Datasets:
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from pathlib import Path
import mteb
log_file_path = Path("remove_empty.log")
# remove log file if exists
if log_file_path.exists():
log_file_path.unlink()
tasks = mteb.get_tasks(tasks=["STS22"])
from datasets import load_dataset
dataset = load_dataset(**tasks[0].metadata.dataset)
def filter_sample(x):
if len(x["sentence1"]) > 0 and len(x["sentence2"]) > 0:
return True
log = f"Filtered: {x['sentence1']} -- {x['sentence2']}"
with open(log_file_path, "a") as f:
f.write(log + "\n")
print(log)
return False
for split in dataset:
ds = dataset[split]
# filter empty sentences
n_samples = len(ds)
ds = ds.filter(lambda x: filter_sample(x))
n_left = len(ds)
log = f"Filtered {n_samples - n_left} samples from {n_samples} in {split}"
with open(log_file_path, "a") as f:
f.write(log + "\n")
print(log)
dataset[split] = ds
save_path = Path(__file__).parent.parent / "data"
for split in dataset:
# dataset[split].to_parquet(save_path / f"{split}-00000-of-00001.parquet")
dataset[split].to_json(save_path / f"{split}.jsonl.gz", compression="gzip")
ds = load_dataset(tasks[0].metadata.dataset["path"])
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