hSSKrSSKJrJr SSKJr SSKJr SSKJ r SSK J r SSK J r \"SS 55rS r\ "S 5r\S :Xa\ "\5r\R'5Sr\ "S SS9r\R-\/SQ\R.S9r\R1SSS9r\R2(a6\R3\R45 \R3\R4SS9 ggg)N) dataclassfield)chain)Optional) load_dataset) ModelCard)HfArgumentParserct\rSrSr%Sr\"SSS0S9r\\S'\"SSS 0S9r \ \S '\"S SS 0S9r \ \ \S 'Srg )ScriptArgumentsa Arguments for the script. Args: push_to_hub (`bool`, *optional*, defaults to `False`): Whether to push the dataset to the Hugging Face Hub. repo_id (`str`, *optional*, defaults to `"trl-lib/math_shepherd"`): Hugging Face repository ID to push the dataset to. dataset_num_proc (`int` or `None`, *optional*, defaults to `None`): Number of workers to use for dataset processing. Fhelpz4Whether to push the dataset to the Hugging Face Hub.)defaultmetadata push_to_hubztrl-lib/math_shepherdz2Hugging Face repository ID to push the dataset to.repo_idNz0Number of workers to use for dataset processing.dataset_num_proc)__name__ __module__ __qualname____firstlineno____doc__rrbool__annotations__rstrrrint__static_attributes__rY/home/james-whalen/.local/lib/python3.13/site-packages/examples/datasets/math_shepherd.pyr r si PQK'NOGS',LM'hsmrr c^TSRSS5n[R"SU5Vs/sHo"R5PM nn[ U4SjU55(deUVs/sHnTSUS:HPM nn[ [ S/U5[ US/55VVs/sH upgXUPM nnnUSSnUSn US Sn [ S U 55(deU V s/sHoS SR5PM n n U RS 5(aUS SnOS U ;a-U RS 5upS U -n U R5/U -n OtS U ;a-U RS 5upS U -n U R5/U -n OASU ;a-U RS5upU S-n U R5/U -n O[SU 35eU R5n [U 5[U5:XdeXUS.$s snfs snfs snnfs sn f)Ninputuкиⶻc3:># UHnTSUS;v M g7f)label)+-Nr).0idxexamples r "process_example..=s Fgsww$ 2gsr$r%rc3B# UHoRS5v M g7f)r"N) startswith)r' completions rr*r+MsJk $$U++ks) z Mr. RockyParkerzWhat is the smallest positivez The MythzLet $\mathbf{a}$zFind the arithmeticzDetermine an ordered pairzDetermine the ordered pairz"At the Quill and Scroll stationeryzRound to the nearestzCalculate $\sqrt{10p}zSimplify $\sqrt{28x}zStep 1:zstep 1:?zPrompt can't be processed: )prompt completionslabels) replacerefinditerstartallziprstripr/split ValueErrorlen) r)inputsmindexesr(r5ijstepsr3r4r0 first_steps ` rprocess_examplerG5s@ W  % %fe 4F#%++eV"<=" >gsggs#s*gF >&)sG)T%U V%UTQVa[%UE V #2JE1XF)K JkJ J JJ J'')KKH " f #\\)4+ !'')*[8 f #\\)4+ !'')*[8 #\\#.#!'')*[8 6vh?@@\\^F { s6{ ** *F KKE> ? WIsG6+G;'H&Ha --- tags: [trl] --- # Math-Shepherd Dataset ## Summary The Math-Shepherd dataset is a processed version of [Math-Shepherd dataset](peiyi9979/Math-Shepherd), designed to train models using the [TRL library](https://github.com/huggingface/trl) for stepwise supervision tasks. It provides step-by-step solutions to mathematical problems, enabling models to learn and verify each step of a solution, thereby enhancing their reasoning capabilities. ## Data Structure - **Format**: [Standard](https://huggingface.co/docs/trl/main/dataset_formats#standard) - **Type**: [Stepwise supervision](https://huggingface.co/docs/trl/main/dataset_formats#stepwise-supervision) Columns: - `"prompt"`: The problem statement. - `"completions"`: A list of reasoning steps generated to solve the problem. - `"labels"`: A list of booleans or floats indicating the correctness of each corresponding reasoning step. This structure allows models to learn the correctness of each step in a solution, facilitating improved reasoning and problem-solving abilities. ## Generation script The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/math_shepherd.py). __main__zpeiyi9979/Math-Shepherdtrain)r=)r!r$task)remove_columnsnum_procg?*) test_sizeseeddataset) repo_type)r7 dataclassesrr itertoolsrtypingrdatasetsrhuggingface_hubr transformersr r rG model_cardrparserparse_args_into_dataclasses script_argsrPmaprtrain_test_splitrrrrrr^s (!%)  6GLT 8 z o .F446q9K4GDGkk1--G &&B&?GK//0{22iHr