Why choose between strong LLM reasoning and efficient models?
Use DeepSeek to generate high-quality training data, then distil that knowledge into ModernBERT answerdotai/ModernBERT-base for fast, efficient classification.
If you haven't seen yet, we just released Inference Providers 🔀
> 4 new serverless inference providers on the Hub 🤯 > Use your HF API key or personal key with all providers 🔑 > Chat with Deepseek R1, V3, and more on HF Hub 🐋 > We support Sambanova, TogetherAI, Replicate, and Fal.ai 💪
Best of all, we don't charge any markup on top of the provider 🫰 Have you tried it out yet? HF Pro accounts get $2 of free usage for the provider inference.
Given an input image, it generates several queries along with explanations to justify them. This approach can generate synthetic data for fine-tuning ColPali models.
The Hugging Face community has rated educational content in languages spoken by 1.6 billion people! New additions: • Japanese • Italian • Old High German
We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub.
FineWeb2 is a massive multilingual dataset for pre-training language models. Like any web-scale dataset, it contains low-quality content. How can we improve it?
Over the past months, an amazing community of 400+ annotators has been labelling content quality (using Argilla) across 23 languages through the FineWeb-C initiative.
Today, I'm happy to share the first classifier trained on this data.
🔍 What we've built:
- A lightweight classifier that efficiently removes low-quality content - 90%+ precision demonstrated on Danish & Swedish - Can process the 43M+ documents in Danish FineWeb2 with minimal compute
🌍 Why this matters: The approach can be reproduced for any of the 23 languages in FineWeb-C (data-is-better-together/fineweb-c). We can improve training data quality at scale without massive compute resources by starting with community annotations and training small, efficient classifiers.
This week a few more languages have got 1,000 annotations for the educational quality of data from HuggingFaceFW/fineweb-2.
Why should you care?
The quality of pre-training data can have a big impact on the performance of downstream language models trained on that data (HuggingFaceFW/blogpost-fineweb-v1).
Being able to filter by educational quality is on way of improving the quality of the data you use for training an LLM. Very importantly this approach can also reduce the amount of data needed for pertaining.
Why not use an LLM?
LLMs can be used to annotate educational quality for a subset of data. This data can then be used to train a smaller encoder only model to label the full dataset. However, this may not work well for languages outside of english. This is where fineweb-c (community) comes in.
The community is annotating the educational quality of fineweb2 data. Currently 114 languages have some annotations. These annotations will enable a number of things:
- Evaluate whether an LLM can label the educational quality for texts in that language well - Directly be used for training quality classifiers - Help discover other rules and huerisitcs for refining fineweb2 further for different languages.
Community fine-tuned models are more carbon efficient than the models they are derived from! 🥳🌿
@alozowski@clefourrier@SaylorTwift@albertvillanova evaluated CO₂ emissions associated with model inference for over 3000 models on the Open LLM Leaderboard. Interesting trends and new insights emerged...👀
All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub 💪
How do your annotations for FineWeb2 compare to your teammates'?
I started contributing some annotations to the FineWeb2 collaborative annotation sprint and I wanted to know if my labelling trends were similar to those of my teammates.
I did some analysis and I wasn't surprised to see that I'm being a bit harsher on my evaluations than my mates 😂
Do you want to see how your annotations compare to others? 👉 Go to this Gradio space: nataliaElv/fineweb2_compare_my_annotations ✍️ Enter the dataset that you've contributed to and your Hugging Face username.
