content.raw: I've created a v1 dataset (
content.type: text
content.value: I've created a v1 dataset (
content.raw: https://huggingface.co/datasets/davanstrien/reasoning-required
content.id: davanstrien/reasoning-required
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/davanstrien/reasoning-required
content.raw: ) and model (
content.type: text
content.value: ) and model (
content.raw: https://huggingface.co/davanstrien/ModernBERT-based-Reasoning-Required
content.id: davanstrien/ModernBERT-based-Reasoning-Required
content.type: model
content.type: resource
content.url: https://huggingface.co/davanstrien/ModernBERT-based-Reasoning-Required
content.raw: ) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.
content.type: text
content.value: ) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.
content.type: new_line
content.type: new_line
content.raw: - I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
content.type: text
content.value: - I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
content.type: new_line
content.raw: - I used educational content from 
content.type: text
content.value: - I used educational content from 
content.raw: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu
content.id: HuggingFaceFW/fineweb-edu
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu
content.raw: , adding annotations for domains, reasoning types, and example questions
content.type: text
content.value: , adding annotations for domains, reasoning types, and example questions
content.type: new_line
content.type: new_line
content.raw: My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content. 
content.type: text
content.value: My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content. 
content.type: new_line
content.type: new_line
content.raw: This significantly reduces computation costs while expanding reasoning dataset domain coverage. 
content.type: text
content.value: This significantly reduces computation costs while expanding reasoning dataset domain coverage. 
rawContent: I've created a v1 dataset (https://huggingface.co/datasets/davanstrien/reasoning-required) and model (https://huggingface.co/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 https://huggingface.co/datasets/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. 
reactions.reaction: 👍
reactions.users: John6666 zarmalhotra onslm