FineWeb2-C: Help Build Better Language Models in Your Language
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10
Daniel Vila
dvilasuero
AI & ML interests
RLHF, RLAIF, DPO, data, data, data
Recent Activity
updated
a dataset
5 days ago
data-is-better-together/fineweb-c-progress
liked
a dataset
5 days ago
m-a-p/FineFineWeb
updated
a dataset
5 days ago
dvilasuero/product-reviews-labelled
Articles
Organizations
dvilasuero's activity
reacted to
burtenshaw's
post with 🤗❤️
6 days ago
Post
2540
People are flexing their end of year stats, so I made this app to show hub stats in a tidy design!
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
- 1 reply
reacted to
davidberenstein1957's
post with 🔥
9 days ago
Post
4106
Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs). The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code.
Blog: https://huggingface.co/blog/synthetic-data-generator
Space: argilla/synthetic-data-generator
Blog: https://huggingface.co/blog/synthetic-data-generator
Space: argilla/synthetic-data-generator
- 4 replies
reacted to
burtenshaw's
post with 🔥❤️
15 days ago
Post
2386
Quick update from week 1 of smol course. The community is taking the driving seat and using the material for their own projects. If you want to do the same, join in!
- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.
⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR
REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA
- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.
⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR
REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA
reacted to
nataliaElv's
post with 👀
19 days ago
Post
1177
We're so close to reaching 100 languages! Can you help us cover the remaining 200? Check if we're still looking for language leads for your language:
nataliaElv/language-leads-dashboard
posted
an
update
19 days ago
Post
2259
🌐 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
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
reacted to
burtenshaw's
post with ❤️
22 days ago
Post
2565
For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. It’s not big like Li Yin and
@mlabonne
, it’s just smol.
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
reacted to
davidberenstein1957's
post with 👀❤️🔥
23 days ago
Post
1568
🔥 Dataset Drop - Open Image Preferences
BlackForest Labs Flux Dev VS. Stability AI Stable Diffusion Large 3.5
Together with the data-is-better-together community, we've worked on an Apache 2.0 licensed open image preference dataset based on the fal ai imgsys prompts dataset. Thanks to the awesome community, we have managed to get 5K preference pairs in less than 2 days. The annotation alignment among annotators is great too.
Aashish Kumar won a month of Hugging Face Pro by making the most contributions! Congrats from the entire team 🥇
The best thing?! We are not done yet! Let's keep the annotations coming for 5K more in the second part of the sprint! (with more prices to go around).
Dataset: https://huggingface.co/datasets/data-is-better-together/image-preferences-results
BlackForest Labs Flux Dev VS. Stability AI Stable Diffusion Large 3.5
Together with the data-is-better-together community, we've worked on an Apache 2.0 licensed open image preference dataset based on the fal ai imgsys prompts dataset. Thanks to the awesome community, we have managed to get 5K preference pairs in less than 2 days. The annotation alignment among annotators is great too.
Aashish Kumar won a month of Hugging Face Pro by making the most contributions! Congrats from the entire team 🥇
The best thing?! We are not done yet! Let's keep the annotations coming for 5K more in the second part of the sprint! (with more prices to go around).
Dataset: https://huggingface.co/datasets/data-is-better-together/image-preferences-results
reacted to
elliesleightholm's
post with 🔥🤗
about 1 month ago
Post
2765
I made a beginners guide to Hugging Face Spaces 🤗 I hope it's useful to some of you :)
YouTube video: https://www.youtube.com/watch?v=xqdTFyRdtjQ
Blog: https://www.marqo.ai/blog/how-to-create-a-hugging-face-space
YouTube video: https://www.youtube.com/watch?v=xqdTFyRdtjQ
Blog: https://www.marqo.ai/blog/how-to-create-a-hugging-face-space
- 8 replies
reacted to
andito's
post with ❤️
about 1 month ago
Post
1082
Hugging face presents FineVideo 🎥! Unlocking the next generation of Video understanding 🚀
🤯3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
🔥
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. ⚡️
Explore the videos: HuggingFaceFV/FineVideo-Explorer
🤯3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
🔥
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. ⚡️
Explore the videos: HuggingFaceFV/FineVideo-Explorer
reacted to
singhsidhukuldeep's
post with 👍👀
about 1 month ago
Post
1259
Sorry judge, my lawyer hallucinated? 😂 If you get an AI lawyer, you would want it to be hallucination-free!
New @Stanford -@Yale research reveals surprising findings about leading AI legal research tools. Here's what you need to know:
>> Key Findings
The study tested LexisNexis (Lexis+ AI), Thomson Reuters (Westlaw AI & Ask Practical Law AI), and GPT-4, finding hallucination rates between 17-33% despite claims of being "hallucination-free".
>> Technical Deep Dive
The research evaluated these tools using Retrieval-Augmented Generation (RAG) architecture, which operates in two crucial steps:
1. Retrieval System:
- Uses neural text embeddings to capture semantic meaning
- Employs both lexical and semantic search mechanisms
- Implements document filtering and extraction
- Retrieves relevant legal documents from vast databases
2. Generation Pipeline:
- Processes retrieved documents alongside original queries
- Synthesizes information from multiple legal sources
- Generates responses based on retrieved context
- Includes citation verification mechanisms
>> Performance Breakdown:
- Lexis+ AI: 65% accuracy rate
- Westlaw AI: 42% accuracy rate
- Ask Practical Law AI: Over 60% incomplete answers
>> Why This Matters
This research exposes critical vulnerabilities in AI legal tools that lawyers increasingly rely on. It's essential for legal professionals to understand these limitations when incorporating AI into their practice.
New @Stanford -@Yale research reveals surprising findings about leading AI legal research tools. Here's what you need to know:
>> Key Findings
The study tested LexisNexis (Lexis+ AI), Thomson Reuters (Westlaw AI & Ask Practical Law AI), and GPT-4, finding hallucination rates between 17-33% despite claims of being "hallucination-free".
>> Technical Deep Dive
The research evaluated these tools using Retrieval-Augmented Generation (RAG) architecture, which operates in two crucial steps:
1. Retrieval System:
- Uses neural text embeddings to capture semantic meaning
- Employs both lexical and semantic search mechanisms
- Implements document filtering and extraction
- Retrieves relevant legal documents from vast databases
2. Generation Pipeline:
- Processes retrieved documents alongside original queries
- Synthesizes information from multiple legal sources
- Generates responses based on retrieved context
- Includes citation verification mechanisms
>> Performance Breakdown:
- Lexis+ AI: 65% accuracy rate
- Westlaw AI: 42% accuracy rate
- Ask Practical Law AI: Over 60% incomplete answers
>> Why This Matters
This research exposes critical vulnerabilities in AI legal tools that lawyers increasingly rely on. It's essential for legal professionals to understand these limitations when incorporating AI into their practice.
reacted to
prithivMLmods's
post with ❤️🤗
about 1 month ago
Post
2029
🍅 Glif App's Remixes feature allows you to slap a logo onto anything, seamlessly integrating the input image (logo) into various contexts. The result is stunning remixes that blend the input logo with generated images (img2img logo mapping) for incredible outcomes.
Check out Any Logo Anywhere remixes on Glif: [Glif Remixes](https://glif.app/glifs/cm3o7dfsd002610z48sz89yih/remixes)
🌐The browser extension enables thousands of Glif-based img2img workflows on any image you find online. Experience Glif Remix with WebAI: [Chrome Extension](https://chromewebstore.google.com/detail/glif-remix-the-web-with-a/abfbooehhdjcgmbmcpkcebcmpfnlingo)
.
.
.
🤗Have fun with the cool stuff !!
@prithivMLmods
Check out Any Logo Anywhere remixes on Glif: [Glif Remixes](https://glif.app/glifs/cm3o7dfsd002610z48sz89yih/remixes)
🌐The browser extension enables thousands of Glif-based img2img workflows on any image you find online. Experience Glif Remix with WebAI: [Chrome Extension](https://chromewebstore.google.com/detail/glif-remix-the-web-with-a/abfbooehhdjcgmbmcpkcebcmpfnlingo)
.
.
.
🤗Have fun with the cool stuff !!
@prithivMLmods
posted
an
update
about 1 month ago
Post
1106
@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!
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!
posted
an
update
about 2 months ago
Post
683
Build datasets for AI on the Hugging Face Hub—10x easier than ever!
Today, I'm excited to share our biggest feature since we joined Hugging Face.
Here’s how it works:
1. Pick a dataset—upload your own or choose from 240K open datasets.
2. Paste the Hub dataset ID into Argilla and set up your labeling interface.
3. Share the URL with your team or the whole community!
And the best part? It’s:
- No code – no Python needed
- Integrated – all within the Hub
- Scalable – from solo labeling to 100s of contributors
I am incredibly proud of the team for shipping this after weeks of work and many quick iterations.
Let's make this sentence obsolete: "Everyone wants to do the model work, not the data work."
Read, share, and like the HF blog post:
https://huggingface.co/blog/argilla-ui-hub
Today, I'm excited to share our biggest feature since we joined Hugging Face.
Here’s how it works:
1. Pick a dataset—upload your own or choose from 240K open datasets.
2. Paste the Hub dataset ID into Argilla and set up your labeling interface.
3. Share the URL with your team or the whole community!
And the best part? It’s:
- No code – no Python needed
- Integrated – all within the Hub
- Scalable – from solo labeling to 100s of contributors
I am incredibly proud of the team for shipping this after weeks of work and many quick iterations.
Let's make this sentence obsolete: "Everyone wants to do the model work, not the data work."
Read, share, and like the HF blog post:
https://huggingface.co/blog/argilla-ui-hub