Ondrej Salamon

onslm

30 46

AI & ML interests

MAGIC to me!!!

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liked a model 4 months ago

pyannote/speaker-diarization-community-1

liked a dataset over 1 year ago

uonlp/CulturaX

updated a collection over 1 year ago

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liked a model 4 months ago

pyannote/speaker-diarization-community-1

Automatic Speech Recognition • Updated Sep 29, 2025 • 4.9M • 889

liked a dataset over 1 year ago

uonlp/CulturaX

Viewer • Updated Dec 16, 2024 • 7.18B • 17.8k • 661

updated a collection over 1 year ago

Papers

Collection

5 items • Updated Apr 19, 2025

liked 2 models over 1 year ago

ReadyArt/Fallen-Abomination-70B-R1-v4.1

71B • Updated Mar 24, 2025 • 6

davanstrien/ModernBERT-based-Reasoning-Required

Text Classification • 0.1B • Updated Apr 8, 2025 • 9 • 11

liked a dataset over 1 year ago

librarian-bots/paper-recommendations-v2

Viewer • Updated Feb 21 • 9.99k • 133 • 16

reacted to davanstrien's post with 👍 over 1 year ago

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I've created a v1 dataset ( davanstrien/reasoning-required) and model ( davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.

- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions

My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.

This significantly reduces computation costs while expanding reasoning dataset domain coverage.