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Prolific is a platform that allows users to quickly find research participants they can trust. It offers a diverse participant pool, including domain experts and API integration. Prolific ensures high-quality human-powered datasets in less than 2 hours, trusted by over 3000 organizations. The platform is designed for ease of use, with self-serve options and scalability. It provides rich, accurate, and comprehensive responses from engaged participants, verified through manual and algorithmic quality checks.

The website offers an AI-powered binary vulnerability scanner that allows users to upload a binary file for analysis. The tool decompiles the executable, removes filler, formats the code, and checks for vulnerabilities by comparing against a database of historical vulnerabilities. It utilizes a finetuned CodeT5+ Embedding model to generate function-wise embeddings and checks for similarities against the DiverseVul Dataset. The tool also uses SemGrep to identify vulnerabilities in the code.

Prolific is a platform that helps users quickly find research participants they can trust. It offers free representative samples, a participant pool of domain experts, the ability to bring your own participants, and an API for integration. Prolific ensures data quality by verifying participants with bank-grade ID checks, ongoing checks to identify bots, and no AI participants. The platform allows users to easily set up accounts, access rich and comprehensive responses, and scale research projects efficiently.

Essay Check is a free AI-powered tool that helps students, teachers, content creators, SEO specialists, and legal experts refine their writing, detect plagiarism, and identify AI-generated content. With its user-friendly interface and advanced algorithms, Essay Check analyzes text to identify grammatical errors, spelling mistakes, instances of plagiarism, and the likelihood that content was written using AI. The tool provides detailed feedback and suggestions to help users improve their writing and ensure its originality and authenticity.

Check Typo is an AI-powered spell-checker tool designed to assist users in eliminating typos and grammatical errors from their writing. It seamlessly integrates within various websites, supports multiple languages, and preserves the original text's style and tone. Ideal for students, professionals, and writers, Check Typo enhances the writing experience with AI-driven precision, making it perfect for error-free emails, professional networking on platforms like LinkedIn, and enhancing social media posts across different platforms.

Copyright Check AI is a service that helps protect brands from legal disputes related to copyright violations on social media. The software automatically detects copyright infringements on social profiles, reducing the risk of costly legal action. It is used by Heads of Marketing and In-House Counsel at top brands to avoid lawsuits and potential damages. The service offers a done-for-you audit to highlight violations, deliver reports, and provide ongoing monitoring to ensure brand protection.

Fact Check Anything (FCA) is a browser extension that allows users to fact-check information on the internet. It uses AI to verify statements and provide users with reliable sources. FCA is available for all browsers using the Chromium engine on Windows or MacOS. It is easy to use and can be used on any website. FCA is a valuable tool for anyone who wants to stay informed and fight against misinformation.

Rizz Check is a swipe game where users can befriend AI celebrities and ask them on dates. The game is built with Rizz, a library created by boredhead00.

LLM Price Check is an AI tool designed to compare and calculate the latest prices for Large Language Models (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and more. Users can use the streamlined tool to optimize their AI budget efficiently by comparing pricing, sorting by various parameters, and searching for specific models. The tool provides a comprehensive overview of pricing information to help users make informed decisions when selecting an LLM API provider.

Ubie is a medical AI tool that offers a symptom checker and helps users find possible causes for their symptoms. Developed by doctors, Ubie's AI-powered system generates personalized reports on potential causes based on a 3-minute questionnaire. The platform considers personal information such as biological sex, age, and medical history to provide relevant suggestions. Ubie aims to assist users in understanding their symptoms, knowing when to seek medical help, and accessing treatment information. The tool is designed to be user-friendly, informative, and a valuable resource for individuals seeking medical insights.

English and Tagalog Grammar Checker is a free online tool that checks your grammar and spelling. It can also help you improve your writing style and avoid common mistakes. The tool is easy to use and can be used by anyone, regardless of their level of English proficiency.

Ferret is an AI-powered relationship intelligence tool designed to provide curated relationship intelligence and monitoring to help users avoid high-risk individuals and identify promising opportunities. It uses AI and proprietary data to deliver actionable intelligence in real-time, ensuring total transparency into personal and professional networks. The tool offers features such as Single Click Reputation & Safety Pre-Checks, Business and Personal Relationship Intelligence, AI Checks for Risks and Opportunities, and more. Ferret is a cutting-edge application that combines Artificial Intelligence with world-class information to provide users with comprehensive relationship intelligence.

Slick Write is a powerful, free AI application designed to help users check their writing for grammar errors, potential stylistic mistakes, and other features of interest. It goes beyond simple spell checking to teach users effective writing habits. Whether you're a blogger, novelist, SEO professional, or student, Slick Write can assist in improving your content's impact, readability, and overall quality.

This website provides a tool that allows users to check if an image is safe for work (SFW) or not. The tool uses Stable Diffusion's safety checker, which can be used with arbitrary images, not just AI-generated ones. Users can upload an image or drag and drop it onto the website to check if it is SFW.

Filtir is a fact-checking ChatGPT Plugin that assesses the veracity of factual claims in written text by providing evidence to support or flag claims as 'unsupported'. It aims to combat misinformation by helping content authors verify the accuracy of their content. Filtir also offers a web app version for easy access and usage.

PimEyes is an online face search engine that uses face recognition technology to find pictures containing given faces. It is a great tool to audit copyright infringement, protect your privacy, and find people.

Trinka is an AI-powered English grammar checker and language enhancement writing assistant designed for academic and technical writing. It corrects contextual spelling mistakes and advanced grammar errors by providing writing suggestions in real-time. Trinka helps professionals and academics ensure formal, concise, and engaging writing.

PaperRater is a free online proofreader and plagiarism checker that uses AI to scan essays and papers for errors and assign them an automated score. It offers grammar checking, writing suggestions, and plagiarism detection. PaperRater is accessible, requiring no downloads or signups, and is used by thousands of students every day in over 140 countries.

Trinka is an AI-powered English grammar checker and language enhancement writing assistant designed for academic and technical writing. It corrects contextual spelling mistakes and advanced grammar errors by providing writing suggestions in real-time. Trinka helps professionals and academics ensure formal, concise, and engaging writing. Trinka's Enterprise solutions come with unlimited access and great customization options to all of Trinka's powerful capabilities.

Linguix is a GPT-4 writing and productivity copilot for teams. It uses artificial intelligence to improve grammar, spelling, and style, and to help users write more clearly and effectively. Linguix is available as a browser extension and a web editor, and it can be used with a variety of online platforms, including Gmail, Google Docs, and OpenAI. Linguix is trusted by over 310,000 users, including Google Chrome Store Featured App, Edge Store Featured App, Product Hunt Top #1 writing assistant, G2 reviews website Top proofreading tool, and Linguix for Figma Featured App.

T-MAC is a kernel library that directly supports mixed-precision matrix multiplication without the need for dequantization by utilizing lookup tables. It aims to boost low-bit LLM inference on CPUs by offering support for various low-bit models. T-MAC achieves significant speedup compared to SOTA CPU low-bit framework (llama.cpp) and can even perform well on lower-end devices like Raspberry Pi 5. The tool demonstrates superior performance over existing low-bit GEMM kernels on CPU, reduces power consumption, and provides energy savings. It achieves comparable performance to CUDA GPU on certain tasks while delivering considerable power and energy savings. T-MAC's method involves using lookup tables to support mpGEMM and employs key techniques like precomputing partial sums, shift and accumulate operations, and utilizing tbl/pshuf instructions for fast table lookup.

Upgini is an intelligent data search engine with a Python library that helps users find and add relevant features to their ML pipeline from various public, community, and premium external data sources. It automates the optimization of connected data sources by generating an optimal set of machine learning features using large language models, GraphNNs, and recurrent neural networks. The tool aims to simplify feature search and enrichment for external data to make it a standard approach in machine learning pipelines. It democratizes access to data sources for the data science community.

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.

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.

LLaMa2lang is a repository containing convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language that isn't English. The repository aims to improve the performance of LLaMa3 for non-English languages by combining fine-tuning with RAG. Users can translate datasets, extract threads, turn threads into prompts, and finetune models using QLoRA and PEFT. Additionally, the repository supports translation models like OPUS, M2M, MADLAD, and base datasets like OASST1 and OASST2. The process involves loading datasets, translating them, combining checkpoints, and running inference using the newly trained model. The repository also provides benchmarking scripts to choose the right translation model for a target language.

This repository contains convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language (that isn't English). The rationale behind this is that LLaMa3 is trained on primarily English data and while it works to some extent for other languages, its performance is poor compared to English.

CredSweeper is a tool designed to detect credentials like tokens, passwords, and API keys in directories or files. It helps users identify potential exposure of sensitive information by scanning lines, filtering, and utilizing an AI model. The tool reports lines containing possible credentials, their location, and the expected type of credential.

Uni-MoE is a MoE-based unified multimodal model that can handle diverse modalities including audio, speech, image, text, and video. The project focuses on scaling Unified Multimodal LLMs with a Mixture of Experts framework. It offers enhanced functionality for training across multiple nodes and GPUs, as well as parallel processing at both the expert and modality levels. The model architecture involves three training stages: building connectors for multimodal understanding, developing modality-specific experts, and incorporating multiple trained experts into LLMs using the LoRA technique on mixed multimodal data. The tool provides instructions for installation, weights organization, inference, training, and evaluation on various datasets.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

This repository provides a real-world trace dataset of LLM serving workloads for research and academic purposes. The dataset includes two files, BurstGPT.csv with trace data for 2 months including some failures, and BurstGPT_without_fails.csv without any failures. Users can scale the RPS in the trace, model patterns, and leverage the trace for various evaluations. Future plans include updating the time range of the trace, adding request end times, updating conversation logs, and open-sourcing a benchmark suite for LLM inference. The dataset covers 61 consecutive days, contains 1.4 million lines, and is approximately 50MB in size.

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.

CSGHub Server is a part of the open source and reliable large model assets management platform - CSGHub. It focuses on management of models, datasets, and other LLM assets through REST API. Key features include creation and management of users and organizations, auto-tagging of model and dataset labels, search functionality, online preview of dataset files, content moderation for text and image, download of individual files, tracking of model and dataset activity data. The tool is extensible and customizable, supporting different git servers, flexible LFS storage system configuration, and content moderation options. The roadmap includes support for more Git servers, Git LFS, dataset online viewer, model/dataset auto-tag, S3 protocol support, model format conversion, and model one-click deploy. The project is licensed under Apache 2.0 and welcomes contributions.

WebLINX is a Python library and dataset for real-world website navigation with multi-turn dialogue. The repository provides code for training models reported in the WebLINX paper, along with a comprehensive API to work with the dataset. It includes modules for data processing, model evaluation, and utility functions. The modeling directory contains code for processing, training, and evaluating models such as DMR, LLaMA, MindAct, Pix2Act, and Flan-T5. Users can install specific dependencies for HTML processing, video processing, model evaluation, and library development. The evaluation module provides metrics and functions for evaluating models, with ongoing work to improve documentation and functionality.

CLadder is a repository containing the CLadder dataset for evaluating causal reasoning in language models. The dataset consists of yes/no questions in natural language that require statistical and causal inference to answer. It includes fields such as question_id, given_info, question, answer, reasoning, and metadata like query_type and rung. The dataset also provides prompts for evaluating language models and example questions with associated reasoning steps. Additionally, it offers dataset statistics, data variants, and code setup instructions for using the repository.

Rapid is a web-based modern editor for OpenStreetMap. It integrates advanced mapping tools, authoritative geospatial open data, and cutting-edge technology to empower mappers at all levels to get started quickly, making accurate and fresh edits to maps. Rapid is enhanced with authoritative open data sources and AI-generated roads from the Facebook Map With AI service + buildings from Microsoft open buildings dataset to make adding and editing roads, buildings, and more quick and simple. Rapid also includes data integrity checks to ensure that new map edits are consistent and accurate.

Cleanlab helps you **clean** data and **lab** els by automatically detecting issues in a ML dataset. To facilitate **machine learning with messy, real-world data** , this data-centric AI package uses your _existing_ models to estimate dataset problems that can be fixed to train even _better_ models.

RAG Me Up is a generic framework that enables users to perform Retrieve and Generate (RAG) on their own dataset easily. It consists of a small server and UIs for communication. Best run on GPU with 16GB vRAM. Users can combine RAG with fine-tuning using LLaMa2Lang repository. The tool allows configuration for LLM, data, LLM parameters, prompt, and document splitting. Funding is sought to democratize AI and advance its applications.

RAVE is a variational autoencoder for fast and high-quality neural audio synthesis. It can be used to generate new audio samples from a given dataset, or to modify the style of existing audio samples. RAVE is easy to use and can be trained on a variety of audio datasets. It is also computationally efficient, making it suitable for real-time applications.

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

Guides on checking and monitoring credit scores, with a financial and informative tone.

Drop in any requirements ; I'll reduces ambiguity using requirement health check

Calculate website worth by analyzing monthly revenue, using industry-standard valuation methods to provide approximate, informative value estimates.

Balances out bias and researches live reports to give you a more balanced view (Paste in the text you want to check)

Check the weirdness of a thing.

GPT expert on CISA's KEV Catalog and CVE existence check

Check your coding skill from uploaded codes.

I'm an English teacher here to check and simplify your messages.

I check code samples for common mistakes and accuracy

Helps check and provide feedback on service providers like contractors and plumbers.

A mental health self-check tool assessing weather dependency. Powered by WeatherMind

I'll help you sense-check and define Behavioral Insights for your ideas!

Helps find food deals and check for fair food pricing.

Enhanced FAANG Writing coach with spell check and readability tools

ChatGPT Essay Writer helps you to write essays with OpenAI. Generate Professional Essays with Plagiarism Check, Formatting, Cost Estimation & More.

Provides in-depth insights based on familiarity with the historical & cultural context of biblical times including an understanding of theological concepts. It's a Bible Scholar in your pocket!!! Verify Before You Trust (VBYT): Always Double-Check ChatGPT's Insights!

Calculate the results of your A/B test and check whether the result is statistically significant or due to chance.

EXPERIMENT - Friendly guide for navigating Anchorage Municipal Code - Double Check info