README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license: mit
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
  - text-generation
task_ids:
  - sentiment-classification
paperswithcode_id: imdb-movie-reviews
pretty_name: IMDB
dataset_info:
  config_name: plain_text
  features:
    - name: text
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': neg
            '1': pos
  splits:
    - name: train
      num_bytes: 33432823
      num_examples: 25000
    - name: test
      num_bytes: 32650685
      num_examples: 25000
    - name: unsupervised
      num_bytes: 67106794
      num_examples: 50000
  download_size: 83446840
  dataset_size: 133190302
configs:
  - config_name: plain_text
    data_files:
      - split: train
        path: plain_text/train-*
      - split: test
        path: plain_text/test-*
      - split: unsupervised
        path: plain_text/unsupervised-*
    default: true
train-eval-index:
  - config: plain_text
    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 macro
        args:
          average: macro
      - 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 "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.

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