Band of Agents Hackathon

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Semantic Search AI Hackathon

Build and Deploy LLM-powered apps

๐Ÿ—“๏ธ This will be a 7-day virtual hackathon from 16-23 December ๐Ÿ’ป Build AI application with the latest large language model-powered technology by Cohere ๐Ÿ’ก Get the chance to work with the best AI professionals in the industry and learn from them โœ”๏ธEntry level = 0. Youโ€™ve just started with AI? Are you an experienced Data Scientist? Or maybe you are a Designer or Business Developer? Join us! We need your domain knowledge! ๐Ÿฑโ€๐Ÿ’ป Register now and let's get started! Itโ€™s free!
Semantic Search AI Hackathon event thumbnail

Cohere

Cohere API provides access to state of the art, affordable, and easy-to-deploy large language models capable of powering the next generation of game-changing AI native applications.

Powerful NLP Model

Cohere trained their models on billions of words to understand the nuances and context markers of human communication.

Flexible API

The Cohere API works with many different libraries that fit every stack. Cohere's Python, Node, and Go SDKs make AI easy to integrate into your app.

The Future of software

Build innovative and extremely powerful applications that were previously not possible. The future of software development is AI native.

Cohere Challenge

๐Ÿ‘‰ The challenge for this hackathon is to build the most creative and innovative Semantic Search application using Cohere API.

Prizes and Awards

๐Ÿฅ‡ $5000 API credits + $1000 cash
๐Ÿ† $4000 API credits + $1300 cash to be distributed among the finalists

Additionally, all winning teams will receive exclusive Cohere Swag and have the chance to: Meet Cohere Founder for a virtual coffee & record a video of their demo that will be promoted on Cohere's channels!

What is Semantic Search?

Language models give computers the ability to search by meaning and go beyond searching by matching keywords. This capability is called semantic search.

โšก Powering a Private Search Engine

Popular use case of semantic search is building a next generation web search engine. Impressive, but the applications of semantic search go beyond that! They can empower a private search engine for internal documents or records. They can be used to power features like StackOverflow's "similar questions" feature. And you can build many more things with it.

๐Ÿ–น Semantic Search and Text Sources

Semantic search is the most successful with text sources where the answer to a query is likely to be in a single, concrete paragraph, such as technical documentation or wikis which are organized as a list of instructions or facts.

๐Ÿ—๏ธ Build a State of the Art Application

Get the creative juices flowing and build a state of the art application of semantic search we havenโ€™t seen yet!

Semantic Search Sandbox + Resources

We encourage you to explore semantic search with Basic Semantic Search notebook, Cohereโ€™s docs and Toy Semantic Search sandbox. Sandbox is a collection of experimental, open-source GitHub repositories by Cohere that make building applications for developers fast and easy, regardless of ML experience.

Multilingual Semantic Search

Text embeddings are a central component in machine language understanding. They are numeric representations of text (be it a document, an email, or even a sentence). An embedding model translates text into a list of numbers that capture its meaning. A multilingual embedding model is able to do that well for many languages.

This video demonstrates Cohere's multilingual embedding model, and its ability to represent many languages.

๐Ÿ‘‰ Check out multilingual model Github repo

Cohere AI Hackathon

API Access

Signup for API Access

Cohere's API is currently free-to-use for everyone. Sign up for Cohere and start integrating NLP into your builds now!

Semantic Search Hackathon details

Join lablab and Cohere for a week to innovate and build the new generation of NLP powered applications. Find all the relevant details below.

๐Ÿ—“๏ธ Where and when

The hackathon starts on December 16th and ends on December 23rd. Over the weekend, you'll have the opportunity to learn from Cohere and lablab experts during workshops, keynotes, and mentoring sessions. The hackathon will take place on the lablab.ai platform.

๐Ÿฆธ๐Ÿผโ€โ™‚๏ธ Who should participate?

Previous experience in AI is not required but welcomed. While many participants are industry experts, we also welcome people with other types of domain knowledge that want to understand & explore how AI can be used in their fields.

๐Ÿ” Access to Cohere API

To get started with Cohere NLP API please signup using the following link: https://cohere.ai/signup. Your trial API key are free and has and can handle up to 100 calls per minute free of charge. You can find more information about the API here.

๐Ÿ˜… How about teams?

If you donโ€™t have a team you will be able to match and team up with other participants around the world. Finding & creating teams can be done from the dashboard you can access after you enroll. We also recommend checking our Discord server to find teammates and discuss ideas. You can join it here

๐Ÿ› ๏ธ How to participate in the hackathon

The hackathon will take place online on lablab.ai platform and lablab.ai Discord Server. Please register for both in order to participate. To participate click the "Enroll" button at the bottom of the page and read our Hackathon Guidelines.

๐Ÿง  Get prepared

To get prepared for the hackathon, we recommend you to start at our Cohere technology page where you can find all the relevant information about the API and how to use it plus cohere tutorials and cohere boilerplates.

Applications build on Cohere

Learn about the winning projects from previous episodes of the Cohere hackathons.

Perfect Prompt

Prompt engineering and image generation tool.

Tip of my Tongue

An application that improves the efficiency of chat-based support systems.

Turing Test

A tool for finding and browsing books based on short prompts, keywords, or story plot.

Speakers, Mentors and Organizers

  • Nick Frosst
    Speaker

    Nick Frosst

    Co-Founder at Cohere

  • Nils Reimers
    Speaker

    Nils Reimers

    Director of Machine Learning at Cohere

  • Sandra Kublik
    Speaker

    Sandra Kublik

    Developer Relations at Cohere

  • Luis Serrano
    Speaker

    Luis Serrano

    Author of Grokking Machine Learning | AI scientist and communicator

  • Amr Kayid
    Speaker

    Amr Kayid

    Research Engineer

  • Mathias Asberg
    Speaker

    Mathias Asberg

    Founder New Native

  • Paweล‚ Czech
    Speaker

    Paweล‚ Czech

    Founder New Native

  • Ervin Moore
    Mentor

    Ervin Moore

    PhD Student in Computer Science

  • Omar Atef Sesa
    Mentor

    Omar Atef Sesa

    Teaching Assistant || Machine Learning Eng. & Researcher || AI | ML

  • Skander Karoui
    Mentor

    Skander Karoui

    Telecommunications Engineering

  • Arjun Patel
    Mentor

    Arjun Patel

    Data Scientist | Origami Artist

  • Elizabeth Marchuk
    Organizer

    Elizabeth Marchuk

    Social Media Specialist at New Native

  • Anastasiia Strakhova
    Organizer

    Anastasiia Strakhova

    Community Manager at New Native

  • Olesia Zinchenko
    Organizer

    Olesia Zinchenko

    Marketing Manager at NewNative

Hackathon FAQ

Who can join the Hackathon?

We welcome domain experts from all industries, not just AI or tech. Successful AI solutions require a combination of technical expertise and domain knowledge. Coding experience is recommended.

Do I need a team?

You are welcome to join as a team or solo, if solo. We encourage you to look for a team before the event. We recommend you to join the Deep Learning Labs Discord channel: https://discord.gg/gCuBwBB35k and posting in the #looking-for-team channel to get to know your potential future team members.

Do I need a Github account?

It is recommended, that at least one team member has a Github account. You can create one for free if you don't already have one.

I have other questions.

Feel free to reach us on social media, or through our Discord channel.

Event Schedule

  • To be announced

Winner Submissions ๐Ÿ†

Submitted concepts, prototypes and pitches

Submissions from the teams participating in the Semantic Search AI Hackathon event and making it to the end ๐Ÿ‘Š

Project Eval

Project Eval

Eval aims to address the problem of subjectively evaluating test answers. Traditionally, this task has been carried out manually by human graders, which can be time-consuming and prone to bias. To address this issue, the project utilizes Cohere powered APIs to automate the evaluation process. The use of Cohere APIs allows for the integration of advanced natural language processing techniques, enabling the system to accurately understand and analyze the content of test answers. The custom model built upon these APIs then scores the answers based on suitable metrics, which can be tailored to the specific requirements of the test or assessment. One potential application of this technology is in the field of education, where it could be used to grade assignments or exams in a more efficient and unbiased manner. It could also be utilized in professional settings for evaluating job applications or performance evaluations. In addition to increasing efficiency and reducing bias, the use of automated evaluation techniques has the potential to provide more consistent and reliable scoring. This can help to ensure that test-takers receive fair and accurate assessments of their knowledge and skills. The model for the same was evaluated based on 4 major metrics: - Semantic Search: this is the primary scoring strategy of Eval. It is used to semantically understand the answer given and evaluate based on content rather than simply scoring based on textual similarities. Cohere Embed was used to generate embeddings for 5 suggested answers for the question and the answer to be checked. Then we find the distance from the nearest neighbor out of the 5 suggestions and the answer. This distance is used to grade the answer. - Duplication Check: partially correct answers with duplication of text tended to get higher similarity scores compared to the ones without duplication. To stop students from using this exploit to gain extra marks, a duplication checker was implemented based on Jaccard-Similarity between sentences within the answer. - Grammar Check: this strategy aims to check the grammar of the answer and assign a score based on the number of grammatical errors. We used Cohere Generate endpoint to generate a grammatically correct version of the answer, then check for cosine similarity of the generated version with original version to check if the original version was grammatically correct. - Toxicity Check: this aims to detect for toxic content in the answer and penalize an answer if it is toxic. We trained a custom classification model on Cohere using the Social Media Toxicity Dataset by SurgeAI which gave a 98% precision on the test split. We also implemented a Custom Checks which allows users to give different weights to each of the three different metrics based on how important they are for the evaluation of the answer. This allows for a more personalized evaluation of the answer. We built our custom model into a Flask-based REST API server deployed on Replit to streamline usage and allow people to access the full-functionality of the model. We also built a highly interactive UI that allows for users to easily interact with the API and evaluate their answers as well as submit questions.

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Band of Agents Hackathon

Next Hackathon

Band of Agents Hackathon

Starts Jun 12, 2026

Join this hackathon โ†’