README.md

metadata

pretty_name: anon-greentext
language:
  - en
size_categories:
  - 100K<n<1M
task_categories:
  - text-generation
tags:
  - greentext
  - 4chan
  - reddit
  - ocr
  - humor
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: validation
        path: data/val-*.parquet

anon-greentext

157,342 greentext stories transcribed from screenshots posted to r/greentext, r/4chan, r/4tran and r/classic4chan between 2010 and October 2026. This is the exact data anon-1b was fine-tuned on, with the same train/validation split, and every row links back to the Reddit post it came from.

split	rows	tokens (Gemma 3)
train	154,204	40.49M
validation	3,138	0.79M

By subreddit: r/4chan 81,349 · r/greentext 64,406 · r/4tran 9,890 · r/classic4chan 1,697.

Columns

column	what it is
text	The transcribed screenshot: board post headers (Anonymous 08/05/18(Sun)00:07:46 No.776285187), > lines, and replies, separated by blank lines.
subreddit	Subreddit of the representative Reddit post.
post_id	Reddit post id of the representative post (the highest-scoring post of this image).
permalink	https://www.reddit.com/comments/<post_id>/
image_index	Position of the image in the post (galleries have several).
image_url	Original image URL from the post. Older imgur links may be dead.
title	Reddit post title.
score, num_comments	Reddit score and comment count when the data was collected.
created_utc	Post time, Unix seconds.
all_posts	Every Reddit post (subreddit + id) that shared this exact image, so reposts and crossposts can be traced too.
recovered	true when the screenshot wasn't a normal imageboard capture (a cropped story, a phone screenshot) and the greentext was pulled from its other text.
id	sha256 of the image. A story too long for one 8,192-token training example is split, giving ids ending in #0, #1…; doc_id is the image hash shared by the parts, and parts always stay in the same split.
train_header	The prompt header anon-1b saw for this row, e.g. sub: r/4chan | score: 100-500. Each field was replaced with [N/A] half the time.

How it was built

1. Collection. Image posts from the four subreddits: older posts from Reddit data dumps, plus 2026 posts collected separately. A post was kept at score ≥ 100, or for 2023 posts with unreliable scores, at ≥ 20 comments.
2. Transcription. Every image was transcribed by gpt-6-luna into a post-by-post structure. Text the model couldn't read is marked [illegible] (794 rows).
3. Filtering.
   • Images that weren't imageboard screenshots and had no recoverable greentext (at least two > lines) were dropped: 8,177.
   • Stories with under 20 characters of post text were dropped: 2,849.
   • 518 images the transcription step couldn't process were dropped.
4. Deduplication. Exact reposts of an image collapse into one row. Near-duplicate transcriptions (MinHash LSH over the text) were clustered and only the highest-scoring copy kept, dropping 32,335.
5. Split. 2% held out for validation by a seeded hash of the image, so a story never straddles train and validation.

Notes

• The text is OCR of screenshots, so expect the occasional transcription error, especially on old low-resolution images.
• Scores are a snapshot from collection time, not live values.
• The posts belong to their original authors. This dataset is a transcription for research, with links back to every source.

Related

• Model: scratchyone/anon-1b, Gemma 3 1B fine-tuned on this data plus an 8% general-text replay mix (FineWeb sample-10BT, and comments from r/CasualConversation and r/tifu), which isn't included here.