File size: 3,245 Bytes
0072b08 | 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 | import argparse
import os
from datasets import DatasetDict, concatenate_datasets, load_from_disk
SOURCE_SPECS = {
"qqp": {"sentence1": "question1", "sentence2": "question2", "keep": lambda label: label == 1},
"mrpc": {"sentence1": "sentence1", "sentence2": "sentence2", "keep": lambda label: label == 1},
"stsb": {"sentence1": "sentence1", "sentence2": "sentence2", "keep": lambda label: label >= 3},
"rte": {"sentence1": "sentence1", "sentence2": "sentence2", "keep": lambda label: label == 1},
"anli": {"sentence1": "sentence1", "sentence2": "sentence2", "keep": lambda label: label == 0},
"smf": {"sentence1": "sentence1", "sentence2": "sentence2", "keep": lambda label: label == 0},
}
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", default="./data")
parser.add_argument("--sources", default="qqp,mrpc,stsb")
parser.add_argument("--output", default="ling_conversion")
return parser.parse_args()
def rename_dev_split(data):
if "dev" not in data and "validation" in data:
data["dev"] = data["validation"]
del data["validation"]
return data
def normalize_columns(data, source_name, sentence1_name, sentence2_name):
for split in data.keys():
split_data = data[split]
rename_map = {}
if sentence1_name in split_data.column_names and sentence1_name != "sentence1":
rename_map[sentence1_name] = "sentence1"
if sentence2_name in split_data.column_names and sentence2_name != "sentence2":
rename_map[sentence2_name] = "sentence2"
if rename_map:
split_data = split_data.rename_columns(rename_map)
if "source" not in split_data.column_names:
split_data = split_data.add_column("source", [source_name] * len(split_data))
keep_columns = [column for column in split_data.column_names if column.startswith("sentence") or column == "source"]
data[split] = split_data.remove_columns(sorted(set(split_data.column_names) - set(keep_columns)))
return data
def main():
args = parse_args()
sources = [source.strip() for source in args.sources.split(",") if source.strip()]
datasets = {}
for source_name in sources:
if source_name not in SOURCE_SPECS:
raise ValueError(f"Unsupported source dataset: {source_name}")
spec = SOURCE_SPECS[source_name]
dataset = load_from_disk(os.path.join(args.data_dir, source_name))
dataset = rename_dev_split(dataset)
dataset = dataset.filter(lambda row: spec["keep"](row["label"]))
dataset = normalize_columns(dataset, source_name, spec["sentence1"], spec["sentence2"])
datasets[source_name] = dataset
merged = DatasetDict(
{
"train": concatenate_datasets([dataset["train"] for dataset in datasets.values() if "train" in dataset]),
"dev": concatenate_datasets([dataset["dev"] for dataset in datasets.values() if "dev" in dataset]),
"test": concatenate_datasets([dataset["test"] for dataset in datasets.values() if "test" in dataset]),
}
)
merged.save_to_disk(os.path.join(args.data_dir, args.output))
if __name__ == "__main__":
main()
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