ArtifactAI/arxiv-beir-cs-ml-generated-queries

### Dataset Summary

A BEIR style dataset derived from [ArXiv](https://arxiv.org/). The dataset consists of corpus/query pairs derived from ArXiv abstracts from the following categories: "cs.CL", "cs.AI", "cs.CV", "cs.HC", "cs.IR", "cs.RO", "cs.NE", "stat.ML"

### Languages

All tasks are in English (`en`).

## Dataset Structure

The dataset contains a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`

### Data Instances

A high level example of any beir dataset:

```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}

queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}

qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```

### Data Fields

Examples from all configurations have the following features:

### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.

### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.

### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

Cite as:
```
@misc{arxiv-beir-cs-ml-generated-queries,
title={arxiv-beir-cs-ml-generated-queries},
author={Matthew Kenney},
year={2023}
}
```

原始信息汇总

数据集概述

该数据集是一个BEIR风格的集合，源自ArXiv，包含以下类别的ArXiv摘要的语料库/查询对："cs.CL", "cs.AI", "cs.CV", "cs.HC", "cs.IR", "cs.RO", "cs.NE", "stat.ML"。所有任务语言为英语（en）。

数据集结构

数据集包含三个主要部分：

• 语料库文件：.jsonl格式，包含一系列字典，每个字典包含三个字段：_id（唯一文档标识符），title（文档标题，可选），text（文档段落或文本）。
• 查询文件：.jsonl格式，包含一系列字典，每个字典包含两个字段：_id（唯一查询标识符），text（查询文本）。
• qrels文件：.tsv格式，包含三个列：query-id，corpus-id，score，表示查询与文档的相关性评分。

数据实例

数据集的示例包括：

• 语料库：字典形式，包含文档的_id、title和text。
• 查询：字典形式，包含查询的_id和text。
• qrels：字典形式，包含查询_id、文档_id和相关性score。

数据字段

• 语料库：包含_id（字符串，唯一文档标识符），title（字符串，文档标题），text（字符串，文档文本）。
• 查询：包含_id（字符串，唯一查询标识符），text（字符串，查询文本）。
• qrels：包含_id（字符串，查询标识符），_id（字符串，文档标识符），score（整数，相关性评分）。

引用信息

引用格式为：

@misc{arxiv-beir-cs-ml-generated-queries, title={arxiv-beir-cs-ml-generated-queries}, author={Matthew Kenney}, year={2023} }