Moved build scripts to github

.gitattributes

@@ -50,3 +50,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 formal/setmm/set.mm filter=lfs diff=lfs merge=lfs -text
+train filter=lfs diff=lfs merge=lfs -text
+test filter=lfs diff=lfs merge=lfs -text
+dev filter=lfs diff=lfs merge=lfs -text

README.md

@@ -22,8 +22,8 @@ task_ids:
 ---
 
 # Dataset Description
-The `proof-pile` is a 40GB pre-training dataset of mathematical text that comprises roughly 15 billion tokens. The dataset is composed of diverse sources of both informal and formal mathematics, namely
-- ArXiv.math (37GB)
+The `proof-pile` is a 36GB pre-training dataset of mathematical text that comprises roughly 15 billion tokens. The dataset is composed of diverse sources of both informal and formal mathematics, namely
+- ArXiv.math (35GB)
 - Open-source math textbooks (50MB)
 - Formal mathematics libraries (500MB)
     - Lean mathlib and other Lean repositories 
@@ -38,22 +38,60 @@ The `proof-pile` is a 40GB pre-training dataset of mathematical text that compri
   - Wikipedia math articles
 - MATH dataset (6MB)
 
+The construction of the dataset is reproducible using the code and instructions in the [proof-pile Github
+repo](https://github.com/zhangir-azerbayev/proof-pile). 
+
 # Supported Tasks
-This dataset is intended to be used for pre-training language models. We envision models pre-trained on the `proof-pile` will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization. 
+This dataset is intended to be used for pre-training and fine-tuning language models. We envision models trained on the `proof-pile` will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization. 
 
 # Languages
 All informal mathematics in the `proof-pile` is written in English and LaTeX (arXiv articles in other languages are filtered out using [languagedetect](https://github.com/shuyo/language-detection/blob/wiki/ProjectHome.md)). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar.
-
- # Configurations
- The data is sorted into `"arxiv", "books", "formal", "stack-exchange", "wiki",` and `"math-dataset"` configurations. This is so that it is easy to upsample particular configurations during pre-training with the `datasets.interleave_datasets()` function. 
  
 # Evaluation
 The version of `set.mm` in this dataset has 10% of proofs replaced with the `?` character in order to preserve a validation and test set for Metamath provers pre-trained on the `proof-pile`. The precise split can be found here: [validation](https://github.com/zhangir-azerbayev/mm-extract/blob/main/valid_decls.json) and [test](https://github.com/zhangir-azerbayev/mm-extract/blob/main/test_decls.json). 
-
 The Lean mathlib commit used in this dataset is `6313863`. Theorems created in subsequent commits can be used for evaluating Lean theorem provers. 
 
 This dataset contains only the training set of the [MATH dataset](https://github.com/hendrycks/math). However, because this dataset contains ProofWiki, the Stacks Project, Trench's Analysis, and Stein's Number Theory, models trained on it cannot be evaluated on the [NaturalProofs dataset](https://github.com/wellecks/naturalproofs). 
 
+# Data Preprocessing
+This section describes any significant filtering and transformations made to various subsets of the data. 
+
+### arXiv.math
+The arXiv.math dataset is large, heterogeneous, and contains a great deal of noise. We used the following heuristics
+when choosing which files from arXiv.math source folders to include in the dataset:
+- Keep only files with a `.tex` extension.
+- Only include files that use either a `utf-8/16/32` or `latin-1` text encoding. 
+- Discard files that do not contain a part, chapter, section, sub...section, paragraph, or subparagraph heading. 
+- Delete files that contain the keyword `gnuplot`. Gnuplot-latex is an old command line utility that generates blocks
+  of entirely unintelligible source. 
+- Include only articles in English, as determined by the [langdetect library](https://pypi.org/project/langdetect/). \n",
+    "\n",
+- Exclude files shorter than 280 characters (characters counted after substring removal described below).
+In addition, we apply the following transformations to arXiv.math texts: 
+- Delete everything outside of `\begin{document}` and `\end{document}`. 
+- Delete everything including or after `\Refs`, `\begin{thebibliography}`, or `\begin{bibdiv}`
+- Delete comments. 
+- Any more than three consecutive newlines are replaced by three consecutive newlines. 
+In [this notebook](https://github.com/zhangir-azerbayev/proof-pile/blob/main/analysis/arxiv_noisedetection.ipynb), we provide an analysis of the prevalence of noisy documents in the arXiv.math subset of the
+proof-pile. 
+
+### Stack Exchange
+We only include questions that have at least 5 upvotes and an answer. We format Stack Exchange posts as follows
+```
+QUESTION [{num_upvotes} upvotes]: {text of question}
+
+REPLY [{num_upvotes} votes]: {text of reply}
+
+REPLY [{num_upvotes} votes]: {text of reply}
+
+.
+.
+.
+```
+
+### set.mm
+We converted `set.mm` into human-readable form by following the instructions in the [mm-extract repo](https://github.com/zhangir-azerbayev/mm-extract)
+
 ## Contributions
 Authors: Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski. 
 

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