updated readme

README.md

@@ -56,7 +56,7 @@ This dataset contains only the training set of the [MATH dataset](https://github
 # Data Preprocessing
 This section describes any significant filtering and transformations made to various subsets of the data. 
 
-### arXiv.math
+**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.
@@ -67,7 +67,9 @@ when choosing which files from arXiv.math source folders to include in the datas
 - 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. 
@@ -75,7 +77,7 @@ In addition, we apply the following transformations to arXiv.math texts:
 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
+**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}
@@ -89,7 +91,7 @@ REPLY [{num_upvotes} votes]: {text of reply}
 .
 ```
 
-### set.mm
+**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

utils.py

@@ -1,53 +0,0 @@
-import os
-import tarfile 
-from itertools import cycle
-from shutil import get_terminal_size
-from threading import Thread
-from time import sleep
-
-def make_archive(path): 
-    with tarfile.open(path + ".tar.gz", "w:gz") as tar: 
-        tar.add(path, arcname=os.path.sep)
-    os.system(f"rm -r {path}")
-
-class Loader:
-    def __init__(self, desc="Loading...", end="Done!", timeout=0.1):
-        """
-        A loader-like context manager
-
-        Args:
-            desc (str, optional): The loader's description. Defaults to "Loading...".
-            end (str, optional): Final print. Defaults to "Done!".
-            timeout (float, optional): Sleep time between prints. Defaults to 0.1.
-        """
-        self.desc = desc
-        self.end = end
-        self.timeout = timeout
-
-        self._thread = Thread(target=self._animate, daemon=True)
-        self.steps = ["⢿", "⣻", "⣽", "⣾", "⣷", "⣯", "⣟", "⡿"]
-        self.done = False
-
-    def start(self):
-        self._thread.start()
-        return self
-
-    def _animate(self):
-        for c in cycle(self.steps):
-            if self.done:
-                break
-            print(f"\r{self.desc} {c}", flush=True, end="")
-            sleep(self.timeout)
-
-    def __enter__(self):
-        self.start()
-
-    def stop(self):
-        self.done = True
-        cols = get_terminal_size((80, 20)).columns
-        print("\r" + " " * cols, end="", flush=True)
-        print(f"\r{self.end}", flush=True)
-
-    def __exit__(self, exc_type, exc_value, tb):
-        # handle exceptions with those variables ^
-        self.stop()