
# Dataset Card for "REDDIT_comments"
## Dataset Description

- **Homepage:**
- **Paper: https://arxiv.org/abs/2001.08435**

### 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](https://files.pushshift.io/reddit/) 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](https://github.com/clefourrier) for adding this dataset.
task_categories:text-generation task_ids:dialogue-modeling task_ids:language-modeling annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:10B<n<100B language:en reddit social-media arxiv:2001.08435 region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10B<n<100B"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["dialogue-modeling", "language-modeling"], "pretty_name": "Reddit comments", "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"}], "splits": [{"name": "programming", "num_bytes": 3466623746, "num_examples": 7503347}, {"name": "tifu", "num_bytes": 4761338653, "num_examples": 12738669}, {"name": "explainlikeimfive", "num_bytes": 8451732573, "num_examples": 16392814}, {"name": "WritingPrompts", "num_bytes": 4651591771, "num_examples": 4436210}, {"name": "changemyview", "num_bytes": 8603031915, "num_examples": 11600073}, {"name": "LifeProTips", "num_bytes": 5272994396, "num_examples": 12829459}, {"name": "todayilearned", "num_bytes": 22655655241, "num_examples": 60199778}, {"name": "science", "num_bytes": 7069809765, "num_examples": 18112884}, {"name": "askscience", "num_bytes": 3144754665, "num_examples": 6286702}, {"name": "ifyoulikeblank", "num_bytes": 547200329, "num_examples": 1332211}, {"name": "Foodforthought", "num_bytes": 308377128, "num_examples": 567900}, {"name": "IWantToLearn", "num_bytes": 408331672, "num_examples": 745543}, {"name": "bestof", "num_bytes": 2003718831, "num_examples": 4347522}, {"name": "IAmA", "num_bytes": 9380094090, "num_examples": 25778822}, {"name": "socialskills", "num_bytes": 1000014402, "num_examples": 1842733}, {"name": "relationship_advice", "num_bytes": 22298879735, "num_examples": 38937398}, {"name": "philosophy", "num_bytes": 1494947876, "num_examples": 2391695}, {"name": "YouShouldKnow", "num_bytes": 1165617658, "num_examples": 2639265}, {"name": "history", "num_bytes": 1457852402, "num_examples": 2962043}, {"name": "books", "num_bytes": 4562689426, "num_examples": 10187495}, {"name": "Showerthoughts", "num_bytes": 13259109532, "num_examples": 34123213}, {"name": "personalfinance", "num_bytes": 9484869588, "num_examples": 18361314}, {"name": "buildapc", "num_bytes": 9801044390, "num_examples": 21761801}, {"name": "EatCheapAndHealthy", "num_bytes": 853462012, "num_examples": 1821897}, {"name": "boardgames", "num_bytes": 3131627378, "num_examples": 6328926}, {"name": "malefashionadvice", "num_bytes": 2928017882, "num_examples": 7712258}, {"name": "femalefashionadvice", "num_bytes": 1619784736, "num_examples": 3262969}, {"name": "scifi", "num_bytes": 888152056, "num_examples": 2193741}, {"name": "Fantasy", "num_bytes": 2285934538, "num_examples": 4566639}, {"name": "Games", "num_bytes": 10396813188, "num_examples": 23373965}, {"name": "bodyweightfitness", "num_bytes": 794549854, "num_examples": 1613634}, {"name": "SkincareAddiction", "num_bytes": 3421122597, "num_examples": 5660550}, {"name": "podcasts", "num_bytes": 464773126, "num_examples": 943266}, {"name": "suggestmeabook", "num_bytes": 1842944304, "num_examples": 3492937}, {"name": "AskHistorians", "num_bytes": 2244587909, "num_examples": 2714353}, {"name": "gaming", "num_bytes": 28374513722, "num_examples": 85729253}, {"name": "DIY", "num_bytes": 2113533684, "num_examples": 4489265}, {"name": "sports", "num_bytes": 2230129132, "num_examples": 6470079}, {"name": "space", "num_bytes": 3081499208, "num_examples": 7896182}, {"name": "gadgets", "num_bytes": 1683252868, "num_examples": 4104833}, {"name": "Documentaries", "num_bytes": 1852644771, "num_examples": 4051474}, {"name": "GetMotivated", "num_bytes": 1211761267, "num_examples": 3221980}, {"name": "UpliftingNews", "num_bytes": 2003149025, "num_examples": 4741948}, {"name": "technology", "num_bytes": 10826871436, "num_examples": 25404699}, {"name": "Fitness", "num_bytes": 6191132755, "num_examples": 14319856}, {"name": "travel", "num_bytes": 1740556350, "num_examples": 3806755}, {"name": "lifehacks", "num_bytes": 626791812, "num_examples": 1799437}, {"name": "Damnthatsinteresting", "num_bytes": 6376694618, "num_examples": 15643554}, {"name": "gardening", "num_bytes": 1825313940, "num_examples": 4568468}, {"name": "mildlyinteresting", "num_bytes": 9079894206, "num_examples": 26436769}], "download_size": 109177016105, "dataset_size": 255339788158}, "tags": ["reddit", "social-media"]}
TAGS
#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10B<n<100B #language-English #reddit #social-media #arxiv-2001.08435 #region-us 


# Dataset Card for "REDDIT_comments"
## Dataset Description

- Homepage:
- Paper: URL

### 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.
# Dataset Card for "REDDIT_comments" ## Dataset Description

- Homepage:
- Paper: URL ### 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.
TAGS
#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10B<n<100B #language-English #reddit #social-media #arxiv-2001.08435 #region-us 
 # Dataset Card for "REDDIT_comments" ## Dataset Description

- Homepage:
- Paper: URL ### 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.
passage: TAGS
#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10B<n<100B #language-English #reddit #social-media #arxiv-2001.08435 #region-us 
# Dataset Card for "REDDIT_comments"## Dataset Description

- Homepage:
- Paper: URL### 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