Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
code
Multilinguality:
multilingual
Size Categories:
unknown
ArXiv:
Tags:
License:
File size: 4,069 Bytes
517d89b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
from tqdm import tqdm
from datasets import Dataset
"""to run inside XLCOST_DATA folder after downloading XLCost data from this repo https://github.com/reddy-lab-code-research/XLCoST"""
class Example(object):
"""A single training/test example."""
def __init__(self,
idx,
source,
target,
):
self.idx = idx
self.source = source
self.target = target
def read_examples(filename):
"""Read examples from filename."""
examples=[]
assert len(filename.split(','))==2
src_filename = filename.split(',')[0]
trg_filename = filename.split(',')[1]
idx = 0
with open(src_filename) as f1,open(trg_filename) as f2:
for line1,line2 in zip(f1,f2):
examples.append(
Example(
idx = idx,
source=line1.strip(),
target=line2.strip(),
)
)
idx+=1
return examples
def create_data(filename):
examples = read_examples(filename)
text = []
code = []
print(len(examples))
for i in tqdm(range(len(examples))):
text.append(examples[i].source)
code.append(examples[i].target)
data = {"text": text, "code": code}
data = Dataset.from_dict(data)
return data
if __name__ == "__main__":
#clone xlcost-text-to-code hub repo
LANG = ["Python", "C", "C#", "Java", "PHP", "Javascript", "C++"]
EXTENSION = ["py", "c", "cs", "java", "php", "js", "cpp"]
for i in range(len(LANG)):
# for each language this saves train test and validation subsets for both snippet and program levels
lang = LANG[i]
ext = EXTENSION[i]
print(f"language: {lang}")
if lang == "C#":
path_snippet = f"Csharp-snippet-level"
path_program = f"Csharp-program-level"
else:
path_snippet = f"{lang}-snippet-level"
path_program = f"{lang}-program-level"
train_filename = f"generation/pair_data_tok_1_comment/{lang}-comment/train-{lang}-comment-tok.txt,generation/pair_data_tok_1_comment/{lang}-comment/train-{lang}-comment-tok.{ext}"
valid_filename = f"generation/pair_data_tok_1_comment/{lang}-comment/val-{lang}-comment-tok.txt,generation/pair_data_tok_1_comment/{lang}-comment/val-{lang}-comment-tok.{ext}"
test_filename = f"generation/pair_data_tok_1_comment/{lang}-comment/test-{lang}-comment-tok.txt,generation/pair_data_tok_1_comment/{lang}-comment/test-{lang}-comment-tok.{ext}"
train = create_data(train_filename)
valid = create_data(valid_filename)
test = create_data(test_filename)
train.to_json(f"xlcost-text-to-code/data/{path_snippet}/train.json", lines=True)
valid.to_json(f"xlcost-text-to-code/data/{path_snippet}/valid.json", lines=True)
test.to_json(f"xlcost-text-to-code/data/{path_snippet}/test.json", lines=True)
train_filename = f"generation/pair_data_tok_full_desc_comment/{lang}-desc/train-{lang}-desc-tok.txt,generation/pair_data_tok_full_desc_comment/{lang}-desc/train-{lang}-desc-tok.{ext}"
valid_filename = f"generation/pair_data_tok_full_desc_comment/{lang}-desc/val-{lang}-desc-tok.txt,generation/pair_data_tok_full_desc_comment/{lang}-desc/val-{lang}-desc-tok.{ext}"
test_filename = f"generation/pair_data_tok_full_desc_comment/{lang}-desc/test-{lang}-desc-tok.txt,generation/pair_data_tok_full_desc_comment/{lang}-desc/test-{lang}-desc-tok.{ext}"
train = create_data(train_filename)
valid = create_data(valid_filename)
test = create_data(test_filename)
train.to_json(f"xlcost-text-to-code/data/{path_program}/train.json", lines=True)
valid.to_json(f"xlcost-text-to-code/data/{path_program}/valid.json", lines=True)
test.to_json(f"xlcost-text-to-code/data/{path_program}/test.json", lines=True)
#push to hub the folder xlcost (containing data/ and xlcost.py dataset builder script)
|