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Discussion tracking new models trained on BAAI/TACO
5
#50 opened 9 months ago
by
librarian-bot
davanstrien
updated
a
dataset
about 9 hours ago
Discussion tracking new models trained on HuggingFaceH4/ultrafeedback_binarized
397
#37 opened 12 months ago
by
librarian-bot
Discussion tracking new models trained on Open-Orca/OpenOrca
258
#19 opened about 1 year ago
by
librarian-bot
Discussion tracking new models trained on HuggingFaceH4/ultrachat_200k
394
#15 opened about 1 year ago
by
librarian-bot
librarian-bot
updated
a
dataset
about 13 hours ago
Discussion tracking new models trained on nvidia/HelpSteer
142
#21 opened about 1 year ago
by
librarian-bot
Discussion tracking new models trained on imdb
187
#1 opened over 1 year ago
by
librarian-bot
Discussion tracking new models trained on argilla/ultrafeedback-binarized-preferences-cleaned
64
#43 opened 12 months ago
by
librarian-bot
davanstrien
posted
an
update
2 days ago
Post
1694
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
Discussion tracking new models trained on HuggingFaceH4/cai-conversation-harmless
32
#44 opened 12 months ago
by
librarian-bot
Discussion tracking new models trained on OpenAssistant/oasst1
89
#7 opened over 1 year ago
by
librarian-bot
Discussion tracking new models trained on LDJnr/Capybara
233
#33 opened about 1 year ago
by
librarian-bot
Discussion tracking new models trained on google/fleurs
181
#6 opened over 1 year ago
by
librarian-bot
Discussion tracking new models trained on Zyphra/Zyda-2
3
#65 opened about 2 months ago
by
librarian-bot
davanstrien
updated
a
Space
5 days ago
davanstrien
updated
a
dataset
6 days ago
davanstrien
posted
an
update
16 days ago
Post
3144
🇸🇰 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
-
- 3 replies
davanstrien
posted
an
update
23 days ago
Post
1760
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
Post
2100
🇪🇺 Policy Thoughts in the EU AI Act Implementation 🇪🇺
There is a lot to like in the first draft of the EU GPAI Code of Practice, especially as regards transparency requirements. The Systemic Risks part, on the other hand, is concerning for both smaller developers and for external stakeholders.
I wrote more on this topic ahead of the next draft. TLDR: more attention to immediate large-scale risks and to collaborative solutions supported by evidence can help everyone - as long as developers disclose sufficient information about their design choices and deployment contexts.
Full blog here, based on our submitted response with @frimelle and @brunatrevelin :
https://huggingface.co/blog/yjernite/eu-draft-cop-risks#on-the-proposed-taxonomy-of-systemic-risks
There is a lot to like in the first draft of the EU GPAI Code of Practice, especially as regards transparency requirements. The Systemic Risks part, on the other hand, is concerning for both smaller developers and for external stakeholders.
I wrote more on this topic ahead of the next draft. TLDR: more attention to immediate large-scale risks and to collaborative solutions supported by evidence can help everyone - as long as developers disclose sufficient information about their design choices and deployment contexts.
Full blog here, based on our submitted response with @frimelle and @brunatrevelin :
https://huggingface.co/blog/yjernite/eu-draft-cop-risks#on-the-proposed-taxonomy-of-systemic-risks
- 2 replies