imdb/README.md

annotations_creators	language_creators	language	license	multilinguality	size_categories	source_datasets	task_categories	task_ids	paperswithcode_id	pretty_name	dataset_info	configs	train-eval-index
expert-generated	expert-generated	en	other	monolingual	10K<n<100K	original	text-classification	sentiment-classification	imdb-movie-reviews	IMDB	config_name features splits download_size dataset_size plain_text name dtype text string name dtype label class_label names 0 1 neg pos name num_bytes num_examples train 33432823 25000 name num_bytes num_examples test 32650685 25000 name num_bytes num_examples unsupervised 67106794 50000 83446840 133190302	config_name data_files default plain_text split path train plain_text/train-* split path test plain_text/test-* split path unsupervised plain_text/unsupervised-* true	config task task_id splits col_mapping metrics plain_text text-classification binary_classification train_split eval_split train test text label text target type accuracy name Accuracy type name args f1 F1 macro average macro type name args f1 F1 micro average micro type name args f1 F1 weighted average weighted type name args precision Precision macro average macro type name args precision Precision micro average micro type name args precision Precision weighted average weighted type name args recall Recall macro average macro type name args recall Recall micro average micro type name args recall Recall weighted average weighted

Dataset Card for "imdb"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • 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
  • Contributions

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: 84.13 MB
• Size of the generated dataset: 133.23 MB
• Total amount of disk used: 217.35 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

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

plain_text

• Size of downloaded dataset files: 84.13 MB
• Size of the generated dataset: 133.23 MB
• Total amount of disk used: 217.35 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: a string feature.
• label: a classification label, with possible values including neg (0), pos (1).

Data Splits

name	train	unsupervised	test
plain_text	25000	50000	25000

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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.