datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
yt-personalities
Dataset Information
name: Youtubers by Big Five Personality Traits
license: gpl-3.0
Description
description: |
In trait theory, the Big Five personality traits (sometimes known as the five-factor model of personality or OCEAN or CANOE models) are a group of five characteristics used to study personality:
Openness to Experience (inventive/curious vs. consistent/cautious)
Conscientiousness (efficient/organized vs. extravagant/careless)
Extraversion (outgoing/energetic… See the full description on the dataset page: https://huggingface.co/datasets/visualcomments/yt-personalities.personalized_passkey_retrieval
Dataset Summary
This dataset contains the data for personalized passkey retrieval task in the paper Improving Text Embeddings with Large Language Models.
Data Fields
query: a string feature.
candidates: List of string feature, 100 candidates for each query.
label: a int32 feature, the index of the correct candidate in the candidates list, always 0.
context_length: a int32 feature, the approximate length for the candidate documents.
How to use this dataset… See the full description on the dataset page: https://huggingface.co/datasets/intfloat/personalized_passkey_retrieval.PocketDoc__Dans-PersonalityEngine-v1.0.0-8b-details
Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-v1.0.0-8b
Dataset automatically created during the evaluation run of model PocketDoc/Dans-PersonalityEngine-v1.0.0-8b
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/PocketDoc__Dans-PersonalityEngine-v1.0.0-8b-details.personal-query-grocery-and-gourmet-food
Personal Query: Grocery and Gourmet Food
This dataset contains personalized product search queries for the Grocery_and_Gourmet_Food category.
Each record is built from the Personal Query pipeline:
Stage 6 generated correct personalized queries.
Stage 7 injected user-specific error query variants when a matching error pattern was available.
Stage 5 provided the user profile complexity level.
Files
data.jsonl: all correct Stage 6 queries. Rows without Stage 7 error query… See the full description on the dataset page: https://huggingface.co/datasets/xxxxdszz/personal-query-grocery-and-gourmet-food.PersonalizedDeepResearchBenchThis is the dataset for the paper Towards Personalized Deep Research: Benchmarks and Evaluations.
user_study-preference-personalized_0423_base_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0423_base
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
PocketDoc__Dans-PersonalityEngine-V1.2.0-24b-details
Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
Dataset automatically created during the evaluation run of model PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/PocketDoc__Dans-PersonalityEngine-V1.2.0-24b-details.repro-the-personality-illusion-traces
Agent traces
Agent sessions published from a Trackio Logbook.
personalization-reddit-multiturn
personalization-reddit-multiturn
Multi-turn (question, preferred_answer, full_conversation) records mined
from Reddit. Companion to dipikakhullar/personalization-reddit: same
OP-thanks-reply heuristic for identifying the preferred answerer, but
this dataset additionally captures any contiguous back-and-forth between
the OP and that single answerer after the thanks.
A record is only emitted when there is at least one further turn beyond
the OP's thanks reply.
Splits… See the full description on the dataset page: https://huggingface.co/datasets/dipikakhullar/personalization-reddit-multiturn.user_study-preference-personalized_0505_NP1_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0505_NP1
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
personal-query-baby-products
Personal Query: Baby Products
This dataset contains personalized product search queries for the Baby_Products category.
Each record is built from the Personal Query pipeline:
Stage 6 generated correct personalized queries.
Stage 7 injected user-specific error query variants when a matching error pattern was available.
Stage 5 provided the user profile complexity level.
Files
data.jsonl: all correct Stage 6 queries. Rows without Stage 7 error query keep error_query as… See the full description on the dataset page: https://huggingface.co/datasets/xxxxdszz/personal-query-baby-products.personal-query-pet-supplies
Personal Query: Pet Supplies
This dataset contains personalized product search queries for the Pet_Supplies category.
Each record is built from the Personal Query pipeline:
Stage 6 generated correct personalized queries.
Stage 7 injected user-specific error query variants when a matching error pattern was available.
Stage 5 provided the user profile complexity level.
Files
data.jsonl: all correct Stage 6 queries. Rows without Stage 7 error query keep error_query as… See the full description on the dataset page: https://huggingface.co/datasets/xxxxdszz/personal-query-pet-supplies.adaption-personal-finance-advice-dialogues
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-personal_finance_advice_dialogues
This dataset contains multi-turn conversational samples between users and an AI assistant focused on personal finance topics such as budgeting, investing, insurance, and taxes. Each entry follows a pattern where a user presents an initial scenario, provides an update with new constraints or events, and receives tailored financial advice that adapts… See the full description on the dataset page: https://huggingface.co/datasets/Azfarhashmi/adaption-personal-finance-advice-dialogues.personalized-query
Personalized Query
This repository contains three personalized product-search query datasets in one Hugging Face dataset page.
Each config corresponds to one product category:
baby: Baby Products
grocery: Grocery and Gourmet Food
pets: Pet Supplies
Each config has two splits:
full: all correct Stage 6 queries. Rows without Stage 7 error query keep error_query as null.
paired: only rows where a correct query has a paired error query.
Dataset Size
Config… See the full description on the dataset page: https://huggingface.co/datasets/xxxxdszz/personalized-query.user_study-preference-personalized_0505_NP2_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0505_NP2
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
PocketDoc__Dans-PersonalityEngine-V1.1.0-12b-details
Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
Dataset automatically created during the evaluation run of model PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/PocketDoc__Dans-PersonalityEngine-V1.1.0-12b-details.user_study-preference-personalized_0505_base_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0505_base
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
YNTP-100
Dataset Card for annonymous_100
Dataset Summary
The annonymous_100 dataset is a conversation dataset between English, Chinese, and Japanese users and NPCs during a five-day shared house experience game. This dataset consists of responses to questions from NPCs over five days, with 33 English users, 34 Chinese users, and 33 Japanese users.
Language(s)
The dataset contains conversations in English, Chinese, and Japanese.
Dataset Structure
Data… See the full description on the dataset page: https://huggingface.co/datasets/Personalized-Alignment/YNTP-100.user_study-preference-personalized_0423_4_2_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0423_4_2
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
user_study-preference-personalized_0505_NP3_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0505_NP3
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
HH-Red-Team-Personal-Copyuser_study-preference-personalized_0423_base_personalized_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0423_base_personalized
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
user_study-preference-personalized_0505_base_personalized_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0505_base_personalized
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
user_study-preference-personalized_0423_5_2_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0423_5_2
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
user_study-preference-personalized_0423_6_2_REAL_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0423_6_2_REAL
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
user_study-preference-personalized_BASE_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_BASE
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
trait-vectorsuser_study-preference-personalized_0417_250_filtered
Filtered user study dataset
Source repo: ehejin/user_study-preference-personalized_0417_250
Each row is ONE item review (pre-rating, conversation, post-rating). Submission-level
fields (prolific_pid, demographics, background) are duplicated across rows that share
a submission.
The 25-50 rows here are the FIRST review for each unique pool index, selected the same
way the analysis plot uses — see scripts/plot_vote_shift_3way.py.
Total rows: 50
archetype-personalization