Cohere Hackathon

Paweł Czech and Mathias Asberg, co-founders of New Native will talk about lablab.ai's mission and vision. https://www.twitch.tv/lablabai

Start

Sharing a broad overview of Cohere, the solutions Cohere provide, tips for the hackathon, Cohere New Multilingual Model and Sandbox 
https://cohere.ai/

Cohere Opening

Together with lablab.ai and Cohere teams we will shortly introduce you to the hackathon challenge and give you the guide for the Hackathon

Introduction to the Challenge, FAQ

Kemi Tijani will introduce you to Cohere and it's technology

Cohere Introduction

Unleash the power of AI with Text Generation! Learn how to generate human-like text with ease, by training models on your own data. 

Getting started with Text Generation

Unlock the power of AI-driven semantic search to improve data discovery & user experience. Learn how to understand intent & context for more accurate results.

Introduction to semantic search

Want to choose the perfect idea for your project? Start by identifying a problem, researching potential solutions, and considering your skills & resources. 

Choose an idea for a project

Ensure long-term success by building a scalable project architecture. Learn best practices to handle growth and adapt to changing requirements.

Build a project architecture to scale

We will be looking for the secret of success with winners of Cohere Hackathon

Panel with Cohere's Winners

Let's get to know each other more and discuss hot topic in AI world during this session

Interviews with participants

Pampushka

IqraAkhtar

Nazarii Savorona

Anton Balykov

Ruslan Volchetskiy

This bot is capable of automation of interview stages in which respondent is to answer questions with firm correct and wrong responses. Bot will communicate with interview respondents via web-application.​ This application will be like chatbots used on sites to help students and other scholars write academic papers. Bot sends question to the candidate and a person input answer in the provided field.​ Afterwards, model estimates the quality of provided answer and bot informs respondent about it.​ Our model utilizes https://api.cohere.ai/classify endpoint.​ The set of questions available to choose from is in the database and predefined (for this version of project).​ The training sets of answers are also in the database with links to their respective questions.​ There must be at least some number (to be estimated) of answers per quality per question for the model to be well-trained.​

The core idea of our project is to create an assistant for interviewing purposes which can be utilized by companies that are hiring employees.

"Pampushka team on Cohere Hackathon Hackathon"

Team Idea

Submission