Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Languages:
Persian
Size:
10K - 100K
ArXiv:
DOI:
License:
File size: 4,060 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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | #!/usr/bin/env python
"""Build batched annotation requests for the LLM NER kit.
Renders the system prompt by substituting `{{GUIDELINES}}` in the prompt template with the
full text of GUIDELINES.md, so the guideline is the single source of truth: editing it
changes `prompt_hash` and therefore every `cache_key`, invalidating stale responses.
python scripts/annotation/build_requests.py \
--input annotation/data/select-100.jsonl \
--prompt annotation/prompts/ner-v1.md \
--batch 10 --out annotation/work/requests/select-100.jsonl
"""
import argparse
import hashlib
import json
from pathlib import Path
INSTRUCTION = "Annotate every sentence. Return one item per id."
SCHEMA = {
"type": "object",
"properties": {
"items": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {"type": "string"},
"entities": {
"type": "array",
"items": {
"type": "object",
"properties": {
"text": {"type": "string"},
"label": {"type": "string", "enum": ["PER", "LOC", "ORG", "DAT"]},
},
"required": ["text", "label"],
},
},
},
"required": ["id", "entities"],
},
}
},
"required": ["items"],
}
def sha256(s):
return hashlib.sha256(s.encode("utf8")).hexdigest()
def render_prompt(prompt_path, guidelines_path):
template = prompt_path.read_text(encoding="utf8")
if "{{GUIDELINES}}" not in template:
raise SystemExit(f"{prompt_path} has no {{{{GUIDELINES}}}} placeholder")
return template.replace("{{GUIDELINES}}", guidelines_path.read_text(encoding="utf8").strip())
def main():
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("--input", required=True)
ap.add_argument("--prompt", default="annotation/prompts/ner-v2.2.md")
ap.add_argument("--guidelines", default="annotation/GUIDELINES.md")
# 20 measured on pool-500 against batch 10, same prompt: 25/25 batches returned every
# id with no retry, micro F vs silver 0.7778 against 0.7789, and the two runs agree at
# F 0.976 — tighter than run-to-run noise at a fixed batch size (0.949-0.951).
ap.add_argument("--batch", type=int, default=20)
ap.add_argument("--model", default="default")
ap.add_argument("--out", required=True)
args = ap.parse_args()
system = render_prompt(Path(args.prompt), Path(args.guidelines))
prompt_hash = sha256(system)[:12]
sents = [json.loads(line) for line in Path(args.input).open(encoding="utf8")]
out = Path(args.out)
out.parent.mkdir(parents=True, exist_ok=True)
name = out.name[: -len(".jsonl")] if out.name.endswith(".jsonl") else out.name
n = 0
with out.open("w", encoding="utf8") as fh:
for i in range(0, len(sents), args.batch):
chunk = sents[i : i + args.batch]
pairs = [{"id": s["id"], "text": s["text"]} for s in chunk]
lines = [f"{p['id']}: {p['text']}" for p in pairs]
body = INSTRUCTION + "\n\n" + "\n".join(lines)
req = {
"batch": f"{name}/{i // args.batch:03d}",
"model": args.model,
"ids": [p["id"] for p in pairs],
"sentences": pairs,
"system": system,
"body": body,
"schema": SCHEMA,
"prompt_hash": prompt_hash,
"cache_key": sha256(prompt_hash + args.model + "\n".join(lines)),
}
fh.write(json.dumps(req, ensure_ascii=False) + "\n")
n += 1
print(f"requests={n} sentences={len(sents)} batch={args.batch} model={args.model} "
f"prompt_hash={prompt_hash} -> {out}")
if __name__ == "__main__":
main()
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