Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Languages:
Persian
Size:
10K - 100K
ArXiv:
DOI:
License:
File size: 3,250 Bytes
ea59bf2 | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | #!/usr/bin/env python
"""Assemble the LLM annotations into a drop-in replacement for corpus/perdt-ner-iob.
python scripts/annotation/export_iob.py
Writes two-column IOB2 (`token\\tTAG`, blank line between sentences) in the exact sentence
order and tokenization of the source corpus, so the result can be swapped into project.yml's
`spacy convert` step by changing one path:
corpus/perdt-ner-iob-llm/{train,dev,test}.txt
Sentence order and token strings are asserted against corpus/perdt-ner-iob, so a partial or
misaligned annotation set fails loudly instead of silently training on shifted labels. The
output lives under corpus/ because it is a build artifact regenerable from the committed
JSONL in annotation/data/llm/.
"""
import argparse
import json
from pathlib import Path
SOURCES = {
"train": [f"train/shard-{i:03d}" for i in range(14)],
"dev": ["dev-all"],
"test": ["test-all"],
}
def iob_sents(path):
sents, cur = [], []
for line in path.open(encoding="utf8"):
line = line.rstrip("\n")
if not line.strip():
if cur:
sents.append(cur)
cur = []
continue
parts = line.split("\t") if "\t" in line else line.split()
cur.append(parts[0])
if cur:
sents.append(cur)
return sents
def tag(tokens, entities):
tags = ["O"] * len(tokens)
for e in entities:
tags[e["start"]] = f"B-{e['label']}"
for i in range(e["start"] + 1, e["end"]):
tags[i] = f"I-{e['label']}"
return tags
def main():
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("--llm-dir", default="annotation/data/llm/ner-v2.2-default")
ap.add_argument("--reference", default="corpus/perdt-ner-iob")
ap.add_argument("--out-dir", default="corpus/perdt-ner-iob-llm")
args = ap.parse_args()
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
for split, parts in SOURCES.items():
rows = []
for part in parts:
path = Path(args.llm_dir) / f"{part}.jsonl"
rows += [json.loads(line) for line in path.open(encoding="utf8")]
ref = iob_sents(Path(args.reference) / f"{split}.txt")
if len(rows) != len(ref):
raise SystemExit(f"{split}: {len(rows)} annotated sentences against {len(ref)} in corpus")
for i, (row, tokens) in enumerate(zip(rows, ref)):
if row["tokens"] != tokens:
raise SystemExit(f"{split}: token mismatch at sentence {i} ({row['id']})")
blocks, spans, unannotated = [], 0, 0
for row in rows:
spans += len(row["entities"])
unannotated += 1 if row.get("error") == "missing" else 0
tags = tag(row["tokens"], row["entities"])
blocks.append("\n".join(f"{t}\t{g}" for t, g in zip(row["tokens"], tags)))
# trailing blank line, matching corpus/perdt-ner-iob byte for byte in column 1
(out_dir / f"{split}.txt").write_text("\n\n".join(blocks) + "\n\n", encoding="utf8")
print(f"{split}.txt: {len(rows)} sentences, {spans} entities, "
f"{unannotated} unannotated -> {out_dir / f'{split}.txt'}")
if __name__ == "__main__":
main()
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