Best AI tools for< Assess Model Performance >

K2 AI is an AI consulting company that offers a range of services from ideation to impact, focusing on AI strategy, implementation, operation, and research. They support and invest in emerging start-ups and push knowledge boundaries in AI. The company helps executives assess organizational strengths, prioritize AI use cases, develop sustainable AI strategies, and continuously monitor and improve AI solutions. K2 AI also provides executive briefings, model development, and deployment services to catalyze AI initiatives. The company aims to deliver business value through rapid, user-centric, and data-driven AI development.

FairPlay is a Fairness-as-a-Service solution designed for financial institutions, offering AI-powered tools to assess automated decisioning models quickly. It helps in increasing fairness and profits by optimizing marketing, underwriting, and pricing strategies. The application provides features such as Fairness Optimizer, Second Look, Customer Composition, Redline Status, and Proxy Detection. FairPlay enables users to identify and overcome tradeoffs between performance and disparity, assess geographic fairness, de-bias proxies for protected classes, and tune models to reduce disparities without increasing risk. It offers advantages like increased compliance, speed, and readiness through automation, higher approval rates with no increase in risk, and rigorous Fair Lending analysis for sponsor banks and regulators. However, some disadvantages include the need for data integration, potential bias in AI algorithms, and the requirement for technical expertise to interpret results.

H2O.ai is an AI platform that offers a convergence of the world's best predictive and generative AI solutions. It provides end-to-end GenAI platform for air-gapped, on-premises, or cloud VPC deployments, allowing users to own every part of the stack. With features like h2oGPTe, h2oGPT, H2O Danube3, H2O Eval Studio, and GenAI App Store, H2O.ai empowers users to customize and deploy AI models, assess performance, develop safe applications, and more. The platform is known for democratizing AI with automated machine learning and open-source distributed machine learning.

Simpleem is an Artificial Emotional Intelligence (AEI) tool that helps users uncover intentions, predict success, and leverage behavior for successful interactions. By measuring all interactions and correlating them with concrete outcomes, Simpleem provides insights into verbal, para-verbal, and non-verbal cues to enhance customer relationships, track customer rapport, and assess team performance. The tool aims to identify win/lose patterns in behavior, guide users on boosting performance, and prevent burnout by promptly identifying red flags. Simpleem uses proprietary AI models to analyze real-world data and translate behavioral insights into concrete business metrics, achieving a high accuracy rate of 94% in success prediction.

Underwrite.ai is a platform that leverages advances in artificial intelligence and machine learning to provide lenders with nonlinear, dynamic models of credit risk. By analyzing thousands of data points from credit bureau sources, the application accurately models credit risk for consumers and small businesses, outperforming traditional approaches. Underwrite.ai offers a unique underwriting methodology that focuses on outcomes such as profitability and customer lifetime value, allowing organizations to enhance their lending performance without the need for capital investment or lengthy build times. The platform's models are continuously learning and adapting to market changes in real-time, providing explainable decisions in milliseconds.

Pitch N Hire is an AI-powered Applicant Tracking & Assessment Software designed to assist recruiters in enhancing their talent decisions. The platform offers a robust data-driven approach with descriptive, predictive, and prescriptive analytics to address talent acquisition challenges. It provides insights into candidate behavior, automated processes, and a vast network of career sites. With advanced AI data models, the software forecasts on-the-job performance, streamlines talent pipelines, and offers personalized branded experiences for candidates.

MyLooks AI is an AI-powered tool that allows users to assess their attractiveness based on a quick selfie upload. The tool provides instant feedback on the user's appearance and offers personalized improvement tips to help them enhance their looks. Users can track their progress with advanced AI-powered coaching and receive easy guidance to boost their confidence. MyLooks AI aims to help individuals feel more confident and improve their self-image through the use of artificial intelligence technology.

Dhenu Agri LLMs - KissanAI is an AI-powered application designed to assist farmers in optimizing their agricultural practices. The platform leverages artificial intelligence to provide farmers with valuable insights and recommendations for improving crop yield and overall farm productivity. By analyzing data such as weather patterns, soil quality, and crop health, KissanAI helps farmers make informed decisions to enhance their agricultural output. With user-friendly interfaces and intuitive features, this tool aims to empower farmers with cutting-edge technology to drive sustainable farming practices.

BCT Digital is an AI-powered risk management suite provider that offers a range of products to help enterprises optimize their core Governance, Risk, and Compliance (GRC) processes. The rt360 suite leverages next-generation technologies, sophisticated AI/ML models, data-driven algorithms, and predictive analytics to assist organizations in managing various risks effectively. BCT Digital's solutions cater to the financial sector, providing tools for credit risk monitoring, early warning systems, model risk management, environmental, social, and governance (ESG) risk assessment, and more.

FaceSymAI is an online tool that utilizes advanced AI algorithms to analyze and determine the symmetry of your face. By uploading a photo, the AI examines your facial features, including the eyes, nose, mouth, and overall structure, to provide an accurate assessment of your facial symmetry. The analysis is based on mathematical and statistical methods, ensuring reliable and precise results. FaceSymAI is designed to be user-friendly and accessible, offering a free service to everyone. The uploaded photos are treated with utmost confidentiality and are not stored or used for any other purpose, ensuring your privacy is respected.

This AI-powered age detection tool analyzes your photo to estimate how old you look. It utilizes advanced artificial intelligence technology to assess facial characteristics such as wrinkles, skin texture, and facial features, comparing them against a vast dataset to provide an approximation of your age. The tool is free to use and ensures privacy by automatically deleting uploaded photos after analysis.

Face Shape Detector is an advanced AI tool that analyzes facial landmarks in uploaded photos to identify the user's face shape and provide percentage distributions for different face shapes. It utilizes sophisticated algorithms to assess key metrics such as jawline, forehead width, and cheekbone structure, delivering detailed insights into facial proportions. Users can explore the power of facial analysis, understand their unique face shape, and receive quick and accurate results through this intuitive tool.

Microsoft Responsible AI Toolbox is a suite of tools designed to assess, develop, and deploy AI systems in a safe, trustworthy, and ethical manner. It offers integrated tools and functionalities to help operationalize Responsible AI in practice, enabling users to make user-facing decisions faster and easier. The Responsible AI Dashboard provides a customizable experience for model debugging, decision-making, and business actions. With a focus on responsible assessment, the toolbox aims to promote ethical AI practices and transparency in AI development.

The Data & Trust Alliance is a group of industry-leading enterprises focusing on the responsible use of data and intelligent systems. They develop practices to enhance trust in data and AI models, ensuring transparency and reliability in the deployment processes. The alliance works on projects like Data Provenance Standards and Assessing third-party model trustworthiness to promote innovation and trust in AI applications. Through technology and innovation adoption, they aim to leverage expertise and influence for practical solutions and broad adoption across industries.

Supply Chain Intelligence is an AI application that offers a comprehensive suite of tools for supply chain management. It provides advanced demand forecasting capabilities, digitization guidance, AI forecast model creation, forecasting segmentation, and assessment of demand forecasting process maturity. The application aims to streamline supply chain operations, enhance decision-making, and optimize planning processes through the use of artificial intelligence technologies.

LawBotica is an AI-powered platform that revolutionizes legal work by automating deposition summaries, crafting case timelines, offering comprehensive due diligence, document review, interactive chats, and collaborative workspaces. It transforms legal practice, turning months of work into minutes of efficiency. LawBotica empowers legal teams with AI-powered modules for document review, summarization, chat, repository management, and case assessment. It provides personalized solutions for legal practices, versatile deployment options, and a flexible API for custom integration.

Credo AI is a leading provider of AI governance, risk management, and compliance software. Our platform helps organizations to adopt AI safely and responsibly, while ensuring compliance with regulations and standards. With Credo AI, you can track and prioritize AI projects, assess AI vendor models for risk and compliance, create artifacts for audit, and more.

The website offers content moderation and image analysis products using powerful APIs to automatically assess, filter, and moderate images, videos, and text. It provides features such as image moderation, video moderation, text moderation, AI image detection, and video anonymization. The application helps in detecting unwanted content, AI-generated images, and personal information in videos. It also offers tools to identify near-duplicates, spam, and abusive links, and prevent phishing and circumvention attempts. The platform is fast, scalable, accurate, easy to integrate, and privacy compliant, making it suitable for various industries like marketplaces, dating apps, and news platforms.

3D AI Studio is an AI-powered platform that allows users to create custom 3D models, animations, and textures in seconds. It is designed to be user-friendly and intuitive, with no requirement to master modeling or prompt crafting. Users can simply input a text prompt or upload an image for reference, and the platform will generate a high-quality 3D model in seconds. 3D AI Studio offers a range of features, including the ability to export models in a variety of formats, generate customized and realistic textures, and access a library of pre-made models. It is a valuable tool for a variety of professionals, including game developers, 3D artists, and designers.

Luma Dream Machine is an AI video generator tool that creates high-quality, realistic videos from text and images. It is a scalable and efficient transformer model trained directly on videos, capable of generating physically accurate and eventful shots. The tool aims to build a universal imagination engine, enabling users to bring their creative visions to life effortlessly.

PremSQL is an open-source library designed to help developers create secure, fully local Text-to-SQL solutions using small language models. It provides essential tools for building and deploying end-to-end Text-to-SQL pipelines with customizable components, ideal for secure, autonomous AI-powered data analysis. The library offers features like Local-First approach, Customizable Datasets, Robust Executors and Evaluators, Advanced Generators, Error Handling and Self-Correction, Fine-Tuning Support, and End-to-End Pipelines. Users can fine-tune models, generate SQL queries from natural language inputs, handle errors, and evaluate model performance against predefined metrics. PremSQL is extendible for customization and private data usage.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

AlignBench is the first comprehensive evaluation benchmark for assessing the alignment level of Chinese large models across multiple dimensions. It includes introduction information, data, and code related to AlignBench. The benchmark aims to evaluate the alignment performance of Chinese large language models through a multi-dimensional and rule-calibrated evaluation method, enhancing reliability and interpretability.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

Seismometer is a suite of tools designed to evaluate AI model performance in healthcare settings. It helps healthcare organizations assess the accuracy of AI models and ensure equitable care for diverse patient populations. The tool allows users to validate model performance using standardized evaluation criteria based on local data and workflows. It includes templates for analyzing statistical performance, fairness across different cohorts, and the impact of interventions on outcomes. Seismometer is continuously evolving to incorporate new validation and analysis techniques.

LLM Hallucination Index - RAG Special is a comprehensive evaluation of large language models (LLMs) focusing on context length and open vs. closed-source attributes. The index explores the impact of context length on model performance and tests the assumption that closed-source LLMs outperform open-source ones. It also investigates the effectiveness of prompting techniques like Chain-of-Note across different context lengths. The evaluation includes 22 models from various brands, analyzing major trends and declaring overall winners based on short, medium, and long context insights. Methodologies involve rigorous testing with different context lengths and prompting techniques to assess models' abilities in handling extensive texts and detecting hallucinations.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

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.

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.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

R-Judge is a benchmarking tool designed to evaluate the proficiency of Large Language Models (LLMs) in judging and identifying safety risks within diverse environments. It comprises 569 records of multi-turn agent interactions, covering 27 key risk scenarios across 5 application categories and 10 risk types. The tool provides high-quality curation with annotated safety labels and risk descriptions. Evaluation of 11 LLMs on R-Judge reveals the need for enhancing risk awareness in LLMs, especially in open agent scenarios. Fine-tuning on safety judgment is found to significantly improve model performance.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

Do-Not-Answer is an open-source dataset curated to evaluate Large Language Models' safety mechanisms at a low cost. It consists of prompts to which responsible language models do not answer. The dataset includes human annotations and model-based evaluation using a fine-tuned BERT-like evaluator. The dataset covers 61 specific harms and collects 939 instructions across five risk areas and 12 harm types. Response assessment is done for six models, categorizing responses into harmfulness and action categories. Both human and automatic evaluations show the safety of models across different risk areas. The dataset also includes a Chinese version with 1,014 questions for evaluating Chinese LLMs' risk perception and sensitivity to specific words and phrases.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

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