Comparative analysis of optical character recognition methods for Sámi texts from the National Library of Norway
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Yngvil
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20 days ago
versae
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2 months ago
davanstrien
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an
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3 months ago
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1377
I fine-tuned a smol VLM to generate specialized art history metadata!
https://huggingface.co/davanstrien/iconclass-vlm: Qwen2.5-VL-3B trained using SFT to generate ICONCLASS codes (think Dewey Decimal for art!)
Trained with TRL + HF Jobs - single UV script, no GPU needed!
Space to explore predictions on a test set: davanstrien/iconclass-predictions
Blog soon!
https://huggingface.co/davanstrien/iconclass-vlm: Qwen2.5-VL-3B trained using SFT to generate ICONCLASS codes (think Dewey Decimal for art!)
Trained with TRL + HF Jobs - single UV script, no GPU needed!
Space to explore predictions on a test set: davanstrien/iconclass-predictions
Blog soon!
davanstrien
posted
an
update
6 months ago
Post
3654
Inspired by Hugging Face's official MCP server, I've developed a complementary tool that exposes my semantic search API to enhance discovery across the HF platform.
Key capabilities:
- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation
The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.
Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)
https://github.com/davanstrien/hub-semantic-search-mcp
Key capabilities:
- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation
The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.
Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)
https://github.com/davanstrien/hub-semantic-search-mcp
- 1 reply
davanstrien
posted
an
update
8 months ago
Post
2371
Came across a very nice submission from
@marcodsn
for the reasoning datasets competition (https://huggingface.co/blog/bespokelabs/reasoning-datasets-competition).
The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:
- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model
It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.
I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.
Dataset can be found here: marcodsn/academic-chains (give it a like!)
The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:
- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model
It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.
I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.
Dataset can be found here: marcodsn/academic-chains (give it a like!)
titae
authored
a
paper
8 months ago
davanstrien
posted
an
update
8 months ago
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1767
I've created a v1 dataset (
davanstrien/reasoning-required) and model (
davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
davanstrien
posted
an
update
10 months ago
Post
3003
📊 Introducing "Hugging Face Dataset Spotlight" 📊
I'm excited to share the first episode of our AI-generated podcast series focusing on nice datasets from the Hugging Face Hub!
This first episode explores mathematical reasoning datasets:
- SynthLabsAI/Big-Math-RL-Verified: Over 250,000 rigorously verified problems spanning multiple difficulty levels and mathematical domains
- open-r1/OpenR1-Math-220k: 220,000 math problems with multiple reasoning traces, verified for accuracy using Math Verify and Llama-3.3-70B models.
- facebook/natural_reasoning: 1.1 million general reasoning questions carefully deduplicated and decontaminated from existing benchmarks, showing superior scaling effects when training models like Llama3.1-8B-Instruct.
Plus a bonus segment on bespokelabs/bespoke-manim!
https://www.youtube.com/watch?v=-TgmRq45tW4
I'm excited to share the first episode of our AI-generated podcast series focusing on nice datasets from the Hugging Face Hub!
This first episode explores mathematical reasoning datasets:
- SynthLabsAI/Big-Math-RL-Verified: Over 250,000 rigorously verified problems spanning multiple difficulty levels and mathematical domains
- open-r1/OpenR1-Math-220k: 220,000 math problems with multiple reasoning traces, verified for accuracy using Math Verify and Llama-3.3-70B models.
- facebook/natural_reasoning: 1.1 million general reasoning questions carefully deduplicated and decontaminated from existing benchmarks, showing superior scaling effects when training models like Llama3.1-8B-Instruct.
Plus a bonus segment on bespokelabs/bespoke-manim!
https://www.youtube.com/watch?v=-TgmRq45tW4
davanstrien
posted
an
update
10 months ago
Post
3746
Quick POC: Turn a Hugging Face dataset card into a short podcast introducing the dataset using all open models.
I think I'm the only weirdo who would enjoy listening to something like this though 😅
Here is an example for eth-nlped/stepverify
I think I'm the only weirdo who would enjoy listening to something like this though 😅
Here is an example for eth-nlped/stepverify
- 2 replies
davanstrien
posted
an
update
10 months ago
Post
2705
Hacked together a way to log trl GRPO training completions to a 🤗 dataset repo. This allows you to:
- Track rewards from multiple reward functions
- Treat the completion and rewards from training as a "proper" dataset and do EDA
- Share results for open science
The implementation is super hacky, but I'm curious if people would find this useful.
To push completions to the Hub, you just need two extra parameters:
Example dataset: davanstrien/test-logs
Colab: https://colab.research.google.com/drive/1wzBFPVthRYYTp-mEYlznLg_e_0Za1M3g
- Track rewards from multiple reward functions
- Treat the completion and rewards from training as a "proper" dataset and do EDA
- Share results for open science
The implementation is super hacky, but I'm curious if people would find this useful.
To push completions to the Hub, you just need two extra parameters:
log_completions=True
log_completions_hub_repo='your-username/repo-name'Example dataset: davanstrien/test-logs
Colab: https://colab.research.google.com/drive/1wzBFPVthRYYTp-mEYlznLg_e_0Za1M3g
davanstrien
posted
an
update
10 months ago
Post
2319
Dataset descriptions for trending Hugging Face datasets? Powered by a Smol model
davanstrien/Smol-Hub-tldr
davanstrien
posted
an
update
10 months ago
Post
2054
How do you make 1M+ Hugging Face models & datasets more discoverable?
davanstrien/Smol-Hub-tldr!
I fine-tuned HuggingFaceTB/SmolLM2-360M to generate one-line summaries from a model or dataset README.
Its own self-description?
"A model for generating concise summaries of model & dataset cards from the Hugging Face Hub"
The goal? Make it easier to find the right models and datasets for your specific needs. It's already powering a semantic search for datasets Space.
It's still a WIP but thanks to @loubnabnl , @anton-l , @eliebak et al, for cooking such a nice base model for fine-tuning small, efficient models for specific domains and tasks. 🙏
davanstrien/Smol-Hub-tldr!
I fine-tuned HuggingFaceTB/SmolLM2-360M to generate one-line summaries from a model or dataset README.
Its own self-description?
"A model for generating concise summaries of model & dataset cards from the Hugging Face Hub"
The goal? Make it easier to find the right models and datasets for your specific needs. It's already powering a semantic search for datasets Space.
It's still a WIP but thanks to @loubnabnl , @anton-l , @eliebak et al, for cooking such a nice base model for fine-tuning small, efficient models for specific domains and tasks. 🙏
davanstrien
posted
an
update
10 months ago
Post
1441
Made some significant updates to my 🤗 semantic datasets search app. If you love falling into a wiki black hole, you might like this...
https://huggingface.co/spaces/librarian-bots/huggingface-datasets-semantic-search
https://huggingface.co/spaces/librarian-bots/huggingface-datasets-semantic-search
davanstrien
posted
an
update
11 months ago
Post
1894
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.
Blog post: https://danielvanstrien.xyz/posts/2025/deepseek/distil-deepseek-modernbert.html
Use DeepSeek to generate high-quality training data, then distil that knowledge into ModernBERT answerdotai/ModernBERT-base for fast, efficient classification.
Blog post: https://danielvanstrien.xyz/posts/2025/deepseek/distil-deepseek-modernbert.html
davanstrien
posted
an
update
11 months ago
Post
2037
Updated the ColPali Query Generator Space
davanstrien/ColPali-Query-Generator to use
Qwen/Qwen2.5-VL-7B-Instruct.
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.
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.
davanstrien
posted
an
update
11 months ago
Post
2088
🌍 Big step for multilingual AI data!
The Hugging Face community has rated educational content in languages spoken by 1.6 billion people! New additions:
• Japanese
• Italian
• Old High German
Learn more and contribute: https://huggingface.co/blog/davanstrien/fineweb2-community
These ratings can help enhance training data for major world languages.
The Hugging Face community has rated educational content in languages spoken by 1.6 billion people! New additions:
• Japanese
• Italian
• Old High German
Learn more and contribute: https://huggingface.co/blog/davanstrien/fineweb2-community
These ratings can help enhance training data for major world languages.
- 1 reply
davanstrien
posted
an
update
11 months ago
Post
3108
Introducing scandi-fine-web-cleaner
davanstrien/scandi-fine-web-cleaner, the first model trained on FineWeb-C community annotations!
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.
Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html
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.
Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html
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- 1 reply
davanstrien
posted
an
update
11 months ago
Post
2349
The
data-is-better-together/fineweb-c dataset is growing!
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.
This week the following languages where done:
Swedish thanks to: @Lauler @AntonVic @ohallstrom @bjarlestam @menbom @Ekgren @apsod
Ukrainian thanks to: @hannayukhymenko @robinhad @realPivo @RabotiahovDmytro @reciprocate
Assamese thanks to: @moyoor97 @Arpanjyoti @nawaf-helmi123 @pahigogoi1 @aelhence @kishorekashyap
Want to learn more: https://huggingface.co/blog/davanstrien/fineweb2-community
Contribute yourself here: data-is-better-together/fineweb-c
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.
This week the following languages where done:
Swedish thanks to: @Lauler @AntonVic @ohallstrom @bjarlestam @menbom @Ekgren @apsod
Ukrainian thanks to: @hannayukhymenko @robinhad @realPivo @RabotiahovDmytro @reciprocate
Assamese thanks to: @moyoor97 @Arpanjyoti @nawaf-helmi123 @pahigogoi1 @aelhence @kishorekashyap
Want to learn more: https://huggingface.co/blog/davanstrien/fineweb2-community
Contribute yourself here: data-is-better-together/fineweb-c
- 1 reply
davanstrien
posted
an
update
12 months ago
Post
3401
🇸🇰 Hovorte po slovensky? Help build better AI for Slovak!
We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset ( data-is-better-together/fineweb-c) release!
Your contribution will help create better language models for 5+ million Slovak speakers.
Annotate here: data-is-better-together/fineweb-c.
Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community
We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset ( data-is-better-together/fineweb-c) release!
Your contribution will help create better language models for 5+ million Slovak speakers.
Annotate here: data-is-better-together/fineweb-c.
Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community
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- 3 replies
davanstrien
posted
an
update
12 months ago
Post
1872
Introducing FineWeb-C 🌐🎓, a community-built dataset for improving language models in ALL languages.
Inspired by FineWeb-Edu the community is labelling the educational quality of texts for many languages.
318 annotators, 32K+ annotations, 12 languages - and growing! 🌍
data-is-better-together/fineweb-c
Inspired by FineWeb-Edu the community is labelling the educational quality of texts for many languages.
318 annotators, 32K+ annotations, 12 languages - and growing! 🌍
data-is-better-together/fineweb-c