Special thanks to:
- The Common Crawl folks (Greg Lindahl, @pjox and others)
- The datatrove and FineWeb teams at Hugging Face ( @guipenedo and others)
Bram Vanroy
BramVanroy
AI & ML interests
Artificial intelligence, natural language processing, computational linguistics
Recent Activity
updated
a collection
about 7 hours ago
BLEURT
liked
a dataset
about 7 hours ago
common-pile/dolma-cccc
updated
a Space
about 8 hours ago
BramVanroy/mateo-demo
Organizations
BramVanroy's activity
replied to
their
post
1 day ago
posted
an
update
1 day ago
Post
1278
📢💾 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.
---
📄 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
🌐 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!
-
- 1 reply
reacted to
julien-c's
post with ❤️🔥
5 months ago
Post
10678
After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub
TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)
docs: https://huggingface.co/docs/hub/storage-limits
We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥
cc: @reach-vb @pierric @victor and the HF team
TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)
docs: https://huggingface.co/docs/hub/storage-limits
We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥
cc: @reach-vb @pierric @victor and the HF team
- 28 replies
posted
an
update
5 months ago
Post
997
In the spirit of "Better late than never", I've finally written a brief overview paper for GEITje 7B Ultra. Initially released 10 months ago (oops), but still reaching around 1300 monthly downloads across the HF ecosystem (not including ollama).
GEITje 7B Ultra: A Conversational Model for Dutch (2412.04092)
While the paper discusses the model a little bit, I especially wanted to write about the datasets, which to this day seem an important asset for Dutch LLM training (SFT and preference tuning). We have a long way to go for Dutch, but publishing transparent and reproducible artefacts seems an important step to me, alongside having open discussions about data, bias, architectures.
In that spirit, thanks are in order for the creation of GEITje 7B Ultra and all related datasets:
- Michiel Buisman and UWV for providing the means to create the datasets
- Flemish Supercomputer Center (VSC) for the compute
- The Hugging Face Fellows and rest of the team for their discussions and insights
- The Dutch NLP community, notably @Rijgersberg for building the base GEITje model and the fruitful discussions we've had
More to come, step by step!
BramVanroy/geitje-7b-ultra-65c1ee010ad80fd1f6a8f208
GEITje 7B Ultra: A Conversational Model for Dutch (2412.04092)
While the paper discusses the model a little bit, I especially wanted to write about the datasets, which to this day seem an important asset for Dutch LLM training (SFT and preference tuning). We have a long way to go for Dutch, but publishing transparent and reproducible artefacts seems an important step to me, alongside having open discussions about data, bias, architectures.
In that spirit, thanks are in order for the creation of GEITje 7B Ultra and all related datasets:
- Michiel Buisman and UWV for providing the means to create the datasets
- Flemish Supercomputer Center (VSC) for the compute
- The Hugging Face Fellows and rest of the team for their discussions and insights
- The Dutch NLP community, notably @Rijgersberg for building the base GEITje model and the fruitful discussions we've had
More to come, step by step!
BramVanroy/geitje-7b-ultra-65c1ee010ad80fd1f6a8f208
reacted to
davidberenstein1957's
post with 🚀
9 months ago
Post
3019
🚀 We will be generating a preference dataset for DPO/ORPO and cleaning it with AI feedback during our upcoming meetup!
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback
Sign up here: https://lu.ma/dt0c7jru
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback
Sign up here: https://lu.ma/dt0c7jru
posted
an
update
11 months ago
Post
2214
The InstructGPT paper mentions that they insert 10% pretraining data during SFT, which they find improves the effect of PPO (IIUC). Has anyone else done later ablations on this? I've only seen the inverse suggested, mixing in SFT data during pretraining.
- 2 replies
reacted to
lunarflu's
post with ❤️
11 months ago
Post
2006
cooking up something....anyone interested in a daily activity tracker for HF?
- 12 replies
posted
an
update
12 months ago
Post
2309
All my models seem to be plagued by infinite lists. When you ask a question that requires it to write a list, it most often keeps adding bullet points or enumeration. I am wondering whether this is a result of using chatty GPT-4 as DPO preferences. Any thoughts?
- 1 reply
replied to
their
post
about 1 year ago
Nice! In my experience preference tuning with the ultra feedback datasets does not really change benchmark scores (and sometimes even makes them worse) but it does seem to improve the real-world user experience when chatting with the model.
I'm also not sure if orpo only on UF is better than sft on UC + DPO on UF, especially if you're also trying to do language adaptation. That, or first continue pretraining the model and then doing orpo.
replied to
their
post
about 1 year ago
While the "rules" of OpenAI do get frustrating from time to time, I do not blame others who do not follow the same path as I do. If I am asked why my licenses are different from someone else's I will answer according to what I've written in the post above (the rules suck and our vague, I understand why people do what they do and I do what I do because of other reasons). But I definitely do not want to go around and point fingers pre-emptively in hopes that people just use my models. Our community for Dutch is already quite small so I rather just lift each other up and build on each others work through friendly "competition" than to compete in bad faith.
So I think that for my future models, I'll just make use of ultrachat+ultrafeedback, which should be cleared for training apache 2.0 models because they were created with Azure. This may negatively impact the model's performance (especially for code because it does not include the Stack Overflow set) but I hope the impact is limited.
replied to
their
post
about 1 year ago
What do you mean with compliance in this context? I'm not sure how I can market being non-commercial as a good thing 😅
replied to
their
post
about 1 year ago
Cool! Looking forward to what you'll build with this!
posted
an
update
about 1 year ago
Post
2297
🥳 New license for datasets: Apache 2.0!
I have been struggling mentally for many months now with the OpenAI terms of use that indicate that their model outputs cannot be used to build "competing models". This leads to many questions:
- what is the definition of competing? Is it the same as "commercial"?
- since this is part of the terms of use between OpenAI and the API user, can a third party still use the generated dataset to build competing models?
- are such restrictions even legal in the first place?
Trying to "follow the rules" as much as possible despite wanting to be as open as possible, I kept releasing my datasets under non-commercial licenses (which are too restrictive anyhow - nothing should prevent you from using the data in non-LM commercial settings), just like models trained on these datasets. This has put me at a competitive disadvantage compared to creators who do not follow the same approach and release their data/models on apache 2.0 despite the OpenAI "restrictions". Moreover, I fear (https://twitter.com/BramVanroy/status/1780220420316164246) that my approach blocks adaptation of my data/models for (commercial) applications/integrations.
Thankfully @Rijgersberg noted that these OpenAI terms of use are NOT explicit in the Azure OpenAI API (https://twitter.com/E_Rijgersberg/status/1780308971762450725). Since my latest datasets were created via Azure, this comes as a relief. As far as I can tell after digging through Azure docs, this allows me to change all recent GPT4-generated datasets to apache 2.0! 🥳
- BramVanroy/ultrachat_200k_dutch
- BramVanroy/orca_dpo_pairs_dutch
- BramVanroy/ultra_feedback_dutch
- BramVanroy/ultra_feedback_dutch_cleaned
- BramVanroy/no_robots_dutch
I will have to mull over what I'll do for the older GPT3.5 datasets. What do you think that I should do?
I have been struggling mentally for many months now with the OpenAI terms of use that indicate that their model outputs cannot be used to build "competing models". This leads to many questions:
- what is the definition of competing? Is it the same as "commercial"?
- since this is part of the terms of use between OpenAI and the API user, can a third party still use the generated dataset to build competing models?
- are such restrictions even legal in the first place?
Trying to "follow the rules" as much as possible despite wanting to be as open as possible, I kept releasing my datasets under non-commercial licenses (which are too restrictive anyhow - nothing should prevent you from using the data in non-LM commercial settings), just like models trained on these datasets. This has put me at a competitive disadvantage compared to creators who do not follow the same approach and release their data/models on apache 2.0 despite the OpenAI "restrictions". Moreover, I fear (https://twitter.com/BramVanroy/status/1780220420316164246) that my approach blocks adaptation of my data/models for (commercial) applications/integrations.
Thankfully @Rijgersberg noted that these OpenAI terms of use are NOT explicit in the Azure OpenAI API (https://twitter.com/E_Rijgersberg/status/1780308971762450725). Since my latest datasets were created via Azure, this comes as a relief. As far as I can tell after digging through Azure docs, this allows me to change all recent GPT4-generated datasets to apache 2.0! 🥳
- BramVanroy/ultrachat_200k_dutch
- BramVanroy/orca_dpo_pairs_dutch
- BramVanroy/ultra_feedback_dutch
- BramVanroy/ultra_feedback_dutch_cleaned
- BramVanroy/no_robots_dutch
I will have to mull over what I'll do for the older GPT3.5 datasets. What do you think that I should do?
-
- 9 replies
replied to
their
post
about 1 year ago
I think the correct place for this is to make a new issue on their issue tracker: https://github.com/ScandEval/ScandEval/issues
posted
an
update
about 1 year ago
Post
2459
🎈 LLM Benchmarks Update!
**tl;dr: do not depend on benchmark leaderboards to choose your "chatbot" model! (Especially for non-English languages.)**
First of all, I'm discontinuing the Open #Dutch #LLM Leaderboard (https://lnkd.in/eFnsaFR6). It will stay online for now, but I urge the use of the ScandEval leaderboard instead (https://scandeval.com/dutch-nlg/) by @saattrupdan . It contains more tasks, has better reproducibility and statistics (CI) and a flexible back-end library (
An important note here is that benchmark leaderboards are not a golden truth. Especially evaluating generative models is hard. You run into issues like prompt engineering (and sensitivity of models to one or other prompt), structured output generation, and - quite simply - "how to automatically evaluate open-ended generation".
💡 Another important but under-discussed facet is the discrepancy between models' capability of understanding vs. generating *in different languages* (so the NLU part of NLG benchmarking). In other words: some of the listed models score really well on, e.g., MCQ benchmarks but are not suitable to use as DUTCH chat bots. Interestingly, some of these models seem to understand questions in Dutch and are able to pick the right answer (because they have good knowledge or reasoning skills), but generating fluent and grammatical Dutch is something else entirely! This is perhaps also true for humans: it's easier to sort-of grasp the meaning of a new language and answer with "Yes" or "No", but answering fluently in the language is much harder! Yet, your language production fluency does not necessarily say anything about your knowledge and reasoning skills.
Hopefully we can get a chat arena for Dutch some day - user feedback is the most powerful metric!
**tl;dr: do not depend on benchmark leaderboards to choose your "chatbot" model! (Especially for non-English languages.)**
First of all, I'm discontinuing the Open #Dutch #LLM Leaderboard (https://lnkd.in/eFnsaFR6). It will stay online for now, but I urge the use of the ScandEval leaderboard instead (https://scandeval.com/dutch-nlg/) by @saattrupdan . It contains more tasks, has better reproducibility and statistics (CI) and a flexible back-end library (
scandeval) to run your own benchmarks with. As part of project "Leesplank" (with Michiel Buisman and Maarten Lens-FitzGerald) we recently added GPT-4-1106-preview scores to add a good "target" to the leaderboard.An important note here is that benchmark leaderboards are not a golden truth. Especially evaluating generative models is hard. You run into issues like prompt engineering (and sensitivity of models to one or other prompt), structured output generation, and - quite simply - "how to automatically evaluate open-ended generation".
💡 Another important but under-discussed facet is the discrepancy between models' capability of understanding vs. generating *in different languages* (so the NLU part of NLG benchmarking). In other words: some of the listed models score really well on, e.g., MCQ benchmarks but are not suitable to use as DUTCH chat bots. Interestingly, some of these models seem to understand questions in Dutch and are able to pick the right answer (because they have good knowledge or reasoning skills), but generating fluent and grammatical Dutch is something else entirely! This is perhaps also true for humans: it's easier to sort-of grasp the meaning of a new language and answer with "Yes" or "No", but answering fluently in the language is much harder! Yet, your language production fluency does not necessarily say anything about your knowledge and reasoning skills.
Hopefully we can get a chat arena for Dutch some day - user feedback is the most powerful metric!
-
- 3 replies
replied to
their
post
about 1 year ago
Understandable. I'm especially attracted to the broad vocabulary, which can be of use for language adaptation.
replied to
their
post
about 1 year ago
What kind of weird results? In terms of loss, or really qualitative output?
posted
an
update
about 1 year ago
Post
2405
Does anyone have experience with finetuning Gemma? Even the 2B variant feels more memory heavy than mistral 7B. I know that its vocabulary is much larger (250k) but I'm a bit surprised that the max batch size that I can get in an A100 80GB is only 2 whereas I could fit 4 with mistral 7B - even though Gemma is much smaller except for the embedding layer. Both runs were using FA, same sequence length, same deepspeed zero 3 settings. Oh and yes I'm using the most recent hot fix of transformers that solves a memory issue with Gemma and others.
Any prior experience that you can share or suggestions to improve throughout?
Any prior experience that you can share or suggestions to improve throughout?
-
- 4 replies
replied to
their
post
about 1 year ago
Indeed, there is not a lot of metadata. There's also a discrepancy between the no. scores/languages and the no. paragraphs in the text. I've notified the authors about that. CulturaX is an attractive dataset, too!