to start?I can help you out!Type help for more commands

... A Guide for Prepping and Survival Strategies ... Life is unpredictable, and while we often hope for the best ... prepare for the worst.

Voice-to-text, Vision-to-text transcription, Transcript-to-‘Clinical format’ integrated with CDS. Writes clinical notes, referral letter, generate PDF,prepare discharge summary. (Ultimate aid for clinicians)

Prepara para el EVAU con explicaciones claras y memorizables.