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reinforcelabs/STEM-QA-EVAL

STEM-QA-EVAL Composition Subject Description chem Chemistry — physical, organic, and inorganic chemistry questions. math Mathematics — algebra, geometry, trigonometry, set theory, and combinatorics. phys Physics — mechanics, electromagnetism, optics, and modern physics. resn Reasoning — verbal, logical, and comprehension-style questions. Schema Column Type Description data_id string Stable identifier (stem-NNNN… See the full description on the dataset page: https://huggingface.co/datasets/reinforcelabs/STEM-QA-EVAL.

sourceHugging Faceotherupdated 4mo agoView on Hugging Face
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STEM-QA-EVAL

Composition

SubjectDescription
chemChemistry — physical, organic, and inorganic chemistry questions.
mathMathematics — algebra, geometry, trigonometry, set theory, and combinatorics.
physPhysics — mechanics, electromagnetism, optics, and modern physics.
resnReasoning — verbal, logical, and comprehension-style questions.

Schema

ColumnTypeDescription
data_idstringStable identifier (stem-NNNN, where NNNN is the original 0-based line index in the source corpus).
domainstringOne of chem, math, phys, resn.
qtypestringQuestion format: assertion reasoning, multiple correct choice, matching list.
questionstringProblem stem (LaTeX / HTML entities possible).
optionslist[string]Lettered choices A, B, C, ...
gold_answerstringResolved gold answer — <LETTER>) <option text> (multi-answer joined with ;).
answer_indiceslist[string]Raw option index strings (e.g. ["1"] or ["0","2","3"]).

Usage

python
from datasets import load_dataset
ds = load_dataset("reinforcelabs/STEM-QA-EVAL")
ex = ds["test"][0]
print(ex["data_id"], ex["domain"], ex["qtype"])
print("Q:", ex["question"])
for i, opt in enumerate(ex["options"]):
    print(f"  {chr(ord('A')+i)}) {opt}")
print("GOLD:", ex["gold_answer"])