### 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: "math.AC", "math.AG", "math.AP", "math.AT", "math.CA", "math.CO", "math.CT", "math.CV", "math.DG", "math.DS", "math.FA", "math.GM", "math.GN", "math.GR", "math.GT", "math.HO", "math.IT", "math.KT", "math.LO", "math.MG", "math.MP", "math.NA", "math.NT", "math.OA", "math.OC", "math.PR", "math.QA", "math.RA", "math.RT", "math.SG", "math.SP", "math.ST", "math-ph".

### 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 massâ€“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 WeiÃŸbier 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-math-generated-queries,
    title={arxiv-beir-math-generated-queries},
    author={Matthew Kenney},
    year={2023}
}
```
doi:10.57967/hf/0809 region:us
{}
TAGS
#doi-10.57967/hf/0809 #region-us 

### Dataset Summary

A BEIR style dataset derived from ArXiv. The dataset consists of corpus/query pairs derived from ArXiv abstracts from the following categories: "math.AC", "math.AG", "math.AP", "math.AT", "math.CA", "math.CO", "math.CT", "math.CV", "math.DG", "math.DS", "math.FA", "math.GM", "math.GN", "math.GR", "math.GT", "math.HO", "math.IT", "math.KT", "math.LO", "math.MG", "math.MP", "math.NA", "math.NT", "math.OA", "math.OC", "math.PR", "math.QA", "math.RA", "math.RT", "math.SG", "math.SP", "math.ST", "math-ph".

### 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:



### 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



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information





Cite as:

### Dataset Summary

A BEIR style dataset derived from ArXiv. The dataset consists of corpus/query pairs derived from ArXiv abstracts from the following categories: "math.AC", "math.AG", "math.AP", "math.AT", "math.CA", "math.CO", "math.CT", "math.CV", "math.DG", "math.DS", "math.FA", "math.GM", "math.GN", "math.GR", "math.GT", "math.HO", "math.IT", "math.KT", "math.LO", "math.MG", "math.MP", "math.NA", "math.NT", "math.OA", "math.OC", "math.PR", "math.QA", "math.RA", "math.RT", "math.SG", "math.SP", "math.ST", "math-ph". ### 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: ### 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information





Cite as:
TAGS
#doi-10.57967/hf/0809 #region-us 
 ### Dataset Summary

A BEIR style dataset derived from ArXiv. The dataset consists of corpus/query pairs derived from ArXiv abstracts from the following categories: "math.AC", "math.AG", "math.AP", "math.AT", "math.CA", "math.CO", "math.CT", "math.CV", "math.DG", "math.DS", "math.FA", "math.GM", "math.GN", "math.GR", "math.GT", "math.HO", "math.IT", "math.KT", "math.LO", "math.MG", "math.MP", "math.NA", "math.NT", "math.OA", "math.OC", "math.PR", "math.QA", "math.RA", "math.RT", "math.SG", "math.SP", "math.ST", "math-ph". ### 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: ### 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information





Cite as:
passage: TAGS
#doi-10.57967/hf/0809 #region-us 
### Dataset Summary

A BEIR style dataset derived from ArXiv. The dataset consists of corpus/query pairs derived from ArXiv abstracts from the following categories: "math.AC", "math.AG", "math.AP", "math.AT", "math.CA", "math.CO", "math.CT", "math.CV", "math.DG", "math.DS", "math.FA", "math.GM", "math.GN", "math.GR", "math.GT", "math.HO", "math.IT", "math.KT", "math.LO", "math.MG", "math.MP", "math.NA", "math.NT", "math.OA", "math.OC", "math.PR", "math.QA", "math.RA", "math.RT", "math.SG", "math.SP", "math.ST", "math-ph".### Languages

All tasks are in English ('en').