Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
metadata
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: mr
pretty_name: RottenTomatoes - MR Movie Review Data
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
splits:
- name: train
num_bytes: 1074810
num_examples: 8530
- name: validation
num_bytes: 134679
num_examples: 1066
- name: test
num_bytes: 135972
num_examples: 1066
download_size: 487770
dataset_size: 1345461
train-eval-index:
- config: default
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1
args:
average: binary
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
Dataset Card for "rotten_tomatoes"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://www.cs.cornell.edu/people/pabo/movie-review-data/
- Repository: More Information Needed
- Paper: https://arxiv.org/abs/cs/0506075
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 0.49 MB
- Size of the generated dataset: 1.34 MB
- Total amount of disk used: 1.84 MB
Dataset Summary
Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 0.49 MB
- Size of the generated dataset: 1.34 MB
- Total amount of disk used: 1.84 MB
An example of 'validation' looks as follows.
{
"label": 1,
"text": "Sometimes the days and nights just drag on -- it 's the morning that make me feel alive . And I have one thing to thank for that : pancakes . "
}
Data Fields
The data fields are the same among all splits.
default
text: astringfeature.label: a classification label, with possible values includingneg(0),pos(1).
Data Splits
Reads Rotten Tomatoes sentences and splits into 80% train, 10% validation, and 10% test, as is the practice set out in
Jinfeng Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world Applications.''
| name | train | validation | test |
|---|---|---|---|
| default | 8530 | 1066 | 1066 |
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{Pang+Lee:05a,
author = {Bo Pang and Lillian Lee},
title = {Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales},
booktitle = {Proceedings of the ACL},
year = 2005
}
Contributions
Thanks to @thomwolf, @jxmorris12 for adding this dataset.