Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
100K - 1M
License:
metadata
languages:
- en
paperswithcode_id: imdb-movie-reviews
Dataset Card for "imdb"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://ai.stanford.edu/~amaas/data/sentiment/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 80.23 MB
- Size of the generated dataset: 127.06 MB
- Total amount of disk used: 207.28 MB
Dataset Summary
Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
plain_text
- Size of downloaded dataset files: 80.23 MB
- Size of the generated dataset: 127.06 MB
- Total amount of disk used: 207.28 MB
An example of 'train' looks as follows.
{
"label": 0,
"text": "Goodbye world2\n"
}
Data Fields
The data fields are the same among all splits.
plain_text
text
: astring
feature.label
: a classification label, with possible values includingneg
(0),pos
(1).
Data Splits
name | train | unsupervised | test |
---|---|---|---|
plain_text | 25000 | 50000 | 25000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
Contributions
Thanks to @ghazi-f, @patrickvonplaten, @lhoestq, @thomwolf for adding this dataset.