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README.md
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---
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dtype: string
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- name: task
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dtype: string
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- name: grade
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dtype: string
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- name: subject
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dtype: string
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- name: topic
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dtype: string
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- name: category
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dtype: string
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- name: skill
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dtype: string
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- name: lecture
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dtype: string
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- name: solution
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dtype: string
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- name: source_index
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dtype: int64
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- name: tiny_index
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dtype: int64
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- name: tiny_selection_seed
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dtype: int64
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- name: tiny_selection_strategy
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dtype: string
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- name: source_dataset
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dtype: string
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- name: source_split
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dtype: string
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splits:
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- name: test
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num_bytes: 160045
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num_examples: 100
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download_size: 71008
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dataset_size: 160045
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configs:
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- config_name: default
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data_files:
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- split: test
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path: data/test-*
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---
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license: cc-by-sa-4.0
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task_categories:
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- multiple-choice
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- question-answering
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language:
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- en
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pretty_name: tinyScienceQA
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source_datasets:
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- tasksource/ScienceQA_text_only
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---
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# tinyScienceQA
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Pathfinder-generated 100-example tiny subset of the text-only ScienceQA test
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split. This is intended for fast smoke tests and default small-sample Pathfinder
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runs, not as an official ScienceQA benchmark replacement.
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## Construction
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- source dataset: `tasksource/ScienceQA_text_only`
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- source split: `test`
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- source rows: `2224`
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- tiny rows: `100`
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- seed: `20260511`
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- selection strategy: `subject_answer_quota_topic_coverage_stable_hash`
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The selector preserves subject/answer-cell proportions with exact integer
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quotas, ensures topic coverage where quota constraints permit, and fills the
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remaining slots by a deterministic stable hash. The resulting subset covers
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`25` of `25` topics.
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## Counts
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Subject counts:
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```json
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{
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"language science": 47,
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"natural science": 46,
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"social science": 7
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}
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```
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Answer counts:
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```json
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{
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"0": 44,
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"1": 43,
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"2": 9,
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"3": 4
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}
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```
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The corresponding Pathfinder benchmark id is `scienceqa`, and the default tiny
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dataset id is `PhilipQuirke/tinyScienceQA`.
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