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6b0aeb8
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Parent(s):
Duplicate from EleutherAI/proof-pile-2
Browse filesCo-authored-by: Zhangir Azerbayev <[email protected]>
This view is limited to 50 files because it contains too many changes.
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- algebraic-stack/train/julia0004.jsonl.zst +3 -0
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README.md
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---
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task_categories:
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- text-generation
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language:
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- en
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tags:
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- math
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size_categories:
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- 10B<n<100B
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---
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<img src="proofpile_logo.jpg" width="500">
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[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/)
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[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/)
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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:
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- `arxiv` (29B tokens): the ArXiv subset of [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
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- `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.
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- `algebraic-stack` (11B tokens): A new dataset of mathematical code, including numerical computing, computer algebra, and formal mathematics.
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You can download the dataset as follows
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```python
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from datasets import load_dataset
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ds = load_dataset("EleutherAI/proof-pile-2")
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# To load only a specific subset, pass it as an argument, e.g
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ds_arxiv = load_dataset("EleutherAI/proof-pile-2", "arxiv")
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```
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### Schema
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Each dataset row has the following structure
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```python
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{
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"text": ..., # document text
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"meta": ..., # JSON string of metadata, schema specific to data source
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}
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```
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### Dataset Contents
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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.
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| Language | AlgebraicStack tokens |
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|-----------|-----------------------|
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| Agda | 35.2 M |
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| C | 25.1 M |
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| C++ | 954.1 M |
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| Coq | 281.9 M |
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| Fortran | 724.9 M |
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| GAP | 3.6 M |
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| Haskell | 9.1 M |
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| Idris | 10.9 M |
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| Isabelle | 1,089.7 M |
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| Julia | 531.0 M |
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| Jupyter | 199.1 M |
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| Lean | 285.6 M |
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| Maple | 2.0 M |
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| Matlab | 65.8 M |
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| Python | 6,098.8 M |
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| R | 71.3 M |
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| Tex | 567.7 M |
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| **Total** | **10,955.7 M** |
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### License
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We do not alter the license of any of the underlying data.
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### Version History
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**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.
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**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.
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### Citation
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For the entire Proof-Pile-2, cite
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```
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@misc{azerbayev2023llemma,
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title={Llemma: An Open Language Model For Mathematics},
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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},
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year={2023},
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eprint={2310.10631},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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For the ArXiv subset, cite
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```
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@software{together2023redpajama,
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author = {Together Computer},
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title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
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month = April,
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year = 2023,
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url = {https://github.com/togethercomputer/RedPajama-Data}
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}
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```
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For OpenWebMath, cite
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```
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@misc{paster2023openwebmath,
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title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
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author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
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year={2023},
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eprint={2310.06786},
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archivePrefix={arXiv},
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primaryClass={cs.AI}
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}
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```
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