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Daniel Vila

dvilasuero

https://argilla.io

AI & ML interests

RLHF, RLAIF, DPO, data, data, data

Recent Activity

updated a dataset 4 days ago

data-is-better-together/fineweb-c-progress

liked a dataset 4 days ago

m-a-p/FineFineWeb

updated a dataset 4 days ago

dvilasuero/product-reviews-labelled

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Articles

Argilla 2.4: Easily Build Fine-Tuning and Evaluation datasets on the Hub — No Code Required

Nov 4

• 41

How to build a custom text classifier without days of human labeling

Oct 17

• 55

How to optimize your data labelling project with custom interfaces

Oct 16

• 18

🔥 Argilla 2.0: the data-centric tool for AI makers 🤗

Jul 30

• 37

Llama 3.1 - 405B, 70B & 8B with multilinguality and long context

Jul 23

• 224

How we leveraged distilabel to create an Argilla 2.0 Chatbot

Jul 16

• 32

Ethics and Society Newsletter #6: Building Better AI: The Importance of Data Quality

Jun 24

• 33

🦙⚗️ Using Llama3 and distilabel to build fine-tuning datasets

Jun 4

• 73

Data is better together

Mar 4

• 8

Organizations

Posts 12

Post

2257

🌐 Announcing Global-MMLU: an improved MMLU Open dataset with evaluation coverage across 42 languages, built with Argilla and the Hugging Face community.

Global-MMLU is the result of months of work with the goal of advancing Multilingual LLM evaluation. It's been an amazing open science effort with collaborators from Cohere For AI, Mila - Quebec Artificial Intelligence Institute, EPFL, Massachusetts Institute of Technology, AI Singapore, National University of Singapore, KAIST, Instituto Superior Técnico, Carnegie Mellon University, CONICET, and University of Buenos Aires.

🏷️ +200 contributors used Argilla MMLU questions where regional, dialect, or cultural knowledge was required to answer correctly. 85% of the questions required Western-centric knowledge!

Thanks to this annotation process, the open dataset contains two subsets:

1. 🗽 Culturally Agnostic: no specific regional, cultural knowledge is required.
2. ⚖️ Culturally Sensitive: requires dialect, cultural knowledge or geographic knowledge to answer correctly.

Moreover, we provide high quality translations of 25 out of 42 languages, thanks again to the community and professional annotators leveraging Argilla on the Hub.

I hope this will ensure a better understanding of the limitations and challenges for making open AI useful for many languages.

Dataset: CohereForAI/Global-MMLU

Post

1105

@Jesse-marqo and the Marqo team are killing it on the Hub: top embedding models and datasets!

Here's how to start using their new evaluation dataset for curation and labelling:

1. Deploy Argilla on Spaces: https://huggingface.co./new-space?template=argilla%2Fargilla-template-space
2. Load Marqo/amazon-products-eval with the UI wizard.
3. Start curating!

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