---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Summary

The "humor-chains" dataset is a machine-filtered collection of the most upvoted Reddit submissions and their replies on humor-related subreddits. Generally, a humor chain is when a short post triggers a chain of one or more replies *that Redditors find entertaining*. In other words, some entries might be NSFW, topical, or internal jokes.

For example ([original thread](https://www.reddit.com/r/funny/comments/cmb3z/im_dating_a_midget/?sort=top)):
![Screenshot-reddit-chain.png](https://cdn-uploads.huggingface.co/production/uploads/6564a7606c9793d92e715f33/YKtjZD6V1auH1DVaofQo2.png)

# Dataset Details

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/deed.en)

Note that the dataset was created from Reddit's public user-generated content. 
Reddit's license states in section "2.4 User Content" that such content is [ownership of individual reddit users](https://www.redditinc.com/policies/data-api-terms#:~:text=2.4%20User%20Content,not%20by%20Reddit.) and not Reddit itself.
For privacy reasons, all usernames were removed and replaced by "human" (original submission) and "gpt" (all replies). 
That format is convenient for fine-tuning GPT models, e.g., in [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) that is ["sharegpt" format type](https://openaccess-ai-collective.github.io/axolotl/docs/dataset-formats/conversation.html#sharegpt).

UPDATE 2024-7-9: Axolotl's finetuning formats expect a single GPT reply, so dataset was reduced to include submission and a single top reply.

## Dataset Creation

The dataset was created using the following procedure:
1. Reddit's API was not used directly, as API license switched to a more restrictive license (see [2023 Reddit API controversy](https://en.wikipedia.org/wiki/2023_Reddit_API_controversy)). Instead, I used older Reddit dump [Subreddit comments/submissions 2005-06 to 2023-12](https://academictorrents.com/details/56aa49f9653ba545f48df2e33679f014d2829c10). Inside the big torrent file are zstandard compressed ndjson files, each representing a subreddit.
2. Downloaded a portion of subreddits from [List of funny subreddits](https://www.reddit.com/r/redditlists/comments/128ayc/list_of_funny_subreddits/).
3. Python script filtered .jsonl files inside .zst to include only the most upvoted thread for each submission.
4. Filtered submissions satisfy ALL of the following:
   * score >= min (10 upvotes),
   * total len is shorter than max_text_len (256 characters),
   * title is not "[deleted by user]",
   * selftext is not "[removed]" or "[deleted]",
   * links to itself (Reddit link) and not an image, video, or external link,
   * doesn't start with a link,
   * doesn't have a thumbnail.
5. Filtered comments satisfy ALL of the following:
   * score >= min (5 upvotes),
   * total len is shorter than max_text_len (256 characters),
   * Only top upvoted reply at each reply level is included.
6. All filtered threads were further filtered with a local model for humor detection: [Humor-Research/humor-detection-comb-23](https://huggingface.co/Humor-Research/humor-detection-comb-23/tree/main), which was selected because it performed the best on [lm233/humor_train](https://huggingface.co/datasets/lm233/humor_train?row=9) dataset. Threads not detected as funny were removed.

All code used for filtering of the dataset is available on GitHub: [reddit-filter](https://github.com/ZSvedic/reddit-filter)
## Citation

```
@misc{ZSvedic_humor_chains,
  title={humor-chains},
  author={Zeljko Svedic},
  year={2024},
  url={https://huggingface.co/datasets/ZSvedic/humor-chains/blob/main/README.md}
}
```

## Curated by

[Zeljko Svedic](https://huggingface.co/ZSvedic)