task_categories:text-generation task_ids:language-modeling task_ids:masked-language-modeling annotations_creators:no-annotation multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:en license:cc0-1.0 doi:10.57967/hf/0799 region:us
---
annotations_creators:
- no-annotation
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: arxiv-math-instruct-50k
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: arxiv-math-instruct-50k
---

# Dataset Card for "arxiv-math-instruct-50k"

### Dataset Summary

The "ArtifactAI/arxiv-math-instruct-50k" dataset consists of question-answer 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". 

Questions are generated using the [t5-base model](https://huggingface.co/t5-base), while the answers are generated using the [GPT-3.5-turbo model](https://openai.com/chatgpt).


### Languages

English

## Dataset Structure

### Data Instances

#### train

- **Size of downloaded dataset files:** 38.4 MB

An example of 'train' looks as follows.

{
    "question": "Which math term describes the behaviour of an elliptic curve?", 
    "answer": "The term that describes the behavior of an elliptic curve is its "rank". 
               The rank of an elliptic curve is a measure of the number of rational points on the curve. 
               It is an important concept in number theory and cryptography, as the security of certain cryptographic algorithms based 
               on elliptic curves depends on the rank of the curve."
}


### Data Fields

The data fields present in the dataset are as follows:

- question: a string feature representing the question.
- answer: a string feature representing the answer.


### Data Splits

 train:  50,488 question answer pairs


### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

Question-answer pairs derived from [ArXiv](https://arxiv.org/) abstracts.


#### Initial Data Collection and Normalization

The "ArtifactAI/arxiv-math-instruct-50k" dataset consists of question-answer pairs derived from ArXiv abstracts. Questions are generated from ArXiv papers in 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"

Questions are generated using the [t5-base model](https://huggingface.co/t5-base), while the answers are generated using the [GPT-3.5-turbo model](https://openai.com/chatgpt).


### Annotations

The dataset doesn't contain annotations.

### Personal and Sensitive Information

None


#### Notice policy

Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:

Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.

Clearly identify the copyrighted work claimed to be infringed.

Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.

And contact us at the following email address: matt at artifactai.com and datasets at huggingface.co

#### Take down policy

The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus.
Hugging Face will also update this repository accordingly.

### Citation Information

```
@misc{arxiv-math-instruct-50k,
	title={arxiv-math-instruct-50},
	author={Matthew Kenney},
	year={2023}
}
```
