Datasets:

EleutherAI
/

proof-pile-2

	---
	task_categories:
	- text-generation
	language:
	- en
	tags:
	- math
	size_categories:
	- 10B<n<100B
	---
	<img src="proofpile_logo.jpg" width="500">

	[ArXiv](http://arxiv.org/abs/2310.10631) \| [Models](https://huggingface.co/EleutherAI/llemma_34b) \| [Data](https://huggingface.co/datasets/EleutherAI/proof-pile-2) \| [Code](https://github.com/EleutherAI/math-lm) \| [Blog](https://blog.eleuther.ai/llemma/) \| [Sample Explorer](https://llemma-demo.github.io/)

	[Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Hailey Schoelkopf](https://github.com/haileyschoelkopf), [Keiran Paster](https://keirp.com), [Marco Dos Santos](https://github.com/dsantosmarco), [Stephen McAleer](https://www.andrew.cmu.edu/user/smcaleer/), [Albert Q. Jiang](https://albertqjiang.github.io/), [Jia Deng](https://www.cs.princeton.edu/~jiadeng/), [Stella Biderman](https://www.stellabiderman.com/), [Sean Welleck](https://wellecks.com/)

	The Proof-Pile-2 is a 55 billion token dataset of mathematical and scientific documents. This dataset was created in order to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b) models. It consists of three subsets:
	- `arxiv` (29B tokens): the ArXiv subset of [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
	- `open-web-math` (15B tokens): The [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) dataset, which contains much of the high-quality mathematical text from the internet.
	- `algebraic-stack` (11B tokens): A new dataset of mathematical code, including numerical computing, computer algebra, and formal mathematics.

	You can download the dataset as follows
	```python
	from datasets import load_dataset
	ds = load_dataset("EleutherAI/proof-pile-2")

	# To load only a specific subset, pass it as an argument, e.g
	ds_arxiv = load_dataset("EleutherAI/proof-pile-2", "arxiv")
	```

	### Schema
	Each dataset row has the following structure
	```python
	{
	"text": ..., # document text
	"meta": ..., # JSON string of metadata, schema specific to data source
	}
	```

	### Dataset Contents
	For detailed documentation of the ArXiv and web subsets, refer to [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math). The following table enumerates the contents of the AlgebraicStack by programming language. The AlgebraicStack is filtered to only include documents that contain mathematics, as judged by hand-crafted, language-specific heuristics.

	\| Language \| AlgebraicStack tokens \|
	\|-----------\|-----------------------\|
	\| Agda \| 35.2 M \|
	\| C \| 25.1 M \|
	\| C++ \| 954.1 M \|
	\| Coq \| 281.9 M \|
	\| Fortran \| 724.9 M \|
	\| GAP \| 3.6 M \|
	\| Haskell \| 9.1 M \|
	\| Idris \| 10.9 M \|
	\| Isabelle \| 1,089.7 M \|
	\| Julia \| 531.0 M \|
	\| Jupyter \| 199.1 M \|
	\| Lean \| 285.6 M \|
	\| Maple \| 2.0 M \|
	\| Matlab \| 65.8 M \|
	\| Python \| 6,098.8 M \|
	\| R \| 71.3 M \|
	\| Tex \| 567.7 M \|
	\| Total \| 10,955.7 M \|

	### License
	We do not alter the license of any of the underlying data.

	### Version History
	v1.1.0: Contains an updated version of OpenWebMath, precisely the one available at [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math). This version of OpenWebMath has slightly improved filtering, for example, removal of very short documents.

	v1.0.0: The data used to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b). Uses a development version of OpenWebMath.

	### Citation
	For the entire Proof-Pile-2, cite
	```
	@misc{azerbayev2023llemma,
	title={Llemma: An Open Language Model For Mathematics},
	author={Zhangir Azerbayev and Hailey Schoelkopf and Keiran Paster and Marco Dos Santos and Stephen McAleer and Albert Q. Jiang and Jia Deng and Stella Biderman and Sean Welleck},
	year={2023},
	eprint={2310.10631},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```
	For the ArXiv subset, cite
	```
	@software{together2023redpajama,
	author = {Together Computer},
	title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
	month = April,
	year = 2023,
	url = {https://github.com/togethercomputer/RedPajama-Data}
	}
	```
	For OpenWebMath, cite
	```
	@misc{paster2023openwebmath,
	title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
	author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
	year={2023},
	eprint={2310.06786},
	archivePrefix={arXiv},
	primaryClass={cs.AI}
	}
	```