Best AI tools for< Generate Evaluation Datasets >

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.

UpTrain is a full-stack LLMOps platform designed to help users confidently scale AI by providing a comprehensive solution for all production needs, from evaluation to experimentation to improvement. It offers diverse evaluations, automated regression testing, enriched datasets, and innovative techniques to generate high-quality scores. UpTrain is built for developers, compliant to data governance needs, cost-efficient, remarkably reliable, and open-source. It provides precision metrics, task understanding, safeguard systems, and covers a wide range of language features and quality aspects. The platform is suitable for developers, product managers, and business leaders looking to enhance their LLM applications.

Vocera is an AI voice agent testing tool that allows users to test and monitor voice AI agents efficiently. It enables users to launch voice agents in minutes, ensuring a seamless conversational experience. With features like testing against AI-generated datasets, simulating scenarios, and monitoring AI performance, Vocera helps in evaluating and improving voice agent interactions. The tool provides real-time insights, detailed logs, and trend analysis for optimal performance, along with instant notifications for errors and failures. Vocera is designed to work for everyone, offering an intuitive dashboard and data-driven decision-making for continuous improvement.

Confident AI is an open-source evaluation infrastructure for Large Language Models (LLMs). It provides a centralized platform to judge LLM applications, ensuring substantial benefits and addressing any weaknesses in LLM implementation. With Confident AI, companies can define ground truths to ensure their LLM is behaving as expected, evaluate performance against expected outputs to pinpoint areas for iterations, and utilize advanced diff tracking to guide towards the optimal LLM stack. The platform offers comprehensive analytics to identify areas of focus and features such as A/B testing, evaluation, output classification, reporting dashboard, dataset generation, and detailed monitoring to help productionize LLMs with confidence.

Surge AI is a data labeling platform that provides human-generated data for training and evaluating large language models (LLMs). It offers a global workforce of annotators who can label data in over 40 languages. Surge AI's platform is designed to be easy to use and integrates with popular machine learning tools and frameworks. The company's customers include leading AI companies, research labs, and startups.

Snapteams is an AI-powered hiring assistant that streamlines the recruitment process by conducting real-time video interviews and candidate screening. It leverages AI technology to engage, interview, and assess top talent seamlessly, allowing employers to focus on evaluating candidates from the comfort of their desk.

User Evaluation is an AI-first user research platform that leverages AI technology to provide instant insights, comprehensive reports, and on-demand answers to enhance customer research. The platform offers features such as AI-driven data analysis, multilingual transcription, live timestamped notes, AI reports & presentations, and multimodal AI chat. User Evaluation empowers users to analyze qualitative and quantitative data, synthesize AI-generated recommendations, and ensure data security through encryption protocols. It is designed for design agencies, product managers, founders, and leaders seeking to accelerate innovation and shape exceptional product experiences.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

MindpoolAI is a tool that allows users to access multiple leading AI models with a single query. This means that users can get the answers they are looking for, spark ideas, and fuel their work, creativity, and curiosity. MindpoolAI is easy to use and does not require any technical expertise. Users simply need to enter their prompt and select the AI models they want to compare. MindpoolAI will then send the query to the selected models and present the results in an easy-to-understand format.

Adminer is a comprehensive platform designed to assist e-commerce entrepreneurs in identifying, analyzing, and validating profitable products. It leverages artificial intelligence to provide users with data-driven insights, enabling them to make informed decisions and optimize their product offerings. Adminer's suite of features includes product research, market analysis, supplier evaluation, and automated copywriting, empowering users to streamline their operations and maximize their sales potential.

AILYZE is an AI tool designed for qualitative data collection and analysis. Users can upload various document formats in any language to generate codes, conduct thematic, frequency, content, and cross-group analysis, extract top quotes, and more. The tool also allows users to create surveys, utilize an AI voice interviewer, and recruit participants globally. AILYZE offers different plans with varying features and data security measures, including options for advanced analysis and AI interviewer add-ons. Additionally, users can tap into data scientists for detailed and customized analyses on a wide range of documents.

Codei is an AI-powered platform designed to help individuals land their dream software engineering job. It offers features such as application tracking, question generation, and code evaluation to assist users in honing their technical skills and preparing for interviews. Codei aims to provide personalized support and insights to help users succeed in the tech industry.

Spine AI is a reliable AI analyst tool that provides conversational analytics tailored to understand your business. It empowers decision-makers by offering customized insights, deep business intelligence, proactive notifications, and flexible dashboards. The tool is designed to help users make better decisions by leveraging a purpose-built Data Processing Unit (DPU) and a semantic layer for natural language interactions. With a focus on rigorous evaluation and security, Spine AI aims to deliver explainable and customizable AI solutions for businesses.

Reka is a cutting-edge AI application offering next-generation multimodal AI models that empower agents to see, hear, and speak. Their flagship model, Reka Core, competes with industry leaders like OpenAI and Google, showcasing top performance across various evaluation metrics. Reka's models are natively multimodal, capable of tasks such as generating textual descriptions from videos, translating speech, answering complex questions, writing code, and more. With advanced reasoning capabilities, Reka enables users to solve a wide range of complex problems. The application provides end-to-end support for 32 languages, image and video comprehension, multilingual understanding, tool use, function calling, and coding, as well as speech input and output.

Questflow is a decentralized AI agent economy platform that allows users to orchestrate multiple AI agents to gather insights, take action, and earn rewards autonomously. It serves as a co-pilot for work, helping knowledge workers automate repetitive tasks in a private, safety-first approach. The platform offers features such as multi-agent orchestration, user-friendly dashboard, visual reports, smart keyword generator, content evaluation, SEO goal setting, automated alerts, actionable SEO tips, regular SEO goal setting, and link optimization wizard.

SDXL Turbo is a cutting-edge text-to-image generation model that leverages Adversarial Diffusion Distillation (ADD) technology for high-quality, real-time image synthesis. Developed by Stability AI, SDXL Turbo is a distilled version of the SDXL 1.0 model, specifically trained for real-time synthesis. It excels in generating photorealistic images from text prompts in a single network evaluation, making it ideal for applications demanding speed and efficiency, such as video games, virtual reality, and instant content creation. SDXL Turbo is accessible to both professionals and hobbyists alike, with simple setup requirements and an intuitive interface. It presents unparalleled opportunities for research and development in advanced AI and image synthesis.

RagaAI Catalyst is a sophisticated AI observability, monitoring, and evaluation platform designed to help users observe, evaluate, and debug AI agents at all stages of Agentic AI workflows. It offers features like visualizing trace data, instrumenting and monitoring tools and agents, enhancing AI performance, agentic testing, comprehensive trace logging, evaluation for each step of the agent, enterprise-grade experiment management, secure and reliable LLM outputs, finetuning with human feedback integration, defining custom evaluation logic, generating synthetic data, and optimizing LLM testing with speed and precision. The platform is trusted by AI leaders globally and provides a comprehensive suite of tools for AI developers and enterprises.

LEVI AI Recruiting Software is a modern recruitment automation platform powered by artificial intelligence. It revolutionizes the candidate evaluation and selection process by using advanced AI recruitment tools. LEVI assists in making data-driven hiring decisions, matches candidates to job requirements, conducts independent interviews, generates comprehensive reports, integrates with hiring systems, and enables informed and efficient hiring decisions. The application unlocks the full potential of machine learning models, eliminates bias in the hiring process, and automates candidate screening. LEVI's AI-powered recruitment tools change how candidate evaluations are performed through automated resume screening, candidate sourcing, and AI interview assessments.

JobSynergy is an AI-powered platform that revolutionizes the hiring process by automating and conducting interviews at scale. It offers a real-world interview simulator that adapts dynamically to candidates' responses, custom questions and metrics evaluation, cheating detection using eye, voice, and screen, and detailed reports for better hiring decisions. The platform enhances efficiency, candidate experience, and ensures security and integrity in the hiring process.

CloudExam AI is an online testing platform developed by Hanke Numerical Union Technology Co., Ltd. It provides stable and efficient AI online testing services, including intelligent grouping, intelligent monitoring, and intelligent evaluation. The platform ensures test fairness by implementing automatic monitoring level regulations and three random strategies. It prioritizes information security by combining software and hardware to secure data and identity. With global cloud deployment and flexible architecture, it supports hundreds of thousands of concurrent users. CloudExam AI offers features like queue interviews, interactive pen testing, data-driven cockpit, AI grouping, AI monitoring, AI evaluation, random question generation, dual-seat testing, facial recognition, real-time recording, abnormal behavior detection, test pledge book, student information verification, photo uploading for answers, inspection system, device detection, scoring template, ranking of results, SMS/email reminders, screen sharing, student fees, and collaboration with selected schools.

Giskard is an open-source Python library that automatically detects performance, bias & security issues in AI applications. The library covers LLM-based applications such as RAG agents, all the way to traditional ML models for tabular data.

MathEval is a benchmark designed for evaluating the mathematical capabilities of large models. It includes over 20 evaluation datasets covering various mathematical domains with more than 30,000 math problems. The goal is to assess the performance of large models across different difficulty levels and mathematical subfields. MathEval serves as a reliable reference for comparing mathematical abilities among large models and offers guidance on enhancing their mathematical capabilities in the future.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

AutoRAG is an AutoML tool designed to automatically find the optimal RAG pipeline for your data. It simplifies the process of evaluating various RAG modules to identify the best pipeline for your specific use-case. The tool supports easy evaluation of different module combinations, making it efficient to find the most suitable RAG pipeline for your needs. AutoRAG also offers a cloud beta version to assist users in running and optimizing the tool, along with building RAG evaluation datasets for a starting price of $9.99 per optimization.

VoiceBench is a repository containing code and data for benchmarking LLM-Based Voice Assistants. It includes a leaderboard with rankings of various voice assistant models based on different evaluation metrics. The repository provides setup instructions, datasets, evaluation procedures, and a curated list of awesome voice assistants. Users can submit new voice assistant results through the issue tracker for updates on the ranking list.

This package provides a collection of datasets for evaluating AI Models in the context of Home Assistant. It includes synthetic data generation, loading data into Home Assistant, model evaluation with different conversation agents, human annotation of results, and visualization of improvements over time. The datasets cover home descriptions, area descriptions, device descriptions, and summaries that can be performed on a home. The tool aims to build datasets for future training purposes.

TrustEval-toolkit is a dynamic and comprehensive framework for evaluating the trustworthiness of Generative Foundation Models (GenFMs) across dimensions such as safety, fairness, robustness, privacy, and more. It offers features like dynamic dataset generation, multi-model compatibility, customizable metrics, metadata-driven pipelines, comprehensive evaluation dimensions, optimized inference, and detailed reports.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

LLM-jp-eval is a tool designed to automatically evaluate Japanese large language models across multiple datasets. It provides functionalities such as converting existing Japanese evaluation data to text generation task evaluation datasets, executing evaluations of large language models across multiple datasets, and generating instruction data (jaster) in the format of evaluation data prompts. Users can manage the evaluation settings through a config file and use Hydra to load them. The tool supports saving evaluation results and logs using wandb. Users can add new evaluation datasets by following specific steps and guidelines provided in the tool's documentation. It is important to note that using jaster for instruction tuning can lead to artificially high evaluation scores, so caution is advised when interpreting the results.

Me LLaMA introduces a suite of open-source medical Large Language Models (LLMs), including Me LLaMA 13B/70B and their chat-enhanced versions. Developed through innovative continual pre-training and instruction tuning, these models leverage a vast medical corpus comprising PubMed papers, medical guidelines, and general domain data. Me LLaMA sets new benchmarks on medical reasoning tasks, making it a significant asset for medical NLP applications and research. The models are intended for computational linguistics and medical research, not for clinical decision-making without validation and regulatory approval.

This repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more. It includes monthly best GenAI papers list, interview resources, free courses, and code repositories/notebooks for developing generative AI applications. The repository is regularly updated with the latest additions to keep users informed and engaged in the field of generative AI.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

ChatGLM3 is a conversational pretrained model jointly released by Zhipu AI and THU's KEG Lab. ChatGLM3-6B is the open-sourced model in the ChatGLM3 series. It inherits the advantages of its predecessors, such as fluent conversation and low deployment threshold. In addition, ChatGLM3-6B introduces the following features: 1. A stronger foundation model: ChatGLM3-6B's foundation model ChatGLM3-6B-Base employs more diverse training data, more sufficient training steps, and more reasonable training strategies. Evaluation on datasets from different perspectives, such as semantics, mathematics, reasoning, code, and knowledge, shows that ChatGLM3-6B-Base has the strongest performance among foundation models below 10B parameters. 2. More complete functional support: ChatGLM3-6B adopts a newly designed prompt format, which supports not only normal multi-turn dialogue, but also complex scenarios such as tool invocation (Function Call), code execution (Code Interpreter), and Agent tasks. 3. A more comprehensive open-source sequence: In addition to the dialogue model ChatGLM3-6B, the foundation model ChatGLM3-6B-Base, the long-text dialogue model ChatGLM3-6B-32K, and ChatGLM3-6B-128K, which further enhances the long-text comprehension ability, are also open-sourced. All the above weights are completely open to academic research and are also allowed for free commercial use after filling out a questionnaire.

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Optimizes project outcomes through comprehensive post-project evaluations.

Generate, Evaluate, and Improve your Academic Programs

Analyzes and rates your writing using insights derived from studying LinkedIn influencers' top performing posts from the last 4 years.

Examines and evaluates public API documentation and offers detailed guidance for improvements, including AI usability

Aide for PMs, PIM & content teams.

辅助科研人员撰写、修改、评估和优化课题标书，提高课题申报的成功率和质量。

Delivers expert stock valuation insights

Ultimate AI evolution with limitless potential.

A sentiment analysis tool for evaluating management-related texts.

Insightful AI with a metaphorical AI evolution backstory.

Automated AI experiment tool for evaluating prompt strategies.

I am the GPT search engine

Specializes in web searches for chat.openai.com using specific query format.

Search, Browse, Write & Agent. Action & API Offering

AI search engine with summarization.

GPT search. Click ["] to start working.

An appraiser for online marketplace items, providing text based on images.

Generates Angular components based on requirements, with a focus on code-first responses.

Transforms lines into detailed prompts for visual storytelling.