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metadata
dataset_info:
  features:
    - name: archived
      dtype: string
    - name: author
      dtype: string
    - name: author_fullname
      dtype: string
    - name: body
      dtype: string
    - name: comment_type
      dtype: string
    - name: controversiality
      dtype: string
    - name: created_utc
      dtype: string
    - name: edited
      dtype: string
    - name: gilded
      dtype: string
    - name: id
      dtype: string
    - name: link_id
      dtype: string
    - name: locked
      dtype: string
    - name: name
      dtype: string
    - name: parent_id
      dtype: string
    - name: permalink
      dtype: string
    - name: retrieved_on
      dtype: string
    - name: score
      dtype: string
    - name: subreddit_id
      dtype: string
    - name: subreddit_name_prefixed
      dtype: string
    - name: subreddit_type
      dtype: string
    - name: total_awards_received
      dtype: string
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      num_examples: 7503347
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    - name: explainlikeimfive
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    - name: WritingPrompts
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    - name: LifeProTips
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    - name: science
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      num_examples: 6286702
    - name: ifyoulikeblank
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    - name: Foodforthought
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    - name: IWantToLearn
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    - name: bestof
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    - name: philosophy
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    - name: Games
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    - name: podcasts
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    - name: Documentaries
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    - name: GetMotivated
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    - name: technology
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    - name: Fitness
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    - name: travel
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    - name: lifehacks
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    - name: Damnthatsinteresting
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  download_size: 109177016105
  dataset_size: 255339788158
annotations_creators:
  - no-annotation
language:
  - en
language_creators:
  - found
license: []
multilinguality:
  - monolingual
pretty_name: Reddit comments
size_categories:
  - 10B<n<100B
source_datasets: []
tags:
  - reddit
  - social-media
task_categories:
  - text-generation
task_ids:
  - dialogue-modeling
  - language-modeling

Dataset Card for "REDDIT_comments"

Dataset Description

Dataset Summary

Comments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).

Supported Tasks

These comments can be used for text generation and language modeling, as well as dialogue modeling.

Dataset Structure

Data Splits

Each split corresponds to a specific subreddit in the following list: "tifu", "explainlikeimfive", "WritingPrompts", "changemyview", "LifeProTips", "todayilearned", "science", "askscience", "ifyoulikeblank", "Foodforthought", "IWantToLearn", "bestof", "IAmA", "socialskills", "relationship_advice", "philosophy", "YouShouldKnow", "history", "books", "Showerthoughts", "personalfinance", "buildapc", "EatCheapAndHealthy", "boardgames", "malefashionadvice", "femalefashionadvice", "scifi", "Fantasy", "Games", "bodyweightfitness", "SkincareAddiction", "podcasts", "suggestmeabook", "AskHistorians", "gaming", "DIY", "mildlyinteresting", "sports", "space", "gadgets", "Documentaries", "GetMotivated", "UpliftingNews", "technology", "Fitness", "travel", "lifehacks", "Damnthatsinteresting", "gardening", "programming"

Dataset Creation

Curation Rationale

All the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: "archived", "author", "author_fullname", "body", "comment_type", "controversiality", "created_utc", "edited", "gilded", "id", "link_id", "locked", "name", "parent_id", "permalink", "retrieved_on", "score", "subreddit", "subreddit_id", "subreddit_name_prefixed", "subreddit_type", "total_awards_received".

Source Data

The Reddit PushShift data dumps are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.

Initial Data Collection and Normalization

See the paper.

Who are the source language producers?

Redditors are mostly young (65% below 30), male (70%), and American (50% of the site).

Personal and Sensitive Information

The data contains Redditor's usernames associated to their content.

Considerations for Using the Data

This dataset should be anonymized before any processing. Though the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.

Contributions

Thanks to @clefourrier for adding this dataset.