abhinavk/openpi_v2

---
annotations_creators:
- expert-generated
language:
- en
language_creators: []
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: openpi_v2
size_categories:
- 10K<n<100K
source_datasets: []
tags: []
task_categories:
- question-answering
- text-classification
task_ids:
- entity-linking-classification
- natural-language-inference
---

# Dataset Card for openpi_v2

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

Open PI is the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. Our solution is a new task formulation in which just the text is provided, from which a set of state changes (entity, attribute, before, after) is generated for each step, where the entity, attribute, and values must all be predicted from an open vocabulary.

### Supported Tasks and Leaderboards

- `Task 1`: Given paragraph (e.g., with 5 steps), identify entities that change (challenge: implicit entities, some explicit entities that don’t change)

- `Task 3`: Given paragraph, identify the attributes of entity that change (challenge: implicit entities, attributes & many combinations)

- `Task 4`: Given paragraph & an entity, identify the sequence of attribute value changes (challenge: implicit attributes)

- `Task 7`: Given image url, identify the visual attributes of entity and non-visual attributes of entity that change

### Languages

English

## Dataset Structure

### Data Instances

A typical instance in the dataset:

```
{
"goal": "goal1_text",
"steps": [
"step1_text",
"step2_text",
...
],
"topics": "topic1_annotation",
"image_urls": [
"step1_url_text",
"step2_url_text",
...
],
"states": [
{
"answers_openpiv1_metadata": {
"entity": "entity1 | entity2 | ...",
"attribute": "attribute1 | attribute2 | ...",
"answers": [
"before: step1_entity1_before | step1_entity2_before, after: step1_entity1_after | step1_entity2_after",
...
],
"modality": [
"step1_entity1_modality_id | step1_entity2_modality_id",
...
]
},
"entity": "entity1 | entity2 | ...",
"attribute": "attribute1 | attribute2 | ...",
"answers": [
"before: step1_entity1_before_merged | step1_entity2_before_merged, after: step1_entity1_after_merged | step1_entity2_after_merged",
...
]
}
]
}
```

### Data Fields

The following is an excerpt from the dataset README:

Within "goal", "steps", "topics", and "image_urls", the fields should be self-explanatory. Listed below is an explanation about those within "states":

#### Fields specific to questions:

### Data Splits

Train, Valid, Dev

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

原始信息汇总

数据集概述

• 名称: openpi_v2
• 语言: 英语
• 许可证: cc-by-4.0
• 规模: 10K<n<100K
• 多语言性: 单语种
• 注释创建者: 专家生成
• 任务类别:
  • 问答
  • 文本分类
• 任务ID:
  • entity-linking-classification
  • natural-language-inference

数据集描述

数据集摘要

Open PI是首个用于追踪程序文本中状态变化的开放词汇数据集。该数据集通过提供文本，从中生成一系列状态变化（实体、属性、之前、之后），其中实体、属性和值均需从开放词汇中预测。

支持的任务和排行榜

• 任务1: 给定段落（例如，包含5个步骤），识别变化的实体（挑战：隐含实体，一些明确但不变化的实体）
• 任务3: 给定段落，识别实体属性的变化（挑战：隐含实体、属性和多种组合）
• 任务4: 给定段落和实体，识别属性值变化的序列（挑战：隐含属性）
• 任务7: 给定图像URL，识别实体的视觉属性和非视觉属性的变化

数据集结构

数据实例

一个典型的数据实例包括：

• "goal": 目标文本
• "steps": 步骤文本列表
• "topics": 主题注释
• "image_urls": 图像URL列表
• "states": 状态变化详细信息，包括实体、属性和答案变化

数据字段

• 目标、步骤、主题和图像URL：自解释性字段
• 状态字段：详细描述实体、属性和答案的变化，包括之前和之后的值

数据分割

• 训练集、验证集、开发集

数据集创建

来源数据

• 初始数据收集和标准化：信息待补充
• 源语言生产者：信息待补充

注释

• 注释过程：信息待补充
• 注释者：信息待补充

个人和敏感信息

• 信息待补充