Datasets:

scratchyone
/

anon-greentext

---
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](https://huggingface.co/scratchyone/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](https://huggingface.co/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.

	---
	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](https://huggingface.co/scratchyone/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](https://huggingface.co/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.