README.md

---
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-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [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)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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|