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QuestionPro
QuestionPro
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Free survey software: QuestionPro online survey software to create surveys and questionnaires in minutes! Use our free survey platform with 80+ question-types, ready made templates, multiple survey distribution & data collection option and robust surve...
Free survey software: QuestionPro online survey software to create surveys and questionnaires in minutes! Use our free survey platform with 80+ question-types, ready made templates, multiple survey distribution & data collection option and robust surve...
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Product Description

QuestionPro is a leading provider of online survey software that allows our users to generate the insights they need to make better business decisions. Our software includes not only tools for creation, distribution, and analysis of surveys, but also provides a platform for polling, tablet-based mobile research, and data visualization.

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Product Description

QuestionPro CX analyzes textual feedback of your customers and gain insights from the qualitative data.

Product Description

QuestionPro LivePolls is a completely free audience response software that can be used to run polls live. It requires no app downloads and can be used to run polls, quizzes, or play trivia games. Supports anonymity as well as live comments & voting, with no limits whatsoever. Build by one of the world's leading research and data collection software, QuestionPro LivePolls is sophisticated to meet the enterprise demands while also being user-friendly and intuitive for a student. A versatile a

Product Description

QuestionPro Workforce is a culture assessment and analytics solution designed to measure how employees experience the workplace within a 21st century context, with deep demographic segmentation and comparisons.

Product Description

SurveyRock is an intuitive online survey platform designed to help businesses and individuals gather valuable feedback efficiently. With a user-friendly interface, it enables the creation, distribution, and analysis of surveys tailored to diverse needs. Supporting multiple languages, including right-to-left scripts, SurveyRock ensures accessibility for a global audience. Its versatile question types, such as multiple-choice, text, image, and rating scales, allow for comprehensive data collection

Product Description

QuestionScout scales with your business and helps you collect data through online forms and surveys.

Product Description

Versational® helps people maximize the value of their conversations. Versational transcribes 60 minutes of conversations into 6 minutes of summarized takeaways. It combines the power of human and artificial intelligence to extract key phrases based on your topics of importance. It cuts 1/3 of your work with automation. Versational identifies and extracts what’s important in every conversation. Our patent-pending, AI-Gems™ technology is the first to pair your question-responses with insi

Product Description

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

Product Description

NLSQL works as NLP to SQL API without any sensitive or confidential data transfer outside company IT ecosystem. Client NLSQL App deploys on-premise services you need with a brand new Microsoft Teams and dedicated to your company Web App, including data visualisation, reporting and anomaly detection services. NLSQL model requires a few prerequisites such as database structure emulation at our self-service platform, KPIs intents calculation methodology mapping to specific columns. NLSQL administra

Product Description

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on Multilingual Wikipedia returns an embedding of the input pair of question-context strings.

Product Description

Cloud-based low-code / no-code platform for building websites, landing pages and advanced brand funnels. Build your web assets with easy to use drag & drop user interface. Ready-to-use predefined modules allow you to build fast. Global styling keeps your projects consistent. Use copy-paste for your modules and create a unique design system for future development. From simple web forms to advanced multistep quizzes and questioners. Popups, booking widgets and ready-to-use integrations API.

Product Description

Collabrify Writer is an easy to learn, easy to use, collabrified, multimedia authoring app that allows teachers to use Writer to create reflective assessments with Question/Response items and to create interactive readers with multimedia content where scaffolding prompts are interspersed throughout the content.

Product Description

8Quali é um software de gestão da qualidade desenvolvido para automatizar e otimizar os processos diários das empresas, alinhando-se às principais certificações e acreditações, como ISO 9001, PALC e Acreditação ONA. A plataforma é 100% web, acessível via navegadores padrão, eliminando a necessidade de instalações locais e permitindo acesso remoto seguro. Principais Funcionalidades: - Gestão de Documentos: Permite a definição de responsáveis e prazos para elaboração, revisão e aprovação de doc

Product Description

We are listeners, questioners, and collaborative problem-solvers. We are passionate advocates for your audience. We are smart, experienced, and polished. We are designers who get things done.

Product Description

It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

Product Description

It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

Product Description

It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

Product Description

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

Product Description

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

Product Description

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on Multilingual Wikipedia returns an embedding of the input pair of question-context strings.