Datasets:
File size: 2,868 Bytes
146a370 | 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 | #!/usr/bin/env python3
"""Build a ready-to-train text view from a local references-only release."""
import argparse
import itertools
import os
from pathlib import Path
import tempfile
import pyarrow as pa
from finepdf_release import FinePDFResolver, iter_release_rows, iter_training_examples
from release_data import ShardedWriter, write_json
def main(argv=None):
p = argparse.ArgumentParser(description=__doc__)
p.add_argument("--release-dir", type=Path, required=True)
p.add_argument("--config", help="Language config, e.g. eng_Latn or fra_Latn; required at the combined root")
p.add_argument("--output-dir", type=Path, required=True)
p.add_argument("--local-source-root", type=Path)
p.add_argument("--cache-dir")
p.add_argument("--num-negatives", type=int, default=8)
p.add_argument("--all-queries", action="store_true")
p.add_argument("--limit", type=int, help="Maximum emitted examples, including additional queries")
p.add_argument("--rows-per-shard", type=int, default=100_000)
args = p.parse_args(argv)
if args.num_negatives < 0 or args.rows_per_shard <= 0 or (args.limit is not None and args.limit <= 0):
p.error("Invalid negative count, shard size, or limit")
if args.output_dir.exists():
p.error("Output already exists; choose a new directory")
args.output_dir.parent.mkdir(parents=True, exist_ok=True)
staging = Path(tempfile.mkdtemp(prefix=args.output_dir.name + ".partial-", dir=args.output_dir.parent))
schema = pa.schema([(name, pa.string()) for name in
["query", "positive"] + ["negative_{}".format(i) for i in range(args.num_negatives)]])
writer = ShardedWriter(staging, "train", schema, args.rows_per_shard)
with FinePDFResolver(args.release_dir, args.local_source_root, args.cache_dir, config=args.config) as resolver:
examples = iter_training_examples(iter_release_rows(args.release_dir, config=args.config), resolver,
args.num_negatives, args.all_queries)
if args.limit is not None:
examples = itertools.islice(examples, args.limit)
while True:
batch = list(itertools.islice(examples, 256))
if not batch:
break
writer.write(pa.Table.from_pylist(batch, schema=schema))
manifest = writer.close()
if manifest["num_rows"] == 0:
raise ValueError("No examples to materialize")
write_json(staging / "manifest.json", {"train": manifest, "num_negatives": args.num_negatives,
"all_queries": args.all_queries})
write_json(staging / "_SUCCESS", {"num_rows": manifest["num_rows"]})
os.rename(staging, args.output_dir)
print("Wrote {} examples to {}".format(manifest["num_rows"], args.output_dir))
if __name__ == "__main__":
main()
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