Datasets:

jannalu
/

mbpp-longcontext

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - code
  - python
  - long-context
  - coding
size_categories:
  - 1K<n<10K
configs:
  - config_name: 0k
    data_files:
      - split: test
        path: data/0k/test/data-*
      - split: train
        path: data/0k/train/data-*
      - split: validation
        path: data/0k/validation/data-*
      - split: prompt
        path: data/0k/prompt/data-*
  - config_name: 1k
    data_files:
      - split: test
        path: data/1k/test/data-*
      - split: train
        path: data/1k/train/data-*
      - split: validation
        path: data/1k/validation/data-*
      - split: prompt
        path: data/1k/prompt/data-*
  - config_name: 2k
    data_files:
      - split: test
        path: data/2k/test/data-*
      - split: train
        path: data/2k/train/data-*
      - split: validation
        path: data/2k/validation/data-*
      - split: prompt
        path: data/2k/prompt/data-*
  - config_name: 4k
    data_files:
      - split: test
        path: data/4k/test/data-*
      - split: train
        path: data/4k/train/data-*
      - split: validation
        path: data/4k/validation/data-*
      - split: prompt
        path: data/4k/prompt/data-*
  - config_name: 8k
    data_files:
      - split: test
        path: data/8k/test/data-*
      - split: train
        path: data/8k/train/data-*
      - split: validation
        path: data/8k/validation/data-*
      - split: prompt
        path: data/8k/prompt/data-*
  - config_name: 16k
    data_files:
      - split: test
        path: data/16k/test/data-*
      - split: train
        path: data/16k/train/data-*
      - split: validation
        path: data/16k/validation/data-*
      - split: prompt
        path: data/16k/prompt/data-*
  - config_name: 32k
    data_files:
      - split: test
        path: data/32k/test/data-*
      - split: train
        path: data/32k/train/data-*
      - split: validation
        path: data/32k/validation/data-*
      - split: prompt
        path: data/32k/prompt/data-*
  - config_name: 64k
    data_files:
      - split: test
        path: data/64k/test/data-*
      - split: train
        path: data/64k/train/data-*
      - split: validation
        path: data/64k/validation/data-*
      - split: prompt
        path: data/64k/prompt/data-*
  - config_name: 128k
    data_files:
      - split: test
        path: data/128k/test/data-*
      - split: train
        path: data/128k/train/data-*
      - split: validation
        path: data/128k/validation/data-*
      - split: prompt
        path: data/128k/prompt/data-*
  - config_name: 196k
    data_files:
      - split: test
        path: data/196k/test/data-*
      - split: train
        path: data/196k/train/data-*
      - split: validation
        path: data/196k/validation/data-*
      - split: prompt
        path: data/196k/prompt/data-*
  - config_name: 256k
    data_files:
      - split: test
        path: data/256k/test/data-*
      - split: train
        path: data/256k/train/data-*
      - split: validation
        path: data/256k/validation/data-*
      - split: prompt
        path: data/256k/prompt/data-*
  - config_name: 512k
    data_files:
      - split: test
        path: data/512k/test/data-*
      - split: train
        path: data/512k/train/data-*
      - split: validation
        path: data/512k/validation/data-*
      - split: prompt
        path: data/512k/prompt/data-*
  - config_name: 1m
    data_files:
      - split: test
        path: data/1m/test/data-*
      - split: train
        path: data/1m/train/data-*
      - split: validation
        path: data/1m/validation/data-*
      - split: prompt
        path: data/1m/prompt/data-*
dataset_info:
  features:
    - name: task_id
      dtype: int64
    - name: text
      dtype: string
    - name: code
      dtype: string
    - name: test_list
      sequence: string
    - name: test_setup_code
      dtype: string
    - name: challenge_test_list
      sequence: string
    - name: context
      dtype: string
    - name: context_id
      dtype: string
    - name: context_length_tokens
      dtype: int64
    - name: code_length_chars
      dtype: int64
    - name: dataset_version
      dtype: string
  splits:
    - name: test
      num_examples: 500
    - name: train
      num_examples: 374
    - name: validation
      num_examples: 90
    - name: prompt
      num_examples: 10

MBPP Long-Context Dataset

Overview

MBPP Long-Context is a benchmark dataset that combines coding problems from the MBPP (Mostly Basic Python Problems) dataset with long-context distractors from BABILong. This dataset evaluates code generation performance under long-context conditions, testing whether models can maintain coding ability with stuffed context.

Dataset Structure

Data Fields

Each sample contains:

Original MBPP Fields

task_id (int): Unique task identifier
text (str): Problem description
code (str): Reference solution
test_list (List[str]): Test cases (assertions)
test_setup_code (str): Optional setup code
challenge_test_list (List[str]): Additional test cases

Long-Context Fields

context (str): Prepended distractor text from BABILong, ranging from 0k to 1M.
context_id (str): BABILong source identifier (e.g., "babilong_128k_qa1_sample_42")
context_length_tokens (int): Token count using Llama tokenizer

Metadata

code_length_chars (int): Reference solution length for difficulty tracking

Data Splits

All configurations follow the original MBPP split structure:

test: 500 samples (primary evaluation set)
train: 374 samples
validation: 90 samples
prompt: 10 samples (few-shot examples)

Creating the dataset

To avoid confounding variables, this dataset uses stratified random assignment, where:

Sort MBPP tasks by code length
Get text from BABILong qa1-qa10 splits
Duplicate contexts to match task count (974 samples)
Shuffle contexts and assign to sorted tasks

Source Datasets

MBPP (Mostly Basic Python Problems)

Source: google-research-datasets/mbpp
Size: 974 problems
Paper: Program Synthesis with Large Language Models

BABILong

Source: RMT-team/babilong
Content: input field from qa1-qa10 splits
Paper: BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack