README.md

---
license: mit
task_categories:
- question-answering
language:
- en
size_categories:
- 1M<n<10M
dataset_info:
- config_name: database
  features:
  - name: content
    dtype: string
  splits:
  - name: depth_20_size_25_seed_1
    num_bytes: 16053
    num_examples: 1
  - name: depth_20_size_25_seed_2
    num_bytes: 16117
    num_examples: 1
  - name: depth_20_size_25_seed_3
    num_bytes: 15387
    num_examples: 1
  - name: depth_20_size_50_seed_1
    num_bytes: 25163
    num_examples: 1
  - name: depth_20_size_50_seed_2
    num_bytes: 25205
    num_examples: 1
  - name: depth_20_size_50_seed_3
    num_bytes: 25015
    num_examples: 1
  - name: depth_20_size_100_seed_1
    num_bytes: 44636
    num_examples: 1
  - name: depth_20_size_100_seed_2
    num_bytes: 44461
    num_examples: 1
  - name: depth_20_size_100_seed_3
    num_bytes: 43392
    num_examples: 1
  - name: depth_20_size_200_seed_1
    num_bytes: 81118
    num_examples: 1
  - name: depth_20_size_200_seed_2
    num_bytes: 81001
    num_examples: 1
  - name: depth_20_size_200_seed_3
    num_bytes: 79531
    num_examples: 1
  - name: depth_20_size_300_seed_1
    num_bytes: 118709
    num_examples: 1
  - name: depth_20_size_300_seed_2
    num_bytes: 117125
    num_examples: 1
  - name: depth_20_size_300_seed_3
    num_bytes: 116605
    num_examples: 1
  - name: depth_20_size_400_seed_1
    num_bytes: 154360
    num_examples: 1
  - name: depth_20_size_400_seed_2
    num_bytes: 154134
    num_examples: 1
  - name: depth_20_size_400_seed_3
    num_bytes: 153304
    num_examples: 1
  - name: depth_20_size_500_seed_1
    num_bytes: 191003
    num_examples: 1
  - name: depth_20_size_500_seed_2
    num_bytes: 190407
    num_examples: 1
  - name: depth_20_size_500_seed_3
    num_bytes: 189702
    num_examples: 1
  - name: depth_20_size_1000_seed_1
    num_bytes: 374830
    num_examples: 1
  - name: depth_20_size_1000_seed_2
    num_bytes: 371639
    num_examples: 1
  - name: depth_20_size_1000_seed_3
    num_bytes: 372656
    num_examples: 1
  - name: depth_20_size_2500_seed_1
    num_bytes: 926185
    num_examples: 1
  - name: depth_20_size_2500_seed_2
    num_bytes: 923497
    num_examples: 1
  - name: depth_20_size_2500_seed_3
    num_bytes: 924416
    num_examples: 1
  - name: depth_20_size_5000_seed_1
    num_bytes: 1847718
    num_examples: 1
  - name: depth_20_size_5000_seed_2
    num_bytes: 1845391
    num_examples: 1
  - name: depth_20_size_5000_seed_3
    num_bytes: 1846249
    num_examples: 1
  - name: depth_20_size_10000_seed_1
    num_bytes: 3686132
    num_examples: 1
  - name: depth_20_size_10000_seed_2
    num_bytes: 3693544
    num_examples: 1
  - name: depth_20_size_10000_seed_3
    num_bytes: 3686026
    num_examples: 1
  download_size: 7115174
  dataset_size: 22380711
- config_name: question-answer
  features:
  - name: id
    dtype: string
  - name: question
    dtype: string
  - name: intermediate_answers
    dtype: string
  - name: answer
    sequence: string
  - name: prolog
    struct:
    - name: query
      dtype: string
    - name: answer
      dtype: string
  - name: template
    sequence: string
  - name: type
    dtype: int64
  - name: difficulty
    dtype: int64
  splits:
  - name: depth_20_size_25_seed_1
    num_bytes: 293183
    num_examples: 500
  - name: depth_20_size_25_seed_2
    num_bytes: 294498
    num_examples: 500
  - name: depth_20_size_25_seed_3
    num_bytes: 286264
    num_examples: 500
  - name: depth_20_size_50_seed_1
    num_bytes: 299562
    num_examples: 500
  - name: depth_20_size_50_seed_2
    num_bytes: 303664
    num_examples: 500
  - name: depth_20_size_50_seed_3
    num_bytes: 293959
    num_examples: 500
  - name: depth_20_size_100_seed_1
    num_bytes: 301175
    num_examples: 500
  - name: depth_20_size_100_seed_2
    num_bytes: 309569
    num_examples: 500
  - name: depth_20_size_100_seed_3
    num_bytes: 298646
    num_examples: 500
  - name: depth_20_size_200_seed_1
    num_bytes: 300673
    num_examples: 500
  - name: depth_20_size_200_seed_2
    num_bytes: 304991
    num_examples: 500
  - name: depth_20_size_200_seed_3
    num_bytes: 297196
    num_examples: 500
  - name: depth_20_size_300_seed_1
    num_bytes: 305996
    num_examples: 500
  - name: depth_20_size_300_seed_2
    num_bytes: 301507
    num_examples: 500
  - name: depth_20_size_300_seed_3
    num_bytes: 299086
    num_examples: 500
  - name: depth_20_size_400_seed_1
    num_bytes: 307257
    num_examples: 500
  - name: depth_20_size_400_seed_2
    num_bytes: 314689
    num_examples: 500
  - name: depth_20_size_400_seed_3
    num_bytes: 299549
    num_examples: 500
  - name: depth_20_size_500_seed_1
    num_bytes: 308562
    num_examples: 500
  - name: depth_20_size_500_seed_2
    num_bytes: 322956
    num_examples: 500
  - name: depth_20_size_500_seed_3
    num_bytes: 300467
    num_examples: 500
  - name: depth_20_size_1000_seed_1
    num_bytes: 307844
    num_examples: 500
  - name: depth_20_size_1000_seed_2
    num_bytes: 313826
    num_examples: 500
  - name: depth_20_size_1000_seed_3
    num_bytes: 307334
    num_examples: 500
  - name: depth_20_size_2500_seed_1
    num_bytes: 314572
    num_examples: 500
  - name: depth_20_size_2500_seed_2
    num_bytes: 323000
    num_examples: 500
  - name: depth_20_size_2500_seed_3
    num_bytes: 312583
    num_examples: 500
  - name: depth_20_size_5000_seed_1
    num_bytes: 338703
    num_examples: 500
  - name: depth_20_size_5000_seed_2
    num_bytes: 344577
    num_examples: 500
  - name: depth_20_size_5000_seed_3
    num_bytes: 320320
    num_examples: 500
  - name: depth_20_size_10000_seed_1
    num_bytes: 341720
    num_examples: 500
  - name: depth_20_size_10000_seed_2
    num_bytes: 341562
    num_examples: 500
  - name: depth_20_size_10000_seed_3
    num_bytes: 361873
    num_examples: 500
  download_size: 2482076
  dataset_size: 10271363
- config_name: text-corpus
  features:
  - name: title
    dtype: string
  - name: article
    dtype: string
  splits:
  - name: depth_20_size_25_seed_1
    num_bytes: 12734
    num_examples: 26
  - name: depth_20_size_25_seed_2
    num_bytes: 12888
    num_examples: 25
  - name: depth_20_size_25_seed_3
    num_bytes: 11729
    num_examples: 25
  - name: depth_20_size_50_seed_1
    num_bytes: 25543
    num_examples: 51
  - name: depth_20_size_50_seed_2
    num_bytes: 25910
    num_examples: 50
  - name: depth_20_size_50_seed_3
    num_bytes: 25426
    num_examples: 51
  - name: depth_20_size_100_seed_1
    num_bytes: 52632
    num_examples: 101
  - name: depth_20_size_100_seed_2
    num_bytes: 52884
    num_examples: 101
  - name: depth_20_size_100_seed_3
    num_bytes: 51329
    num_examples: 101
  - name: depth_20_size_200_seed_1
    num_bytes: 105140
    num_examples: 201
  - name: depth_20_size_200_seed_2
    num_bytes: 104014
    num_examples: 203
  - name: depth_20_size_200_seed_3
    num_bytes: 103079
    num_examples: 202
  - name: depth_20_size_300_seed_1
    num_bytes: 159074
    num_examples: 302
  - name: depth_20_size_300_seed_2
    num_bytes: 155315
    num_examples: 303
  - name: depth_20_size_300_seed_3
    num_bytes: 155195
    num_examples: 304
  - name: depth_20_size_400_seed_1
    num_bytes: 208421
    num_examples: 403
  - name: depth_20_size_400_seed_2
    num_bytes: 207117
    num_examples: 403
  - name: depth_20_size_400_seed_3
    num_bytes: 206623
    num_examples: 404
  - name: depth_20_size_500_seed_1
    num_bytes: 259954
    num_examples: 503
  - name: depth_20_size_500_seed_2
    num_bytes: 258230
    num_examples: 503
  - name: depth_20_size_500_seed_3
    num_bytes: 257583
    num_examples: 504
  - name: depth_20_size_1000_seed_1
    num_bytes: 518503
    num_examples: 1007
  - name: depth_20_size_1000_seed_2
    num_bytes: 514399
    num_examples: 1006
  - name: depth_20_size_1000_seed_3
    num_bytes: 516161
    num_examples: 1008
  - name: depth_20_size_2500_seed_1
    num_bytes: 1294832
    num_examples: 2516
  - name: depth_20_size_2500_seed_2
    num_bytes: 1291796
    num_examples: 2518
  - name: depth_20_size_2500_seed_3
    num_bytes: 1291338
    num_examples: 2518
  - name: depth_20_size_5000_seed_1
    num_bytes: 2594123
    num_examples: 5030
  - name: depth_20_size_5000_seed_2
    num_bytes: 2588081
    num_examples: 5029
  - name: depth_20_size_5000_seed_3
    num_bytes: 2588663
    num_examples: 5039
  - name: depth_20_size_10000_seed_1
    num_bytes: 5175231
    num_examples: 10069
  - name: depth_20_size_10000_seed_2
    num_bytes: 5189283
    num_examples: 10058
  - name: depth_20_size_10000_seed_3
    num_bytes: 5179131
    num_examples: 10070
  download_size: 10322976
  dataset_size: 31192361
configs:
- config_name: database
  data_files:
  - split: depth_20_size_25_seed_1
    path: database/depth_20_size_25_seed_1-*
  - split: depth_20_size_25_seed_2
    path: database/depth_20_size_25_seed_2-*
  - split: depth_20_size_25_seed_3
    path: database/depth_20_size_25_seed_3-*
  - split: depth_20_size_50_seed_1
    path: database/depth_20_size_50_seed_1-*
  - split: depth_20_size_50_seed_2
    path: database/depth_20_size_50_seed_2-*
  - split: depth_20_size_50_seed_3
    path: database/depth_20_size_50_seed_3-*
  - split: depth_20_size_100_seed_1
    path: database/depth_20_size_100_seed_1-*
  - split: depth_20_size_100_seed_2
    path: database/depth_20_size_100_seed_2-*
  - split: depth_20_size_100_seed_3
    path: database/depth_20_size_100_seed_3-*
  - split: depth_20_size_200_seed_1
    path: database/depth_20_size_200_seed_1-*
  - split: depth_20_size_200_seed_2
    path: database/depth_20_size_200_seed_2-*
  - split: depth_20_size_200_seed_3
    path: database/depth_20_size_200_seed_3-*
  - split: depth_20_size_300_seed_1
    path: database/depth_20_size_300_seed_1-*
  - split: depth_20_size_300_seed_2
    path: database/depth_20_size_300_seed_2-*
  - split: depth_20_size_300_seed_3
    path: database/depth_20_size_300_seed_3-*
  - split: depth_20_size_400_seed_1
    path: database/depth_20_size_400_seed_1-*
  - split: depth_20_size_400_seed_2
    path: database/depth_20_size_400_seed_2-*
  - split: depth_20_size_400_seed_3
    path: database/depth_20_size_400_seed_3-*
  - split: depth_20_size_500_seed_1
    path: database/depth_20_size_500_seed_1-*
  - split: depth_20_size_500_seed_2
    path: database/depth_20_size_500_seed_2-*
  - split: depth_20_size_500_seed_3
    path: database/depth_20_size_500_seed_3-*
  - split: depth_20_size_1000_seed_1
    path: database/depth_20_size_1000_seed_1-*
  - split: depth_20_size_1000_seed_2
    path: database/depth_20_size_1000_seed_2-*
  - split: depth_20_size_1000_seed_3
    path: database/depth_20_size_1000_seed_3-*
  - split: depth_20_size_2500_seed_1
    path: database/depth_20_size_2500_seed_1-*
  - split: depth_20_size_2500_seed_2
    path: database/depth_20_size_2500_seed_2-*
  - split: depth_20_size_2500_seed_3
    path: database/depth_20_size_2500_seed_3-*
  - split: depth_20_size_5000_seed_1
    path: database/depth_20_size_5000_seed_1-*
  - split: depth_20_size_5000_seed_2
    path: database/depth_20_size_5000_seed_2-*
  - split: depth_20_size_5000_seed_3
    path: database/depth_20_size_5000_seed_3-*
  - split: depth_20_size_10000_seed_1
    path: database/depth_20_size_10000_seed_1-*
  - split: depth_20_size_10000_seed_2
    path: database/depth_20_size_10000_seed_2-*
  - split: depth_20_size_10000_seed_3
    path: database/depth_20_size_10000_seed_3-*
- config_name: question-answer
  data_files:
  - split: depth_20_size_25_seed_1
    path: question-answer/depth_20_size_25_seed_1-*
  - split: depth_20_size_25_seed_2
    path: question-answer/depth_20_size_25_seed_2-*
  - split: depth_20_size_25_seed_3
    path: question-answer/depth_20_size_25_seed_3-*
  - split: depth_20_size_50_seed_1
    path: question-answer/depth_20_size_50_seed_1-*
  - split: depth_20_size_50_seed_2
    path: question-answer/depth_20_size_50_seed_2-*
  - split: depth_20_size_50_seed_3
    path: question-answer/depth_20_size_50_seed_3-*
  - split: depth_20_size_100_seed_1
    path: question-answer/depth_20_size_100_seed_1-*
  - split: depth_20_size_100_seed_2
    path: question-answer/depth_20_size_100_seed_2-*
  - split: depth_20_size_100_seed_3
    path: question-answer/depth_20_size_100_seed_3-*
  - split: depth_20_size_200_seed_1
    path: question-answer/depth_20_size_200_seed_1-*
  - split: depth_20_size_200_seed_2
    path: question-answer/depth_20_size_200_seed_2-*
  - split: depth_20_size_200_seed_3
    path: question-answer/depth_20_size_200_seed_3-*
  - split: depth_20_size_300_seed_1
    path: question-answer/depth_20_size_300_seed_1-*
  - split: depth_20_size_300_seed_2
    path: question-answer/depth_20_size_300_seed_2-*
  - split: depth_20_size_300_seed_3
    path: question-answer/depth_20_size_300_seed_3-*
  - split: depth_20_size_400_seed_1
    path: question-answer/depth_20_size_400_seed_1-*
  - split: depth_20_size_400_seed_2
    path: question-answer/depth_20_size_400_seed_2-*
  - split: depth_20_size_400_seed_3
    path: question-answer/depth_20_size_400_seed_3-*
  - split: depth_20_size_500_seed_1
    path: question-answer/depth_20_size_500_seed_1-*
  - split: depth_20_size_500_seed_2
    path: question-answer/depth_20_size_500_seed_2-*
  - split: depth_20_size_500_seed_3
    path: question-answer/depth_20_size_500_seed_3-*
  - split: depth_20_size_1000_seed_1
    path: question-answer/depth_20_size_1000_seed_1-*
  - split: depth_20_size_1000_seed_2
    path: question-answer/depth_20_size_1000_seed_2-*
  - split: depth_20_size_1000_seed_3
    path: question-answer/depth_20_size_1000_seed_3-*
  - split: depth_20_size_2500_seed_1
    path: question-answer/depth_20_size_2500_seed_1-*
  - split: depth_20_size_2500_seed_2
    path: question-answer/depth_20_size_2500_seed_2-*
  - split: depth_20_size_2500_seed_3
    path: question-answer/depth_20_size_2500_seed_3-*
  - split: depth_20_size_5000_seed_1
    path: question-answer/depth_20_size_5000_seed_1-*
  - split: depth_20_size_5000_seed_2
    path: question-answer/depth_20_size_5000_seed_2-*
  - split: depth_20_size_5000_seed_3
    path: question-answer/depth_20_size_5000_seed_3-*
  - split: depth_20_size_10000_seed_1
    path: question-answer/depth_20_size_10000_seed_1-*
  - split: depth_20_size_10000_seed_2
    path: question-answer/depth_20_size_10000_seed_2-*
  - split: depth_20_size_10000_seed_3
    path: question-answer/depth_20_size_10000_seed_3-*
- config_name: text-corpus
  data_files:
  - split: depth_20_size_25_seed_1
    path: text-corpus/depth_20_size_25_seed_1-*
  - split: depth_20_size_25_seed_2
    path: text-corpus/depth_20_size_25_seed_2-*
  - split: depth_20_size_25_seed_3
    path: text-corpus/depth_20_size_25_seed_3-*
  - split: depth_20_size_50_seed_1
    path: text-corpus/depth_20_size_50_seed_1-*
  - split: depth_20_size_50_seed_2
    path: text-corpus/depth_20_size_50_seed_2-*
  - split: depth_20_size_50_seed_3
    path: text-corpus/depth_20_size_50_seed_3-*
  - split: depth_20_size_100_seed_1
    path: text-corpus/depth_20_size_100_seed_1-*
  - split: depth_20_size_100_seed_2
    path: text-corpus/depth_20_size_100_seed_2-*
  - split: depth_20_size_100_seed_3
    path: text-corpus/depth_20_size_100_seed_3-*
  - split: depth_20_size_200_seed_1
    path: text-corpus/depth_20_size_200_seed_1-*
  - split: depth_20_size_200_seed_2
    path: text-corpus/depth_20_size_200_seed_2-*
  - split: depth_20_size_200_seed_3
    path: text-corpus/depth_20_size_200_seed_3-*
  - split: depth_20_size_300_seed_1
    path: text-corpus/depth_20_size_300_seed_1-*
  - split: depth_20_size_300_seed_2
    path: text-corpus/depth_20_size_300_seed_2-*
  - split: depth_20_size_300_seed_3
    path: text-corpus/depth_20_size_300_seed_3-*
  - split: depth_20_size_400_seed_1
    path: text-corpus/depth_20_size_400_seed_1-*
  - split: depth_20_size_400_seed_2
    path: text-corpus/depth_20_size_400_seed_2-*
  - split: depth_20_size_400_seed_3
    path: text-corpus/depth_20_size_400_seed_3-*
  - split: depth_20_size_500_seed_1
    path: text-corpus/depth_20_size_500_seed_1-*
  - split: depth_20_size_500_seed_2
    path: text-corpus/depth_20_size_500_seed_2-*
  - split: depth_20_size_500_seed_3
    path: text-corpus/depth_20_size_500_seed_3-*
  - split: depth_20_size_1000_seed_1
    path: text-corpus/depth_20_size_1000_seed_1-*
  - split: depth_20_size_1000_seed_2
    path: text-corpus/depth_20_size_1000_seed_2-*
  - split: depth_20_size_1000_seed_3
    path: text-corpus/depth_20_size_1000_seed_3-*
  - split: depth_20_size_2500_seed_1
    path: text-corpus/depth_20_size_2500_seed_1-*
  - split: depth_20_size_2500_seed_2
    path: text-corpus/depth_20_size_2500_seed_2-*
  - split: depth_20_size_2500_seed_3
    path: text-corpus/depth_20_size_2500_seed_3-*
  - split: depth_20_size_5000_seed_1
    path: text-corpus/depth_20_size_5000_seed_1-*
  - split: depth_20_size_5000_seed_2
    path: text-corpus/depth_20_size_5000_seed_2-*
  - split: depth_20_size_5000_seed_3
    path: text-corpus/depth_20_size_5000_seed_3-*
  - split: depth_20_size_10000_seed_1
    path: text-corpus/depth_20_size_10000_seed_1-*
  - split: depth_20_size_10000_seed_2
    path: text-corpus/depth_20_size_10000_seed_2-*
  - split: depth_20_size_10000_seed_3
    path: text-corpus/depth_20_size_10000_seed_3-*
---

# Dataset Card for PhantomWiki

**This repository is a collection of PhantomWiki instances generated using the `phantom-wiki` Python package.** 

PhantomWiki is a framework for generating unique, factually consistent document corpora with diverse question-answer pairs. 
Unlike prior work, PhantomWiki is neither a fixed dataset, nor is it based on any existing data. 
Instead, a new PhantomWiki instance is generated on demand for each evaluation.

## Dataset Details

### Dataset Description

PhantomWiki generates a fictional universe of characters along with a set of facts. 
We reflect these facts in a large-scale corpus, mimicking the style of fan-wiki websites. 
Then we generate question-answer pairs with tunable difficulties, encapsulating the types of multi-hop questions commonly considered in the question-answering (QA) literature.


- **Curated by:** Albert Gong, Kamilė Stankevičiūtė, Chao Wan, Anmol Kabra, Raphael Thesmar, Johann Lee, Julius Klenke, Carla P. Gomes, Kilian Q. Weinberger
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** Apache License 2.0

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/albertgong1/phantom-wiki
- **Paper [optional]:** TODO
- **Demo [optional]:** [More Information Needed]

## Uses

PhantomWiki is intended to evaluate retrieval augmented generation (RAG) systems and agentic workflows.

### Direct Use

Owing to its fully synthetic and controllable nature, PhantomWiki is particularly useful for disentangling the reasoning and retrieval capabilities of large language models.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

PhantomWiki exposes three components, reflected in the three **configurations**:
1. `question-answer`: Question-answer pairs generated using a context-free grammar
2. `text-corpus`: Documents generated using natural-language templates
3. `database`: Prolog database containing the facts and clauses representing the universe

Each universe is saved as a **split**.

## Dataset Creation

### Curation Rationale

Most mathematical and logical reasoning datasets do not explicity evaluate retrieval capabilities and 
few retrieval datasets incorporate complex reasoning, save for a few exceptions (e.g., [BRIGHT](https://huggingface.co/datasets/xlangai/BRIGHT), [MultiHop-RAG](https://huggingface.co/datasets/yixuantt/MultiHopRAG)). 
However, virtually all retrieval datasets are derived from Wikipedia or internet articles, which are contained in LLM training data.
We take the first steps toward a large-scale synthetic dataset that can evaluate LLMs' reasoning and retrieval capabilities.

### Source Data

This is a synthetic dataset.

#### Data Collection and Processing

This dataset was generated on commodity CPUs using Python and Prolog. See paper for full details of the generation pipeline, including timings. 

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

N/A

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

N/A

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

N/A

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

N/A

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

N/A

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

N/A

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Dataset Card Authors [optional]

Albert Gong

## Dataset Card Contact

[email protected]