size_categories:
- 100M<n<1B
configs:
- config_name: default
data_files:
- split: chunk_0000_sample
path: OKReddit_Sample.jsonl
pretty_name: OKReddit (Release Candidate 3)
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
source_datasets:
- original
language:
- en
tags:
- not-for-all-audiences
OKReddit - Release Candidate 2023
Dataset Summary
OKReddit is a filtered collection of 6.5 TiB (An estimated 600M rows of reddit submissions) of reddit submissions and comments from 2005 to 2023. This dataset has been prepared for research or archival purposes.
This dataset includes (obviously) a filtered list of subreddits.
- Curated by: KaraKaraWitch
- Funded by: Recursal.ai
- Shared by: KaraKaraWitch
- Language(s) (NLP): Mainly English. Other languages are available at smaller sizes.
- License:
Scriptsfolder are Apache 2.0. Refer to Licensing Information for data license.
Dataset Sources
- Source Data: Academic Torrents by (stuck_in_the_matrix, Watchful1, RaiderBDev & pushshift folks.)
Supported Tasks and Leaderboards
The dataset may be used for a variety of natural language processing (NLP) tasks including:
Text Classification: Classifying comments and posts into categories based on sentiment, topic, or subreddit.
Language Modeling: Training language models to understand and generate conversational text.
Sentiment Analysis: Analyzing the sentiment of comments and posts across different subreddits and topics.
Topic Modeling: Identifying and modeling topics discussed in the posts and comments.
Languages
The primary language of the dataset is English, as the majority of redditors are English educated. However, posts in other languages may also be present in smaller quanitites.
Dataset Structure
Data Instances
Each data instance repreasents a submission thread within a subreddit.
thread_id: The submission thread ID. Inclusive of thet3_that reddit uses to mark an id as a thread.https://reddit.com/r/<SUBREDDIT>/comments/<THREAD_ID>/subreddit: The name of the subreddit. Case-insensitive. Reddit just redirects you to the correct-cased subreddit.namedconversation: A OpenAI "compatible" conversation:from: The author username that posted the content. It is notuser,system,model!content: The reddit markdown posted.
- The first value of
namedconversationis the submission. The rest are replies. - If a submission is marked as NSFW / Mature, a
[R-18]is appended to the front of the title. submission/comments: The raw submission and comments respectively.- Refer to sample for full structure
Unsure or Confused? We have provided a real sample below.
Data Sample
Sample Thread
{
"thread_id": "t3_of7h2",
"subreddit": "Gaben",
"namedconversation": [
{
"from": "[deleted]",
"content": "[13 Jan 2012, 07:01:07] TIL Half-Life 2's source code was hacked because the hacker guessed Gabe's password, which was \"gaben\"\n\nLink: half-life.wikia.com"
},
{
"from": "clydethefrog",
"content": "[15 Jan 2012, 18:01:06] That's my password too"
},
{
"from": "Dunge",
"content": "[29 Feb 2012, 02:02:34] \"Gembe was led into believing that Valve wanted to employ him as an in-house security auditor. He was to be offered a flight to the USA and was to be arrested on arrival by the FBI.\"\n\nWow that's sad"
},
{
"from": "captainregularr",
"content": "[13 Jan 2012, 14:01:14] Did you know gaben makes me gaben my gaben?"
},
{
"from": "Turellio",
"content": "[13 Jan 2012, 17:01:53] that's what gaben gaben"
},
{
"from": "captainregularr",
"content": "[13 Jan 2012, 17:01:05] I gaben to gaben's demands."
},
{
"from": "RagingRetard",
"content": "[13 Jan 2012, 17:01:49] Oh, quit your incessant gaben."
}
],
"submission": {
"sub": {
"name": "Gaben",
"id": "2scx1",
"subs": null,
"type": null
},
"author": null,
"title": "TIL Half-Life 2's source code was hacked because the hacker guessed Gabe's password, which was \"gaben\"",
"score": 23,
"created": 1326440407.0,
"id": "of7h2",
"flags": "",
"link_flair": null,
"url": "http://half-life.wikia.com/wiki/Half-Life_2_Beta#Source_code_leak",
"text": "",
"removed": [],
"cross": []
},
"comments": [
{
"sub": {
"name": "Gaben",
"id": "2scx1",
"subs": -1,
"type": ""
},
"author": {
"name": "clydethefrog",
"uid": "",
"create": -1,
"flair": null,
"patreon": false,
"premium": false
},
"text": "That's my password too",
"score": 1,
"created": "1326652326",
"id": "c3hge04",
"parent_id": "t3_of7h2",
"thread_id": "t3_of7h2",
"flags": "A",
"children": []
},
{
"sub": {
"name": "Gaben",
"id": "2scx1",
"subs": -1,
"type": ""
},
"author": {
"name": "Dunge",
"uid": "",
"create": -1,
"flair": null,
"patreon": false,
"premium": false
},
"text": "\"Gembe was led into believing that Valve wanted to employ him as an in-house security auditor. He was to be offered a flight to the USA and was to be arrested on arrival by the FBI.\"\n\nWow that's sad",
"score": 3,
"created": "1330483894",
"id": "c3w2ulz",
"parent_id": "t3_of7h2",
"thread_id": "t3_of7h2",
"flags": "A",
"children": []
},
{
"sub": {
"name": "Gaben",
"id": "2scx1",
"subs": -1,
"type": ""
},
"author": {
"name": "captainregularr",
"uid": "",
"create": -1,
"flair": null,
"patreon": false,
"premium": false
},
"text": "Did you know gaben makes me gaben my gaben?",
"score": 5,
"created": "1326463514",
"id": "c3gsfkx",
"parent_id": "t3_of7h2",
"thread_id": "t3_of7h2",
"flags": "A",
"children": [
{
"sub": {
"name": "Gaben",
"id": "2scx1",
"subs": -1,
"type": ""
},
"author": {
"name": "Turellio",
"uid": "",
"create": -1,
"flair": null,
"patreon": false,
"premium": false
},
"text": "that's what gaben gaben",
"score": 3,
"created": "1326476873",
"id": "c3guihp",
"parent_id": "t1_c3gsfkx",
"thread_id": "t3_of7h2",
"flags": "A",
"children": [
{
"sub": {
"name": "Gaben",
"id": "2scx1",
"subs": -1,
"type": ""
},
"author": {
"name": "captainregularr",
"uid": "",
"create": -1,
"flair": null,
"patreon": false,
"premium": false
},
"text": "I gaben to gaben's demands.",
"score": 5,
"created": "1326477005",
"id": "c3guje0",
"parent_id": "t1_c3guihp",
"thread_id": "t3_of7h2",
"flags": "AE",
"children": [
{
"sub": {
"name": "Gaben",
"id": "2scx1",
"subs": -1,
"type": ""
},
"author": {
"name": "RagingRetard",
"uid": "",
"create": -1,
"flair": null,
"patreon": false,
"premium": false
},
"text": "Oh, quit your incessant gaben.",
"score": 2,
"created": "1326477409",
"id": "c3gulzh",
"parent_id": "t1_c3guje0",
"thread_id": "t3_of7h2",
"flags": "A",
"children": []
}
]
}
]
}
]
}
]
}
Extra dataset notes
Flags: Reddit has some boolean switches which can be compacted into a string. We have done so to compact the number of boolean switches that needs to be stored.
For submissions the list of flag characters to boolean names is as follows:
flag_map = {
"!": "spoiler",
"#": "stickied",
">": "pinned",
"A": "archived",
"C": "is_crosspostable",
"c": "is_original_content",
"E": "edited",
"e": "is_meta",
"G": "can_gild",
"H": "hidden",
"i": "is_robot_indexable",
"L": "allow_live_comments",
"l": "locked",
"m": "is_reddit_media_domain",
"M": "over_18",
"O": "contest_mode",
"q": "quarantine",
"s": "is_self",
"v": "is_video",
}
For comments:
flag_map = {
"#": "stickied",
"A": "archived",
"E": "edited",
"G": "can_gild",
"H": "hidden",
"l": "locked",
"=": "score_hidden",
"P": "author_premium",
"R": "send_replies",
"O": "can_mod_post",
"N": "no_follow",
}
Within named conversation, only the over_18 flag for submissions are used.
Dataset Creation
Curation Rationale
Reddit's distinctive architecture and commenting system, featuring deeply nested comment threads, offer a wealth of conversational data that can be transformed into coherent, linear dialogues.
Given Reddit's longevity since 2005, a vast reservoir of information awaits exploration and utilization, particularly as modern language models have already tapped into this resource.
Filtering Subreddit Quality
In contrast to UpVoteWeb's methodologies, our aim is to construct a more inclusive dataset while still maintaining standards. To achieve this balance, we implemented a pruning process targeting subreddits lacking valuable content according to three key metrics:
- Engagement: The ratio of total comments to total submissions, reflecting a subreddit's activity level
- Richness The square of the proportion of media submissions among all posts, indicating multimedia content density.
- Diversity The combined count of unique authors in comments and submissions divided by the count of unique submission authors, signaling the breadth of community participation.
In practice, it looks something like this:
# ...
engagement = comment_data["comments"] / submission_data["submissions"]
richness = (submission_data["media"] / submission_data["submissions"]) ** 2
diversity = (
comment_data["authors"] + submission_data["authors"]
) / submission_data["submissions"]
Furthermore, we enforce certain baseline thresholds for submissions and author counts:
if (
stats_data["submission"]["authors"] < 70 # Total unique authors
or stats_data["comment"]["authors"] < 20 # Total unique commentors
or stats_data["submission"]["submissions"] < 450 # Total submissions count
or stats_data["comment"]["comments"] < 585 # Total comments count
):
# Skip the subreddit
By applying these criteria, we have narrowed down to approximately 62,000 high-quality subreddits.
Valuable Submissions
To eliminate subreddits with an insufficient number of meaningful contributions, we first identify "useful threads" characterized by either:
- At least five responses,
- Or, if the original post is textual, exceeding 2,500 characters.
A randomized threshold between 5 and 20 is established, and any subreddit falling short of this randomly generated requirement is excluded.
In practice:
fuzz_threads = random.randrange(FUZZY_SUBREDDIT[0], FUZZY_SUBREDDIT[1])
if usable_threads <= fuzz_threads:
logger.debug(
f"/r/{REDDIT} has {usable_threads}, which is less than {fuzz_threads}. Skipping subreddit entirely..."
)
# Cleanup...
return
This step streamlines the dataset by removing less active communities.
Refining Comment Selection
Post-thread-level filtration, comments undergo additional scrutiny based on:
- Comments with a score lower than -4 are discarded.
- Within threads boasting over 50 comments, those nested deeper than six levels are removed.
- If a comment thread's cumulative score dips below zero, the remainder of that thread is pruned.
- Child comments linked to any pruned parent under points 2 or 3 are also eliminated.
An occasional challenge arises when comments lack identifiable parent comments, disrupting the conversation flow; in such cases, the entire comment chain has been deleted.
For more infomation, refer to the scripts provided alongside this repo. Specifically RedditScoring.py for subreddit filtering and RedditThreader.py for per thread filtering. We have also included a NOTES.md for those looking to replicate it on their hardware. You might need a lot of storage (3x current datasize) though.
Source Data
This dataset is a filtered collection of posts and comments from the beginning of reddit to up to end of 2023.
Considerations for Using the Data
Social Impact of Dataset
With the release of this dataset, we aim to make this development resource available to the community at large.
Discussion of Biases
We've decided not to censor out NSFW or toxic content. This allows for better toxic analysis and a varied dataset.
Additional Information
Recursal's Vision
To make AI accessible to everyone, regardless of language, or economical status
This is the collective goal of the RWKV Open Source foundation and Recursal AI, the commercial entity who backs it.
We believe that AI should not be controlled by a select few individual organization. And that it should be made accessible regardless if you are rich or poor, or a native speaker of english.
About RWKV
RWKV is an Open Source, non profit group, under the linux foundation. Focused on developing the RWKV AI architecture, in accordence to our vision.
The RWKV architecture scales efficiently and economically. As an RNN & Transformer hybrid, it is able to provide the performance similar to leading transformer models, while having the compute and energy efficiency of an RNN based architecture.
You can find out more about the project, and latest models, at the following
About Recursal AI
Recursal AI, is the commercial entity built to provide support for RWKV model development and users, while providing commercial services via its public cloud, or private-cloud / on-premise offerings.
As part of our vision. Our commitment, is to ensure open source development and access to the best foundational AI models and datasets.
The following dataset/models provided here, is part of that commitment.
You can find out more about recursal AI here
Licensing Information
Since this dataset is derived from a public crawl of reddit, the original content may be subject to copyright and other licensing terms set by the original site owner and/or the content creators.
Additionally, this dataset is for research and archival purposes only.
Citation Information
If you use this dataset in your research or project, please cite it as follows:
@dataset{OKReddit,
title = {OKReddit},
year = {2024},
publisher = {KaraKaraWitch},
url = {<https://huggingface.co./datasets/recursal/OKReddit-ReleaseCandidate3>}
}
Additionally, pleace cite the following source bibtex as well.
@article{,
title= {Reddit comments/submissions 2005-06 to 2023-12},
journal= {},
author= {stuck_in_the_matrix, Watchful1, RaiderBDev},
year= {},
url= {},
abstract= {Reddit comments and submissions from 2005-06 to 2023-09 collected by pushshift and u/RaiderBDev.
These are zstandard compressed ndjson files. Example python scripts for parsing the data can be found here https://github.com/Watchful1/PushshiftDumps
The more recent dumps are collected by u/RaiderBDev and questions can be submitted here https://github.com/ArthurHeitmann/arctic_shift},
keywords= {reddit},
terms= {},
license= {},
superseded= {}
}
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