configs:
- config_name: default
data_files:
- split: AskAcademia
path: extracted/pairs/sub-AskAcademia.jsonl
- split: AskBaking
path: extracted/pairs/sub-AskBaking.jsonl
- split: AskCulinary
path: extracted/pairs/sub-AskCulinary.jsonl
- split: AskDocs
path: extracted/pairs/sub-AskDocs.jsonl
- split: AskEngineers
path: extracted/pairs/sub-AskEngineers.jsonl
- split: AskHistorians
path: extracted/pairs/sub-AskHistorians.jsonl
- split: AskStatistics
path: extracted/pairs/sub-AskStatistics.jsonl
- split: Coffee
path: extracted/pairs/sub-Coffee.jsonl
- split: DIY
path: extracted/pairs/sub-DIY.jsonl
- split: German
path: extracted/pairs/sub-German.jsonl
- split: JapanTravel
path: extracted/pairs/sub-JapanTravel.jsonl
- split: LanguageTechnology
path: extracted/pairs/sub-LanguageTechnology.jsonl
- split: LearnJapanese
path: extracted/pairs/sub-LearnJapanese.jsonl
- split: Sewing
path: extracted/pairs/sub-Sewing.jsonl
- split: Shoestring
path: extracted/pairs/sub-Shoestring.jsonl
- split: askphilosophy
path: extracted/pairs/sub-askphilosophy.jsonl
- split: askscience
path: extracted/pairs/sub-askscience.jsonl
- split: bicycling
path: extracted/pairs/sub-bicycling.jsonl
- split: gardening
path: extracted/pairs/sub-gardening.jsonl
- split: golang
path: extracted/pairs/sub-golang.jsonl
- split: homeimprovement
path: extracted/pairs/sub-homeimprovement.jsonl
- split: houseplants
path: extracted/pairs/sub-houseplants.jsonl
- split: languagelearning
path: extracted/pairs/sub-languagelearning.jsonl
- split: learnjavascript
path: extracted/pairs/sub-learnjavascript.jsonl
- split: learnpython
path: extracted/pairs/sub-learnpython.jsonl
- split: rust
path: extracted/pairs/sub-rust.jsonl
- split: solotravel
path: extracted/pairs/sub-solotravel.jsonl
- split: tea
path: extracted/pairs/sub-tea.jsonl
- split: woodworking
path: extracted/pairs/sub-woodworking.jsonl
personalization-reddit
Per-subreddit (query, preferred_answer) pairs mined from Reddit using an
OP-thanks-reply heuristic: when the original poster (OP) replies to a
comment with thanks/gratitude, that parent comment is treated as their
preferred answer to their own question.
Source
Raw post + comment dumps from the
arctic_shift Pushshift
mirror, fetched per-subreddit (entire history through the fetch date) and
extracted with the pipeline in may_15/reddit_pipeline/ of the
personalization repo.
Raw NDJSON dumps are kept locally and are not redistributed here.
Splits
One split per subreddit; pick from the dropdown in the data viewer or load programmatically:
from datasets import load_dataset
ds = load_dataset("dipikakhullar/personalization-reddit", split="AskHistorians")
Files
extracted/
pairs/sub-<subreddit>.jsonl # one (query, preferred_answer) per line
stats/sub-<subreddit>.json # funnel counts per subreddit
Record schema (pairs/sub-*.jsonl)
| field | type | description |
|---|---|---|
user_id |
str | anonymized OP id (HMAC of Reddit username, see anon.py) |
timestamp |
str | post creation, ISO 8601 UTC |
subreddit |
str | source subreddit name |
query |
str | post title, with selftext appended if present |
preferred_answer |
str | body of the comment OP thanked (via parent of the thanks reply) |
top_comment |
str | null | body of the highest-scoring non-OP comment (may equal preferred) |
op_metadata |
object | OP user fields captured at post time; see below |
answerer_metadata |
object | answerer user fields captured at comment time; see below |
metadata |
object | see below |
op_metadata sub-object (mirrors the multiturn dataset):
| field | type | description |
|---|---|---|
user_id |
str | same as top-level user_id |
author_flair_text |
str | null | OP's flair text at post time |
author_flair_css_class |
str | null | flair css class |
author_flair_type |
str | null | e.g. "text", "richtext" |
author_flair_background_color |
str | null | hex color |
author_flair_text_color |
str | null | "light" / "dark" |
answerer_metadata sub-object:
| field | type | description |
|---|---|---|
user_id |
str | same as metadata.answerer_anon_id |
author_flair_text |
str | null | answerer's flair text at comment time |
author_flair_css_class |
str | null | flair css class |
metadata sub-object:
| field | type | description |
|---|---|---|
post_id |
str | Reddit submission id |
post_score |
int | submission score at fetch time |
answer_comment_id |
str | comment id of the preferred answer |
answer_score |
int | preferred-answer score at fetch time |
answerer_anon_id |
str | anonymized author id of the preferred answer |
top_comment_id |
str | null | comment id of the top-scoring non-OP comment |
top_comment_score |
int | null | top comment score |
top_comment_anon_id |
str | null | anonymized top-comment author id |
top_equals_preferred |
bool | whether the preferred answer is also the top comment |
thanks_reply_id |
str | OP's thanks-reply comment id (the signal that triggered the pair) |
thanks_reply_score |
int | score of OP's thanks reply |
thanks_reply_text |
str | body of OP's thanks reply |
thanks_reply_timestamp |
str | thanks reply creation, ISO 8601 UTC |
Heuristic — OP thanks-reply
For each post that passes a question filter:
- Find comments whose author == OP and whose body matches a "thanks"
pattern (see
signals.py::is_thanks_reply). - The parent of each such reply is recorded as a candidate "preferred answer".
- Materialize each candidate into a pair, joining post metadata + answer body
- thanks-reply context.
Bots and deleted/removed authors are filtered out before pair emission
(signals.py, subreddits.py::BOT_AUTHORS).
Anonymization
Reddit usernames are hashed via HMAC-SHA256 with a per-run secret salt
(anon.py::anon_user_id) before being written. Post/comment ids and text
bodies are kept verbatim — content from public Reddit threads can still be
re-identified by searching the post id or quoting the body.
Subreddits included in this snapshot
See extracted/stats/ for the list and per-sub funnel counts
(rs_records_scanned, keep_posts, thanks_refs, pairs_emitted, etc.).