readme

README.md

@@ -7,4 +7,95 @@ tags:
 - math
 size_categories:
 - 10B<n<100B
----
+---
+<img src="proofpile_logo.jpg" width="500">
+
+[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/)
+
+[Github ](https://github.com/EleutherAI/math-lm) | [ArXiv](#)
+
+The **Proof-Pile-2** is a 55 billion token dataset of mathematical and scientific documents. 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
+```
+from datasets import load_dataset
+ds = load_dataset("EleuetherAI/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
+```
+{
+  "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
+**v1.0.0**: 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
+```
+@article{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},
+    eprint={xyz.xyz},
+    archivePrefix={arXiv}
+    year={2023}
+}
+```
+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}
+}
+```