Datasets:

CircleCat
/

pymeta

pretty_name: PyMETA
license: cc-by-nc-4.0
language:
  - en
  - zh
annotations_creators:
  - expert-generated
  - machine-generated
language_creators:
  - found
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
  - multi-label-classification
tags:
  - code
  - python
  - code-error-classification
  - education
  - online-judge
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: validation
        path: dev.csv
      - split: test
        path: test.csv

Dataset Card for PyMETA

Dataset Description

Repository (code, prompts, scripts): https://github.com/Circle-Cat/pymeta
Paper: PyMETA: A Benchmark Dataset for Hierarchical Student Code Error Classification with Python-Interpreter-Based Labels
Point of Contact: CircleCat ({cyli, ztang}@circlecat.org)

Dataset Summary

PyMETA (Python Multi-Error TAxonomy) is a large-scale benchmark for hierarchical student code error classification. It contains 48,646 real Python code submissions from 579 users across 155 distinct problems (22 problem types), collected from the Circle Cat online learning platform. Every submission has a single-error label derived from Online Judge execution output, organized under a three-level hierarchical taxonomy (binary → three-class → 14 fine-grained error types grounded in Python's official exception hierarchy).

An expert-annotated multi-error diagnostic subset of 97 samples (13 error types) is provided in the GitHub repository for studying co-occurring errors.

Supported Tasks

Task A — Binary classification: No Error vs. Error.
Task B — Three-class classification: No Error / Logic Error / Explicit Error.
Task C — Multi-class classification: 14 fine-grained error types.
Multi-error classification (on the 97-sample subset): identify all concurrent errors in a submission.

Languages

Student code is Python. Problem descriptions are primarily in Chinese (zh); the evaluation prompts are in English (en).

How to use

from datasets import load_dataset

ds = load_dataset("CircleCat/pymeta")
print(ds["train"][0])

Dataset Structure

Data Fields

Each sample has 9 features plus a single-error label:

Field	Type	Description
`userId`	string	Anonymized numeric identifier of the student
`name`	string	Problem/lesson name (problem type)
`questionId`	string	Unique identifier of the problem
`question`	string	Problem description
`exceptedAnswer`	string	A correct reference code solution
`attemptId`	string	Attempt number
`studentAnswer`	string	The student's submitted Python code
`testOutcome`	string	Online Judge error message / execution output
`attemptstepid`	string	Step identifier for the attempt
`error_category`	string	Single-error label (see taxonomy below)

Taxonomy (label IDs)

0 No Error · 1 Logic Error · 2 Syntax Error · 3 Name Error · 4 Type Error · 5 Indentation Error · 6 Unbound Local Error · 7 Key Error · 8 Index Error · 9 EOF Error · 10 Recursion Error · 11 Value Error · 12 Tab Error · 13 Other Errors. Full definitions and per-class counts are in the GitHub TAXONOMY.md.

Data Splits

Split	Examples
train	39,402
validation	4,379
test	4,865
total	48,646

Source Data

Submissions were collected from historical logs of the Circle Cat online learning platform (a self-hosted Moodle instance with an integrated Online Judge). They were generated organically by learners of varying proficiency during ordinary coursework, so they provide a realistic and diverse distribution of student code errors.

Personal and Sensitive Information

The data were collected from pre-existing educational records under the platform's terms of use (which inform users that anonymized data may be used for educational and research purposes), with no recruitment or experimental intervention. All identifiers have been irreversibly anonymized; the dataset contains no personally identifiable information (PII) and no offensive content.

Licensing Information

Released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

Citation

@misc{li2026pymeta,
  title        = {PyMETA: A Benchmark Dataset for Hierarchical Student Code Error
                  Classification with Python-Interpreter-Based Labels},
  author       = {Li, Chuyue and Tang, Ziqi and Wang, Jingyi and Wu, Yu and
                  Hashimoto, Kazuma and Gao, Lingyu},
  year         = {2026},
  howpublished = {\url{https://github.com/Circle-Cat/pymeta}},
  note         = {CircleCat}
}