metadata

size_categories:
  - 10K<n<100K
pretty_name: OKReddit Visionary
task_categories:
  - text-generation
  - fill-mask
task_ids:
  - language-modeling
  - masked-language-modeling
source_datasets:
  - original
language:
  - en

Dataset Summary

OKReddit Visionary is a collection of 50 GiB (~74K pairs) of image Question & Answers. 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: Scripts folder 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.)

Languages

All the questions and answers should be in english at this size point.

Dataset Structure

Data Instances

The dataset can be loaded with webdataset. Do note that there are multiple extensions to check: jpg, jpeg or png. They have not been reconverted to preserve the original file from reddit.

import webdataset as wds
# After concatting, you may use the file like a regular dataset.

# The dataset is compatible with WebDataset format. Example...

tar_file = "PackedTar.tar"

hf_dataset = wds.WebDataset(str(tar_root)).decode("pil")

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.

Recursal Waifus (The banner image) are licensed under CC-BY-SA. They do not represent the related websites in any official capacity unless otherwise or announced by the website. You may use them as a banner image. However, you must always link back to the dataset.

Citation Information

If you use this dataset in your research or project, please cite it as follows:

@dataset{OKRedditVisionary,
  title = {OKReddit-Visionary},
  year = {2024},
  publisher = {KaraKaraWitch},
  url = {<https://huggingface.co/datasets/recursal/OKReddit-Visionary>}
}

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= {}
}