The MBPP (Mostly Basic Python Problems) dataset consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases.

Dataset Card for Mostly Basic Python Problems (mbpp)

  Dataset Summary

The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. Released here as part of… See the full description on the dataset page: https://huggingface.co/datasets/nlile/mbpp.

The dataset used in the paper for code generation

Dataset

This dataset was created by Mrigank Pawagi

Released under CC0: Public Domain

Contents

MBPP dataset annotated with ground-truth programming solutions, to enable evaluations for retrieval and retrieval-augmented code generation. Please refer to code-rag-bench for more details.

Gen-Verse/MBPP dataset hosted on Hugging Face and contributed by the HF Datasets community

evalplus/mbppplus dataset hosted on Hugging Face and contributed by the HF Datasets community

zsqzz/mbpp-new-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

jash404/mbpp dataset hosted on Hugging Face and contributed by the HF Datasets community

The replication package of AOCGThe repository is divided into two parts: datasets and the code of our AOCG method.### Requirements- python 3.8- Java 1.8.0- transformers 4.5.1- tree-sitter 0.2.2- Pytorch 1.7.1### Data PreprocessingExperimental datasets contain the API_SUM dataset, the Hearthstone dataset, and the MBPP dataset. We use tree sitter tool to automatically extract the API terms and sketches of programs.Take the MBPP dataset as an example:To extract API terms, run 'data_process/api_extract.py' and acquire the 'api_terms.jsonl' To extract sketches, run 'data_process/sketch_extract.py' and acquire the 'sketches.jsonl' Put the API terms, sketches, complete codes, and requirements into the 'final_train.jsonl' and 'final_test.jsonl'.### TrainingGiven a specific requirement, the APIer predicts API terms, and the Sketcher outputs corresponding the sketch based on the API terms and requirements. And the Coder fills the sketch to a complete program according to the API terms, sketch and requirement.export CUDA_VISIBLE_DEVICES=0python AOCG_finetune.py \--stage_1 nl_pp \--stage_2 nl_pp_ss \--stage_3 nl_ss_pp_code \--local_rank -1### InferenceThe AOCG predicts code snippets in a progressive generation manner, and write the predicted codes into 'xx.output'.export CUDA_VISIBLE_DEVICES=0python AOCG_inference.py \--stage_1 nl_pp \--stage_2 nl_pp_ss \--stage_3 nl_ss_pp_code \--local_rank -1### EvaluationAfter acquiring the generated codes, evaluate the programs by running 'evaluator/evaluation.py'.

Evaluation dataset for umanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation Task (arxiv.org/abs/2412.21199).

Dataset

This dataset was created by Vũ Trọng Thanh

Released under Apache 2.0

Contents

This study investigated the effects of a mindfulness-based intervention (MBI) called mindfulness-based peak performance (MBPP) on athletic performance and cognitive functions in archers, as well as the role of psychological status and the dose-response relationship of MBPP in archery performance. Twenty-three archers completed a simulated archery competition and the Stroop task prior to and after MBPP training, which consisted of eight sessions over four weeks, while the mindfulness and rumination levels of the archers were assessed at three time points, namely, before, at the mid-point of, and after the MBPP program. The results revealed that the MBPP program significantly improved the shooting performance (p = 0.002, d = 0.27), multiple cognitive functions (ps < 0.001, d = 0.51~0.71), and mindfulness levels of the archers on the post-test, compared to the pre-test (p = 0.032, ηp2 = 0.15 for general; p = 0.004, ηp2 = 0.22 for athletic). Additionally, negative ruminations level was decreased from the pre-test to the middle-test and post-test (ps < 0.001, ηp2 = 0.43). These findings provide preliminary evidence to support the view that MBPP could serve as a promising form of training for fine motor sport performance, cognitive functions, and specific psychological status, such that it warrants further study.

ALIN-LLM/iself-mbpp dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Thanh Vu

Contents

lilac/mbpp

This dataset is a Lilac processed dataset. Original dataset: https://huggingface.co/datasets/mbpp To download the dataset to a local directory: lilac download lilacai/lilac-mbpp

or from python with: ll.download("lilacai/lilac-mbpp")

Dataset

This dataset was created by td

Contents

Dataset Card for "humaneval-mbpp-codegen-qa"

This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python function which satisfies the functionality described in a specified docstring. The responses are then the generated functions.

ankner/new-Synth-MBPP dataset hosted on Hugging Face and contributed by the HF Datasets community

MUsama100/MBPP-Extended-3104 dataset hosted on Hugging Face and contributed by the HF Datasets community