Got Compute, but No Data: Lessons From Post-training a Finnish LLM
Paper
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2503.09407
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Published
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1
AI & ML interests
Web as a corpus, Large Language Models, Machine Translation, Language Technologies, Natural Language Processing, Internet Archive, CommonCrawl
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Villekom
authored
2
papers
1 day ago
Adding `safetensors` variant of this model
#1 opened about 2 months ago
by
SFconvertbot
MariaFjodorowa
updated
6
models
11 days ago
HPLT/hplt_bert_base_2_0_zho-Hans
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36
HPLT/hplt_bert_base_2_0_vie-Latn
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HPLT/hplt_bert_base_2_0_ukr-Cyrl
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0.1B
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HPLT/hplt_bert_base_2_0_tur-Latn
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HPLT/hplt_bert_base_2_0_tat-Cyrl
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61
HPLT/hplt_bert_base_2_0_swe-Latn
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35
davanstrien
posted
an
update
20 days ago
Post
2817
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
tiedeman
authored
a
paper
25 days ago
jisx
authored
8
papers
26 days ago
Towards Interpretable Mental Health Analysis with Large Language Models
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2304.03347
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Published
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
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2002.00388
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Published
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1
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
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2004.07601
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Published
A New Massive Multilingual Dataset for High-Performance Language Technologies
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2403.14009
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Published
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1
Lucky 52: How Many Languages Are Needed to Instruction Fine-Tune Large Language Models?
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2404.04850
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Published
GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models
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2504.04155
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Published
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1
Rethinking Multilingual Continual Pretraining: Data Mixing for Adapting LLMs Across Languages and Resources
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2504.04152
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Published
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1
Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data
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2506.00469
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Published
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2
BramVanroy
posted
an
update
about 2 months ago
Post
3251
📢💾 Introducing the Common Crawl Creative Commons Corpus (C5)!
C5 is a large-scale effort to heavily filter web-crawled data, as collected by the non-profit Common Crawl, to only documents that are Creative Commons-licensed such as cc-by-4.0 or public domain cc0. At this stage 150 billion tokens have been collected.
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📄 data: BramVanroy/CommonCrawl-CreativeCommons
🧰 software: https://github.com/BramVanroy/CommonCrawl-CreativeCommons
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</> To build C5, HTML pages are scrutinized and all links (if any) to CC licenses are collected, both in regular hyperlinks as well as in metadata. Additional data fields are included such as "was the license found in the
🌐 In this first version of C5, 8 languages are included (Afrikaans, German, English, French, Frysian, Italian, Dutch and Spanish). The language set was limited for two reasons: computational and storage limitations, and a collaboration with GPT-NL, which requested CC data for these languages to train a Dutch-focused, copyright-conscious LLM. In total, this V1 release contains almost 150 thousand documents and 150 billion tokens. This data was not filtered on quality nor deduplicated so that you can decide for yourself how much data to keep. To give some quality indication, a dataset field is present to describe whether a document is included in the FineWeb(-2) datasets, which are of high quality.
🔍 More work needs to be done! Only 7 out of 100+ Common Crawl crawls have been processed so far. That's encouraging because it means there is a lot more Creative Commons data to be collected! But to get there I need help in terms of compute. The current processing was already heavily sponsored by the Flemish Supercomputer but more is needed. If you have the compute available and which to collaborate in an open and transparent manner, please get in touch!
C5 is a large-scale effort to heavily filter web-crawled data, as collected by the non-profit Common Crawl, to only documents that are Creative Commons-licensed such as cc-by-4.0 or public domain cc0. At this stage 150 billion tokens have been collected.
---
📄 data: BramVanroy/CommonCrawl-CreativeCommons
🧰 software: https://github.com/BramVanroy/CommonCrawl-CreativeCommons
---
</> To build C5, HTML pages are scrutinized and all links (if any) to CC licenses are collected, both in regular hyperlinks as well as in metadata. Additional data fields are included such as "was the license found in the
head?" or "if multiple licenses were found, do they contradict each other?", which makes further filtering a breeze. 🌐 In this first version of C5, 8 languages are included (Afrikaans, German, English, French, Frysian, Italian, Dutch and Spanish). The language set was limited for two reasons: computational and storage limitations, and a collaboration with GPT-NL, which requested CC data for these languages to train a Dutch-focused, copyright-conscious LLM. In total, this V1 release contains almost 150 thousand documents and 150 billion tokens. This data was not filtered on quality nor deduplicated so that you can decide for yourself how much data to keep. To give some quality indication, a dataset field is present to describe whether a document is included in the FineWeb(-2) datasets, which are of high quality.
🔍 More work needs to be done! Only 7 out of 100+ Common Crawl crawls have been processed so far. That's encouraging because it means there is a lot more Creative Commons data to be collected! But to get there I need help in terms of compute. The current processing was already heavily sponsored by the Flemish Supercomputer but more is needed. If you have the compute available and which to collaborate in an open and transparent manner, please get in touch!
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- 1 reply