Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
| 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 | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-instances) | |
| - [Data Splits](#data-instances) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| ## Dataset Description | |
| - **Galileo Homepage:** [Galileo ML Data Intelligence Platform](https://www.rungalileo.io) | |
| - **Repository:** [Needs More Information] | |
| - **Dataset Blog:** [Improving Your ML Datasets With Galileo, Part 1](https://www.rungalileo.io/blog/) | |
| - **Leaderboard:** [Needs More Information] | |
| - **Point of Contact:** [Needs More Information] | |
| - **Sklearn Dataset:** [sklearn](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset) | |
| - **20 Newsgroups Homepage:** [newsgroups homepage](http://qwone.com/~jason/20Newsgroups/) | |
| ### Dataset Summary | |
| This dataset is a version of the [**20 Newsgroups**](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset) dataset fixed with the help of the [**Galileo ML Data Intelligence Platform**](https://www.rungalileo.io/). 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**](https://www.rungalileo.io/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<br>comp.os.ms-windows.misc<br>comp.sys.ibm.pc.hardware<br>comp.sys.mac.hardware<br>comp.windows.x | rec.autos<br>rec.motorcycles<br>rec.sport.baseball<br>rec.sport.hockey | sci.crypt<br><sci.electronics<br>sci.med<br>sci.space | | |
| |:---|:---:|---:| | |
| | misc.forsale | talk.politics.misc<br>talk.politics.guns<br>talk.politics.mideast | talk.religion.misc<br>alt.atheism<br>soc.religion.christian | | |
| | None | | |