Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
100K - 1M
License:
File size: 2,268 Bytes
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import os
import random
import json
random.seed(20)
def _get_filepaths(path):
filepaths = []
for f in os.listdir(path):
f_path = os.path.join(path, f)
if os.path.isfile(f_path):
filepaths.append(os.path.normpath(f_path))
elif os.path.isdir(f_path):
filepaths += _get_filepaths(f_path)
return filepaths
def get_split(path, train_split: float, must_be_in_train):
filepaths = _get_filepaths(path)
random.shuffle(filepaths)
boundary = int(train_split * len(filepaths))
train_paths = filepaths[:boundary]
valid_paths = filepaths[boundary:]
print("TRAIN SPLIT (number in train, number in val): ", len(train_paths), len(valid_paths))
for path in must_be_in_train:
normed_path = os.path.normpath(path)
assert normed_path in train_paths or normed_path in valid_paths, f"{normed_path} not in paths"
if normed_path in valid_paths:
print(f"MOVING {path} to validation set")
valid_paths.remove(path)
train_paths.append(path)
return train_paths, valid_paths
def arxiv_split():
train_paths = []
val_paths = []
for f in os.listdir("arxiv"):
if f[-3:] == ".gz":
f_path = os.path.join("./arxiv", f)
# validation set is june of years divisible by 4
if int(f[1])%4==0 and int(f[3])==6:
val_paths.append(f_path)
else:
train_paths.append(f_path)
return train_paths, val_paths
def main():
train_rate = 0.95
splits = {}
args = [
("books", ["books/stein/stein.tex", "books/trench/TRENCH_REAL_ANALYSIS.tex"]),
("formal", ["formal/setmm/set.mm"]),
]
for subdir, must_be_in_train in args:
print(subdir, must_be_in_train)
train, valid = get_split(subdir, train_rate, must_be_in_train)
splits[subdir + "-train"] = train
splits[subdir + "-valid"] = valid
train, valid = arxiv_split()
splits["arxiv-train"] = train
splits["arxiv-valid"] = valid
print("arxiv", len(train), len(valid))
with open("splits.json", "w") as f:
f.write(json.dumps(splits, indent=4))
if __name__=="__main__":
main()
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