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Francisco Aranda

frascuchon

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updated a collection about 10 hours ago

Music generation

upvoted a paper 7 days ago

Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks

reacted to davanstrien's post with 🚀 10 days ago

The https://huggingface.co/datasets/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 https://huggingface.co/datasets/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 (https://huggingface.co/spaces/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: https://huggingface.co/spaces/data-is-better-together/fineweb-c

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How to optimize your data labelling project with custom interfaces

Oct 16, 2024

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frascuchon's activity

updated a collection about 10 hours ago

Music generation

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upvoted a paper 7 days ago

Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks

Paper • 2311.09825 • Published Nov 16, 2023 • 1

reacted to davanstrien's post with 🚀 10 days ago

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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