1 2 4

Hanna Yukhymenko

hannayukhymenko

https://ayukh.com

AI & ML interests

Multilingual LLM, privacy/safety @ ETHZ

Recent Activity

reacted to stefan-it's post with ❤️ 9 days ago

My latest project is the outcome of the last 2+ years working with TPUs from the amazing TPU Research Cloud (TRC) program and training Encoder-only LMs with the TensorFlow Model Garden library. 👉 Link: https://github.com/stefan-it/model-garden-lms An overview of some features: - Cheatsheet for setting-up a TPU VM Pod (with all necessary dependencies) to pretrain LMs with TF Model Garden - Conversion scripts that convert TF Model Garden weights to Hugging Face Transformers-compatible models - Supported architectures include BERT, BERT with Token Dropping and TEAMS I also released BERT-based models pretrained on the great Hugging Face FineWeb and FineWeb-Edu datasets (10BT subset). With more to come! 👉 Model Hub Link: https://huggingface.co/model-garden-lms If you find these resources useful, please give them a like! Made from Bavarian Oberland with ❤️ and 🥨.

reacted to davanstrien's post with 🚀 12 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

upvoted a paper 26 days ago

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

View all activity

Organizations

hannayukhymenko's activity

reacted to stefan-it's post with ❤️ 9 days ago

Post

1340

My latest project is the outcome of the last 2+ years working with TPUs from the amazing TPU Research Cloud (TRC) program and training Encoder-only LMs with the TensorFlow Model Garden library.

👉 Link: https://github.com/stefan-it/model-garden-lms

An overview of some features:

- Cheatsheet for setting-up a TPU VM Pod (with all necessary dependencies) to pretrain LMs with TF Model Garden
- Conversion scripts that convert TF Model Garden weights to Hugging Face Transformers-compatible models
- Supported architectures include BERT, BERT with Token Dropping and TEAMS

I also released BERT-based models pretrained on the great Hugging Face FineWeb and FineWeb-Edu datasets (10BT subset). With more to come!

👉 Model Hub Link: https://huggingface.co/model-garden-lms

If you find these resources useful, please give them a like!

Made from Bavarian Oberland with ❤️ and 🥨.

reacted to davanstrien's post with 🚀 12 days ago

Post

2142

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