metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
pretty_name: 20_Newsgroups_Fixed
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
  - topic-classification

Dataset Card for 20_Newsgroups_Fixed

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Galileo Homepage: Galileo ML Data Intelligence Platform
Repository: [Needs More Information]
Dataset Blog: Improving Your ML Datasets With Galileo, Part 1
Leaderboard: [Needs More Information]
Point of Contact: [Needs More Information]
Sklearn Dataset: sklearn
20 Newsgroups Homepage: newsgroups homepage

Dataset Summary

This dataset is a version of the 20 Newsgroups dataset fixed with the help of the Galileo ML Data Intelligence Platform. In a matter of minutes, Galileo enabled us to uncover and fix a multitude of errors within the original dataset. In the end, we present this improved dataset as a new standard for natural language experimentation and benchmarking using the Newsgroups dataset.

Curation Rationale

This dataset was created to showcase the power of Galileo as a Data Intelligence Platform. Through Galileo, we identify critical error patterns within the original Newsgroups training dataset - garbage data that do not properly fit any newsgroup label category. Moreover, we observe that these errors permeate throughout the test dataset.

As a result of our analysis, we propose the addition of a new class to properly categorize and fix the labeling of garbage data samples: a "None" class. Galileo further enables us to quickly make these data sample changes within the training set (changing garbage data labels to None) and helps guide human re-annotation of the test set.

Total Dataset Errors Fixed: 1163 (6.5% of the dataset)

Errors / Split.	Overall	Train	Test
Garbage samples fixed	718	396	322
Empty samples fixed	445	254	254
Total samples fixed	1163	650	650

To learn more about the process of fixing this dataset, please refer to our Blog.

Dataset Structure

Data Instances

For each data sample, there is the text of the newsgroup post, the corresponding newsgroup forum where the message was posted (label), and a data sample id.

An example from the dataset looks as follows:

{'id': 1,
'text': 'I have win 3.0 and downloaded several icons and BMP\'s but I can\'t figure out\nhow to change the "wallpaper" or use the icons.  Any help would be appreciated.\n\n\nThanx,\n\n-Brando'
'label': comp.os.ms-windows.misc}

Data Fields

id: the unique numerical id associated with a data sample
text: a string containing the text of the newsgroups message
label: a string indicating the newsgroup forum where the sample was posted

Data Splits

The data is split into a training and test split. To reduce bias and test generalizability across time, data samples are split between train and test depending upon whether their message was posted before or after a specific date, respectively.

Data Classes

The fixed data is organized into 20 newsgroup topics + a catch all "None" class. Some of the newsgroups are very closely related to each other (e.g. comp.sys.ibm.pc.hardware / comp.sys.mac.hardware), while others are highly unrelated (e.g misc.forsale / soc.religion.christian). Here is a list of the 21 classes, partitioned according to subject matter:

comp.graphics comp.os.ms-windows.misc comp.sys.ibm.pc.hardware comp.sys.mac.hardware comp.windows.x	rec.autos rec.motorcycles rec.sport.baseball rec.sport.hockey	sci.crypt <sci.electronics sci.med sci.space
misc.forsale	talk.politics.misc talk.politics.guns talk.politics.mideast	talk.religion.misc alt.atheism soc.religion.christian
None

Datasets:

rungalileo
/

20_Newsgroups_Fixed