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[s2s] wmt download script use less ram (#6405)

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Stas Bekman 2020-08-11 09:04:17 -07:00 committed by GitHub
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commit f6cb0f806e
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1 changed files with 21 additions and 17 deletions
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38
examples/seq2seq/download_wmt.py
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@ -4,44 +4,48 @@ import fire
from tqdm import tqdm
def download_wmt_dataset(src_lang, tgt_lang, dataset="wmt19", save_dir=None) -> None:
def download_wmt_dataset(src_lang="ro", tgt_lang="en", dataset="wmt16", save_dir=None) -> None:
"""Download a dataset using the nlp package and save it to the format expected by finetune.py
Format of save_dir: train.source, train.target, val.source, val.target, test.source, test.target.
Args:
src_lang: <str> source language
tgt_lang: <str> target language
dataset: <str> like wmt19 (if you don't know, try wmt19).
dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import nlp; print([d.id for d in nlp.list_datasets() if "wmt" in d.id])`
save_dir: <str>, where to save the datasets, defaults to f'{dataset}-{src_lang}-{tgt_lang}'
Usage:
>>> download_wmt_dataset('en', 'ru', dataset='wmt19') # saves to wmt19_en_ru
>>> download_wmt_dataset('ro', 'en', dataset='wmt16') # saves to wmt16-ro-en
"""
try:
import nlp
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install nlp")
pair = f"{src_lang}-{tgt_lang}"
print(f"Converting {dataset}-{pair}")
ds = nlp.load_dataset(dataset, pair)
if save_dir is None:
save_dir = f"{dataset}-{pair}"
save_dir = Path(save_dir)
save_dir.mkdir(exist_ok=True)
for split in tqdm(ds.keys()):
tr_list = list(ds[split])
data = [x["translation"] for x in tr_list]
src, tgt = [], []
for example in data:
src.append(example[src_lang])
tgt.append(example[tgt_lang])
if split == "validation":
split = "val" # to save to val.source, val.target like summary datasets
src_path = save_dir.joinpath(f"{split}.source")
src_path.open("w+").write("\n".join(src))
tgt_path = save_dir.joinpath(f"{split}.target")
tgt_path.open("w+").write("\n".join(tgt))
print(f"saved dataset to {save_dir}")
for split in ds.keys():
print(f"Splitting {split} with {ds[split].num_rows} records")
# to save to val.source, val.target like summary datasets
fn = "val" if split == "validation" else split
src_path = save_dir.joinpath(f"{fn}.source")
tgt_path = save_dir.joinpath(f"{fn}.target")
src_fp = src_path.open("w+")
tgt_fp = tgt_path.open("w+")
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split]):
ex = x["translation"]
src_fp.write(ex[src_lang] + "\n")
tgt_fp.write(ex[tgt_lang] + "\n")
print(f"Saved {dataset} dataset to {save_dir}")
if __name__ == "__main__":