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File size: 1,884 Bytes
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RANDOMLY Takes 10,000 lines from ../raw_data/raw_mediasum_train_data.txt, 1,000 lines from ../raw_data/raw_mediasum_test_data.txt, and 1,000 lines from ../raw_data/raw_mediasum_val_data.txt. Then converts each one to JSONL.
"""
import random
import json
import os
def reservoir_sample(file_path, k):
reservoir = []
with open(file_path, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
if i < k:
reservoir.append(line.strip())
else:
j = random.randint(0, i)
if j < k:
reservoir[j] = line.strip()
return reservoir
def write_jsonl(lines, output_path):
os.makedirs(os.path.dirname(output_path), exist_ok=True)
with open(output_path, 'w', encoding='utf-8') as f:
for line in lines:
data = json.loads(line)
new_data = {
"id": data["id"],
"original_source": "MediaSum",
"url": data["url"],
"summary": data["summary"],
"transcript": data["utt"],
"speaker": data["speaker"]
}
json.dump(new_data, f, indent=2)
f.write('\n')
print("Cleaning Mediasum dataset...")
ta = '../raw_data/raw_mediasum_train_data.txt'
tb = '../raw_data/raw_mediasum_test_data.txt'
vc = '../raw_data/raw_mediasum_val_data.txt'
train_loc = '../clean1/ms/mediasum_train_10k.jsonl'
test_loc = '../clean1/ms/mediasum_test_1k.jsonl'
val_loc = '../clean1/ms/mediasum_val_1k.jsonl'
print("Sampling lines from raw data files...")
train_lines = reservoir_sample(ta, 10000)
test_lines = reservoir_sample(tb, 1000)
val_lines = reservoir_sample(vc, 1000)
print("Collected Samples. Writing to JSONL files...")
write_jsonl(train_lines, train_loc)
write_jsonl(test_lines, test_loc)
write_jsonl(val_lines, val_loc)
print("Done")
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