Best AI tools for< Deduplicate Data >

Nero Platinum Suite is a comprehensive software collection for Windows PCs that provides a wide range of multimedia capabilities, including burning, managing, optimizing, and editing photos, videos, and music files. It includes various AI-powered features such as the Nero AI Image Upscaler, Nero AI Video Upscaler, and Nero AI Photo Tagger, which enhance and simplify multimedia tasks.

Roundtable is an AI-assisted data cleaning tool designed for enterprise survey programming. It offers an easy-to-integrate API for cleaning open-ended survey responses, saving up to 70% of time. The tool uses real-time behavioral tracking to detect unnatural typing and programmatic entries, and it provides multilingual functionality for deploying studies to various markets. Roundtable also features GPT detection to identify bots and participants, dynamic clustering to group duplicate responses, and programmatic pre-screening to auto-reject low-quality participants. The tool is trusted by leaders and innovators for improving data quality efforts and providing reliable human-generated insights.

Goodlookup is a smart function for spreadsheet users that gets very close to semantic understanding. It’s a pre-trained model that has the intuition of GPT-3 and the join capabilities of fuzzy matching. Use it like vlookup or index match to speed up your topic clustering work in google sheets!

Keploy is an AI tool designed for developers to generate API tests efficiently. It is an open-source platform that converts API calls to test cases with data mocks. Keploy simplifies testing by capturing network interactions and generating automated tests, helping teams accelerate development with streamlined testing processes. The tool allows users to record and replay complex API flows, find duplicate tests, and seamlessly integrate with popular testing libraries like JUnit, PyTest, Jest, and Go-Test in CI/CD pipelines.

Trust Stamp is a global provider of AI-powered identity services offering a full suite of identity tools, including biometric multi-factor authentication, document validation, identity validation, duplicate detection, and geolocation services. The application is designed to empower organizations across various sectors with advanced biometric identity solutions to reduce fraud, protect personal data privacy, increase operational efficiency, and reach a broader user base worldwide through unique data transformation and comparison capabilities. Founded in 2016, Trust Stamp has achieved significant milestones in net sales, gross profit, and strategic partnerships, positioning itself as a leader in the identity verification industry.

Quetext is a plagiarism checker and AI content detector that helps students, teachers, and professionals identify potential plagiarism and AI in their work. With its deep search technology, contextual analysis, and smart algorithms, Quetext makes checking writing easier and more accurate. Quetext also offers a variety of features such as bulk uploads, source exclusion, enhanced citation generator, grammar & spell check, and Deep Search. With its rich and intuitive feedback, Quetext helps users find plagiarism and AI with less stress.

Duplikate is a next-generation AI-powered Community Management tool designed to assist users in managing their social media accounts more efficiently. It helps users save time by retrieving relevant social media posts, categorizing them, and duplicating them with modifications to better suit their audience. The tool is powered by OpenAI and offers features such as post scraping, filtering, and copying, with upcoming features including image generation. Users have praised Duplikate for its ability to streamline content creation, improve engagement, and save time in managing social media accounts.

Dart is the ultimate AI project management tool designed to save time and streamline project management processes. It offers features like task execution, subtask generation, project planning, duplicate detection, roadmaps, calendar views, document storage, meeting notes, integrations with workplace tools, and more. Dart is used by teams across various roles like engineering, product management, leadership, design, and sales to enhance productivity and efficiency in task management. The application leverages AI capabilities to automate tasks, generate reports, and assist in project ideation and execution.

AppZen is an AI-powered application designed for modern finance teams to streamline accounts payable processes, automate invoice and expense auditing, and improve compliance. It offers features such as Autonomous AP for invoice automation, Expense Audit for T&E spend management, and Card Audit for analyzing card spend. AppZen's AI learns and understands business practices, ensures compliance, and integrates with existing systems easily. The application helps prevent duplicate spend, fraud, and FCPA violations, making it a valuable tool for finance professionals.

Snapy is an AI-powered video editing and generation tool that helps content creators create short videos, edit podcasts, and remove silent parts from videos. It offers a range of features such as turning text prompts into short videos, condensing long videos into engaging short clips, automatically removing silent parts from audio files, and auto-trimming, removing duplicate sentences and filler words, and adding subtitles to short videos. Snapy is designed to save time and effort for content creators, allowing them to publish more content, create more engaging videos, and improve the quality of their audio and video content.

WordLlama is a fast, lightweight NLP toolkit optimized for CPU hardware. It recycles components from large language models to create efficient word representations. It offers features like Matryoshka Representations, low resource requirements, binarization, and numpy-only inference. The tool is suitable for tasks like semantic matching, fuzzy deduplication, ranking, and clustering, making it a good option for NLP-lite tasks and exploratory analysis.

Chinese-Tiny-LLM is a repository containing procedures for cleaning Chinese web corpora and pre-training code. It introduces CT-LLM, a 2B parameter language model focused on the Chinese language. The model primarily uses Chinese data from a 1,200 billion token corpus, showing excellent performance in Chinese language tasks. The repository includes tools for filtering, deduplication, and pre-training, aiming to encourage further research and innovation in language model development.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

NeMo Curator is a GPU-accelerated open-source framework designed for efficient large language model data curation. It provides scalable dataset preparation for tasks like foundation model pretraining, domain-adaptive pretraining, supervised fine-tuning, and parameter-efficient fine-tuning. The library leverages GPUs with Dask and RAPIDS to accelerate data curation, offering customizable and modular interfaces for pipeline expansion and model convergence. Key features include data download, text extraction, quality filtering, deduplication, downstream-task decontamination, distributed data classification, and PII redaction. NeMo Curator is suitable for curating high-quality datasets for large language model training.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

**Description:** PromptFuzz is an automated tool that generates high-quality fuzz drivers for libraries via a fuzz loop constructed on mutating LLMs' prompts. The fuzz loop of PromptFuzz aims to guide the mutation of LLMs' prompts to generate programs that cover more reachable code and explore complex API interrelationships, which are effective for fuzzing. **Features:** * **Multiply LLM support** : Supports the general LLMs: Codex, Inocder, ChatGPT, and GPT4 (Currently tested on ChatGPT). * **Context-based Prompt** : Construct LLM prompts with the automatically extracted library context. * **Powerful Sanitization** : The program's syntax, semantics, behavior, and coverage are thoroughly analyzed to sanitize the problematic programs. * **Prioritized Mutation** : Prioritizes mutating the library API combinations within LLM's prompts to explore complex interrelationships, guided by code coverage. * **Fuzz Driver Exploitation** : Infers API constraints using statistics and extends fixed API arguments to receive random bytes from fuzzers. * **Fuzz engine integration** : Integrates with grey-box fuzz engine: LibFuzzer. **Benefits:** * **High branch coverage:** The fuzz drivers generated by PromptFuzz achieved a branch coverage of 40.12% on the tested libraries, which is 1.61x greater than _OSS-Fuzz_ and 1.67x greater than _Hopper_. * **Bug detection:** PromptFuzz detected 33 valid security bugs from 49 unique crashes. * **Wide range of bugs:** The fuzz drivers generated by PromptFuzz can detect a wide range of bugs, most of which are security bugs. * **Unique bugs:** PromptFuzz detects uniquely interesting bugs that other fuzzers may miss. **Usage:** 1. Build the library using the provided build scripts. 2. Export the LLM API KEY if using ChatGPT or GPT4. 3. Generate fuzz drivers using the `fuzzer` command. 4. Run the fuzz drivers using the `harness` command. 5. Deduplicate and analyze the reported crashes. **Future Works:** * **Custom LLMs suport:** Support custom LLMs. * **Close-source libraries:** Apply PromptFuzz to close-source libraries by fine tuning LLMs on private code corpus. * **Performance** : Reduce the huge time cost required in erroneous program elimination.

VSP-LLM (Visual Speech Processing incorporated with LLMs) is a novel framework that maximizes context modeling ability by leveraging the power of LLMs. It performs multi-tasks of visual speech recognition and translation, where given instructions control the task type. The input video is mapped to the input latent space of a LLM using a self-supervised visual speech model. To address redundant information in input frames, a deduplication method is employed using visual speech units. VSP-LLM utilizes Low Rank Adaptors (LoRA) for computationally efficient training.

Agentless is an open-source tool designed for automatically solving software development problems. It follows a two-phase process of localization and repair to identify faults in specific files, classes, and functions, and generate candidate patches for fixing issues. The tool is aimed at simplifying the software development process by automating issue resolution and patch generation.

Dolma is a dataset and toolkit for curating large datasets for (pre)-training ML models. The dataset consists of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. The toolkit provides high-performance, portable, and extensible tools for processing, tagging, and deduplicating documents. Key features of the toolkit include built-in taggers, fast deduplication, and cloud support.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

LLM Datasets is a repository containing high-quality datasets, tools, and concepts for LLM fine-tuning. It provides datasets with characteristics like accuracy, diversity, and complexity to train large language models for various tasks. The repository includes datasets for general-purpose, math & logic, code, conversation & role-play, and agent & function calling domains. It also offers guidance on creating high-quality datasets through data deduplication, data quality assessment, data exploration, and data generation techniques.

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.

x-crawl is a flexible Node.js AI-assisted crawler library that offers powerful AI assistance functions to make crawler work more efficient, intelligent, and convenient. It consists of a crawler API and various functions that can work normally even without relying on AI. The AI component is currently based on a large AI model provided by OpenAI, simplifying many tedious operations. The library supports crawling dynamic pages, static pages, interface data, and file data, with features like control page operations, device fingerprinting, asynchronous sync, interval crawling, failed retry handling, rotation proxy, priority queue, crawl information control, and TypeScript support.

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