hSSKJrJr SSKJr SSKJr SSKJr SSK J r \"SS55r Sr S r \"S 5r\S :Xa\ "\ 5r\R#5SrS S S.r\"S\S9r\R+\ SS/SQ\R,S9r\R.(a6\R/\R05 \R/\R0SS9 ggg)) dataclassfield)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/prm800k"`): 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/prm800kz2Hugging 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__rS/home/james-whalen/.local/lib/python3.13/site-packages/examples/datasets/prm800k.pyr r si PQK!NOGS',LM'hsmrr c/nUSSn/n/nUSSHnUScUScUSc O[US5HBupgXeS:wdMUSnUSSU/-n US S :Hn USSU /-n URX)U S .5 MD USbUSUSn U S S :Hn OUSbUSn S n O O+U SnURU5 URU 5 M URX#US .5 U$) Nquestionproblemlabelsteps completionshuman_completionchosen_completiontextrating)promptr$labelsT) enumerateappend) exampleoutputsr*previous_completionsprevious_labelsstepcompletion_idx completioncontentr$r"r+r&s rprocess_exampler63s_G Z  +FO )   &40B+C+KPTUhPiPq *3D4G*H &N&9!::$V,215 A "8,1(+ug5&X^_`+I # $ 0 $] 3D9L4M N %h/14E $ % 1 $%7 8 E #F+##G,u%3*8 NNfUdef Nrc B/n[US5n[U5HDnUR5VVs0sH upEXEU_M nnnUR[ U55 MF USVVs0sHoDUVs/sHoUUPM sn_M nnnU$s snnfs snfs snnf)Nr"r)lenrangeitemsextendr6)examplesr/ batch_sizeidxkvr.s r process_batchrAZsGXg&'JZ )1)9:)91f9)9:w/0!4;1:>:a)AQ4)):G> N ;*>sB, B6BBBa --- tags: [trl] --- # PRM800K Dataset ## Summary The PRM800K dataset is a processed version of [OpenAI's PRM800K](https://github.com/openai/prm800k), designed to train models using the [TRL library](https://github.com/huggingface/trl) for stepwise supervision tasks. It contains 800,000 step-level correctness labels for model-generated solutions to problems from the MATH dataset. This dataset enables models to learn and verify each step of a solution, 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/prm800k.py). __main__zUhttps://github.com/openai/prm800k/raw/refs/heads/main/prm800k/data/phase1_train.jsonlzThttps://github.com/openai/prm800k/raw/refs/heads/main/prm800k/data/phase1_test.jsonl)traintestjson) data_filesT )labeler timestamp generationis_quality_control_questionis_initial_screening_questionr r")batchedr=remove_columnsnum_procdataset) repo_typeN) dataclassesrrtypingrdatasetsrhuggingface_hubr transformersrr r6rA model_cardrparserparse_args_into_dataclasses script_argsrFrPmaprrrrrrr\s)!%)  6$N 8 z o .F446q9KifJ6j9Gkk --G K//0{22iH5r