Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
100K - 1M
License:
File size: 2,017 Bytes
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import os
import sys
import json
import ndjson
from pathlib import Path
from fetch_mathoverflow import batch_loader
import random
from utils import make_archive
ARCHIVE_URL = "https://people.eecs.berkeley.edu/~hendrycks/MATH.tar"
SAVE_PATH = "math-dataset"
random.seed(20)
def main():
VAL_RATE=5e-2
Path(SAVE_PATH).mkdir(exist_ok=True)
archive_path = os.path.join(SAVE_PATH, "archive.tar")
os.system("wget -O " + archive_path + " " + ARCHIVE_URL)
os.system("tar -xf " + archive_path + " -C " + SAVE_PATH)
cat_dir = os.path.join(SAVE_PATH, "MATH/train")
for cat_name in os.listdir(cat_dir):
cat_path = os.path.join(cat_dir, cat_name)
if os.path.isdir(cat_path):
cat_texts = []
for f in os.listdir(cat_path):
f_path = os.path.join(cat_path, f)
with open(f_path) as fle:
prob_json = json.load(fle)
text = "{\\bf Problem.} " + prob_json["problem"] + "\n" +\
"{\\bf Level.} " + prob_json["level"] + "\n" +\
"{\\bf Type.} " + prob_json["type"] + "\n" +\
"{\\bf Solution.} " + prob_json["solution"]
cat_texts.append(text)
random.shuffle(cat_texts)
instances = [{"text": x.strip(), "meta": {"set_name": "MATH"}} for x in cat_texts]
split = int(VAL_RATE*len(instances))
train = instances[split:]
val = instances[:split]
with open(os.path.join(SAVE_PATH, "train.jsonl"), "a+") as f:
f.write(ndjson.dumps(train))
f.write("\n")
with open(os.path.join(SAVE_PATH, "val.jsonl"), "a+") as f:
f.write(ndjson.dumps(val))
f.write("\n")
os.system("gzip " + os.path.join(SAVE_PATH, "train.jsonl"))
os.system("gzip " + os.path.join(SAVE_PATH, "val.jsonl"))
os.system("rm -r " + os.path.join(SAVE_PATH, "MATH"))
os.remove(archive_path)
if __name__=="__main__":
main()
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