Datasets:
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---
license: apache-2.0
dataset_info:
features:
- name: id
dtype: int64
- name: repo_name
dtype: string
- name: repo_owner
dtype: string
- name: file_link
dtype: string
- name: line_link
dtype: string
- name: path
dtype: string
- name: content_sha
dtype: string
- name: content
dtype: string
splits:
- name: test
num_bytes: 32708409
num_examples: 50
- name: train
num_bytes: 8081954107
num_examples: 10000
download_size: 5914651135
dataset_size: 8114662516
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
tags:
- jupyter notebook
size_categories:
- 1K<n<10K
---
# Dataset Summary
The presented dataset contains `10000` Jupyter notebooks,
each of which contains at least one error. In addition to the notebook content,
the dataset also provides information about the repository where the notebook is stored.
This information can help restore the environment if needed.
# Getting Started
This dataset is organized such that it can be naively loaded via the Hugging Face datasets library. We recommend using streaming due to the large size of the files.
```Python
import nbformat
from datasets import load_dataset
dataset = load_dataset(
"JetBrains-Research/jupyter-errors-dataset", split="test", streaming=True
)
row = next(iter(dataset))
notebook = nbformat.reads(row["content"], as_version=nbformat.NO_CONVERT)
```
# Citation
```
@misc{JupyterErrorsDataset,
title = {Dataset of Errors in Jupyter Notebooks},
author = {Konstantin Grotov and Sergey Titov and Yaroslav Zharov and Timofey Bryksin},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/datasets/JetBrains-Research/jupyter-errors-dataset}},
}
``` |