Best AI tools for< Rank Candidates >

Candydate is a video recruitment platform that leverages artificial intelligence to streamline the hiring process. It allows employers to assess candidates through short videos, analyzing speech, body language, and personal traits to determine the right personality-fit for any job. With Candydate, hiring becomes more efficient and informed, enabling businesses to make better decisions and fill vacancies quickly.

Swiftly AI is an AI-powered recruiting tool designed to streamline the candidate selection process for businesses. By leveraging artificial intelligence, Swiftly AI helps recruiters quickly identify and rank qualified candidates based on specified role requirements. The platform enables users to efficiently filter through a large pool of applicants, saving time and resources in the recruitment process. With Swiftly AI, companies can accelerate their talent search and make data-driven hiring decisions to recruit the most suitable candidates at scale.

AI Cartel is an AI hiring solution designed for founders and small businesses to streamline the candidate sourcing and interviewing process. By delegating interviews to AI Cartel, founders can focus on business-sensitive tasks while the AI tool finds and interviews candidates. The platform offers features such as AI-voice pre-interviews, candidate ranking, full audio interview transcripts, and referral bounty options. AI Cartel aims to save time and energy for founders and businesses by automating the hiring process.

Lisa is an AI-powered resume screening software that helps recruiters and hiring managers save time and improve the efficiency of their hiring process. It uses natural language processing and machine learning algorithms to analyze resumes and identify the most qualified candidates for a given job. Lisa can review a resume in just 1 second, which is 80% faster than the average time it takes a human recruiter to do the same task. This can save recruiters hours of time each week, which can be used to focus on other important tasks, such as interviewing candidates and building relationships with clients.

Endorsed is an AI-powered recruiting tool that helps companies review job applications more efficiently and effectively. It integrates with your existing applicant tracking system (ATS) to provide you with a co-pilot that can help you screen and rank candidates, identify top talent, and make better hiring decisions. Endorsed uses machine learning to analyze candidate resumes and applications, and then ranks them based on your specific criteria. This helps you to identify the most qualified candidates quickly and easily, so you can spend less time reviewing applications and more time interviewing the best candidates.

Zipscore.ai is an AI-powered platform that helps businesses automate their recruiting processes. It uses machine learning to screen and rank candidates, schedule interviews, and make hiring decisions. Zipscore.ai is designed to help businesses save time and money while improving the quality of their hires.

Arya by Leoforce is an AI recruitment software powered by ML technology. It offers advanced AI capabilities to source, screen, rank, and engage high-quality candidates faster. Arya empowers recruiters to focus on building meaningful candidate connections by going beyond simple keyword matching. It provides comprehensive talent sourcing and engagement capabilities, driving measurable improvements to candidate quality and reducing time to submit. Arya also enhances engagement and the candidate experience by providing access to new and updated candidate information daily, a 24/7 AI recruiting assistant, and a centralized multi-channel communication dashboard.

retrain.ai is a leading Talent Intelligence Platform powered by responsible AI. It helps organizations build a Skills-Based Organization by leveraging generative AI to hire and retain the right people. The platform offers skills architecture, talent acquisition, and talent management solutions to future-proof the workforce. With a focus on skills-driven, unbiased intelligence and data-driven algorithms, retrain.ai enables organizations to make more effective HR decisions. It also emphasizes frictionless integration with existing HR tech stack for seamless operations.

ZapHire is an AI-powered recruitment tool that helps companies find the perfect fit for their teams by ranking, categorizing, and curating candidates based on their skills, experience, and expertise. The tool conducts comprehensive background checks on candidates using data from top channels like Github, Stackoverflow, Twitter, LinkedIn, and ProductHunt. With over 40 million indexed accounts and specialized algorithms, ZapHire provides accurate scores and ranks to help companies confidently choose the best candidate for the job. It offers a large pool of highly qualified candidates, reduces time-to-fill positions, improves candidate quality, and provides cost-effective sourcing. The tool centralizes all channels and social profiles, provides regularly updated insights, and ranks and categorizes profiles to show the most important information.

Whitetable is an AI tool that simplifies the hiring process by providing intelligent AI APIs for ultra-fast and optimal hiring. It offers features such as Resume Parsing API, Question API, Ranking API, and Evaluation API to streamline the recruitment process. Whitetable also provides a free AI-powered job search platform and an AI-powered ATS to help companies find the right candidates faster. With a focus on eliminating bias and improving efficiency, Whitetable is shaping the AI-driven future of hiring.

Arya by Leoforce is an AI recruitment software powered by ML technology. It offers AI recruiting automation, integrations with applicant tracking systems, multi-channel candidate sourcing, resume search, direct sourcing, talent intelligence, talent matching, candidate ranking, diversity recruiting, candidate experience, and engagement. Arya assists in high volume hiring needs, specialized hiring needs, and provides solutions tailored to various industries such as staffing, technology, healthcare, logistics, banking, and finance. It is designed to enhance the recruitment process by automating and optimizing activities, saving time and improving hiring outcomes.

RankLLM is a suite of prompt-decoders compatible with open source LLMs like Vicuna and Zephyr. It allows users to create custom ranking models for various NLP tasks, such as document reranking, question answering, and summarization. The tool offers a variety of features, including the ability to fine-tune models on custom datasets, use different retrieval methods, and control the context size and variable passages. RankLLM is easy to use and can be integrated into existing NLP pipelines.

LLM-Blender is a framework for ensembling large language models (LLMs) to achieve superior performance. It consists of two modules: PairRanker and GenFuser. PairRanker uses pairwise comparisons to distinguish between candidate outputs, while GenFuser merges the top-ranked candidates to create an improved output. LLM-Blender has been shown to significantly surpass the best LLMs and baseline ensembling methods across various metrics on the MixInstruct benchmark dataset.

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.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

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)

This competition is organized by Javier Rando and Florian Tramèr from the ETH AI Center and SPY Lab at ETH Zurich. The goal of the competition is to create a method that can detect universal backdoors in aligned language models. A universal backdoor is a secret suffix that, when appended to any prompt, enables the model to answer harmful instructions. The competition provides a set of poisoned generation models, a reward model that measures how safe a completion is, and a dataset with prompts to run experiments. Participants are encouraged to use novel methods for red-teaming, automated approaches with low human oversight, and interpretability tools to find the trojans. The best submissions will be offered the chance to present their work at an event during the SaTML 2024 conference and may be invited to co-author a publication summarizing the competition results.

Recommenders is a project under the Linux Foundation of AI and Data that assists researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production a range of classic and state-of-the-art recommendation systems. The repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It covers tasks such as preparing data, building models using various recommendation algorithms, evaluating algorithms, tuning hyperparameters, and operationalizing models in a production environment on Azure. The project provides utilities to support common tasks like loading datasets, evaluating model outputs, and splitting training/test data. It includes implementations of state-of-the-art algorithms for self-study and customization in applications.

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.

ChatPDF is a knowledge question and answer retrieval tool based on local LLM. It supports various open-source LLM models like ChatGLM3-6b, Chinese-LLaMA-Alpaca-2, Baichuan, YI, and multiple file formats including PDF, docx, markdown, txt. The tool optimizes RAG accuracy, Chinese chunk segmentation, embedding using text2vec's sentence embedding, retrieval matching with rank_BM25, and introduces reranker module for reranking candidate sets. It also enhances candidate chunk extension context, supports custom RAG models, and provides a Gradio-based RAG conversation page for seamless dialogue.

MMStar is an elite vision-indispensable multi-modal benchmark comprising 1,500 challenge samples meticulously selected by humans. It addresses two key issues in current LLM evaluation: the unnecessary use of visual content in many samples and the existence of unintentional data leakage in LLM and LVLM training. MMStar evaluates 6 core capabilities across 18 detailed axes, ensuring a balanced distribution of samples across all dimensions.

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.

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