Why am I not qualified for this round.

Can you please check again? The qualifying criteria for Round 1b has now been removed, so you should be able to make a submission now.

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The team's challenge has team-freeze in effect

Hi ryo2,

This issue has been fixed. Can you please try again.

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📹 Office Hour #1 Recording and Slides | Using Llama 3 Models

Hello all,

Thank you to everyone who joined the Office Hour for the Meta Comprehensive RAG (CRAG) Challenge. If you missed the live session, you can still engage by watching the recording and reviewing the slide deck.

Recording Available: Catch up on the session by watching the recording here.

Slide Deck: Access the presentation slides here.

Using Llama 3 Models: Participants can use Llama 3 Models to build their RAG solutions. Llama 3 models can be downloaded here.

This session featured detailed discussions with our experts Xiao Yang and Kai Sun, covering the CRAG benchmarks, updates on the challenge, and a live Q&A.

Feel free to share your thoughts or questions in the comments!

Warm regards,
Team AIcrowd

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‼️ Imp: Challenge Updates [24 April 2024]

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‼️ Imp: Challenge Updates [24 April 2024]

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‼️ Imp: Challenge Updates [24 April 2024]

Hello everyone,

Here are some updates for the challenge:

• We added the “query_time” to the generate_answer function as an input.
• We’ve corrected an error in the “number of days left” displayed on the main page; it will now accurately show that the challenge ends on 5/20/2024.
• We are working on a batch inference solution and will share it soon. During the interim, we will increase the time-out limit for each example to 30s. Please note we might reduce this limit to a smaller number when the batch inference solution settles.

Thank you for your participation!
Team CRAG

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Why am I not eligible to participate?

Have you clicked on the participate button and accepted the challenge rules?

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Why am I not eligible to participate?

You should be able to make a submission now.

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CS233-2024-FashionMNIST

We will be competing for who gets the best accuracy and F1 score on the FashionMNIST dataset. Your ranking will not be used for grading, however you are required to participate. Let’s see how your models do compared to the others!

You will be uploading your results file in the “submissions” tab of the challenge. Everyone in the team gets 5 submissions per day. Winners get chocolates!

Note: Before you save test dataset predictions, print out the shape and make sure it is of shape (N,) (and not (N,1) etc.)

Good luck and have fun!

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EPFLCSS-233-FashionMNIST

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EPFLCSS-233-FashionMNIST

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EPFLCSS-233-FashionMNIST

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About the Task 1: Predicting Room Indoor Temperature category

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About the Predicting Temperature And Power Consumption In An category

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About the Task 2: Predicting Energy consumption category

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📹 Office Hour #2 Recording

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📹 Office Hour #2 Recording

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📹 Office Hour #2 Recording

Hello all,

Thank you to everyone who joined the Office Hour for the Multi-Task Online Shopping Challenge for LLMs. If you missed the live session check out the recording here .

Recording Available: Watch the session by watching the recording here .

This session featured discussions with our expert, Yilun Jin, covering the info on phase 2 and updates of the challenge, along with a live Q&A.

Feel free to share your thoughts or questions in the comments!

Warm regards,
Team AIcrowd

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💻 Office Hour: May 1, 2024, Wednesday 10AM CST

Hello all,

We invite you to join the next Office Hour for the Amazon KDD Cup 2024. This session provides an excellent opportunity to engage directly with the organizers, explore the intricacies of the challenge, and get your questions answered.

May 1, 2024, Wednesday 4AM CET/10AM CST
Join the Office Hour on Zoom

For those unable to attend, we will provide a recording of the session. Feel free to post your questions here beforehand, and we will address them during the office hour.

Office Hour Highlights:

• Direct engagement with the organizer
• Live Q&A session
• Opportunity to share your feedback

Meet the Speaker:

Yilun Jin: PhD student at the Hong Kong University of Science and Technology and former intern at the Amazon Rufus team. Yilun is the main curator of the ShopBench dataset and has conducted extensive experiments on it.

If you can’t attend, leave your questions in the comments, and the organizers will address them during the session.

Mark your calendars, prepare your questions, and join us live for the Office Hour.

Looking forward to seeing you there!
Team Amazon KDD Cup 2024

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📹 Office Hour #1 Recording

Hello all,

Thank you to everyone who joined the Office Hour for the Multi-Task Online Shopping Challenge for LLMs. If you missed the live session, you can still engage by watching the recording here.

Recording Available: Catch up on the session by watching the recording here.

Pick Your Time: For our next office hour, please share your preferred timing in the comments below!

This session featured detailed discussions with our expert, Yilun Jin, covering the basics and updates of the challenge, along with a live Q&A.

Feel free to share your thoughts or questions in the comments!

Warm regards,
Team AIcrowd

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