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import argparse
import json
import re
from collections import Counter, defaultdict
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
from training.benchmark_utils import classify_resume_noise
from training.labels import ID2LABEL
from training.structured_postprocess import StructuredPostProcessor, build_text_and_spans
ALLOWED_INPUTS = {"input_ids", "attention_mask"}
def predicted_spans_from_text(text: str, offset_mapping: list[tuple[int, int]], pred_ids: list[int]) -> tuple[str, list]:
spans = []
current = None
for (start, end), tag_id in zip(offset_mapping, pred_ids):
if start == end:
continue
label = ID2LABEL[tag_id]
if label == "O":
if current:
spans.append(current)
current = None
continue
bio, base = label.split("-", 1)
piece = text[start:end]
if current is None or bio == "B" or current.label != base:
if current:
spans.append(current)
from training.structured_postprocess import Span
current = Span(label=base, text=piece, start=start, end=end, bio=bio, score=1.0)
else:
gap = text[current.end:start]
current.text += gap + piece
current.end = end
if current:
spans.append(current)
return text, spans
def _split_into_sections(text: str) -> list[str]:
"""Split resume text at double-newline boundaries into paragraph blocks."""
return [block for block in re.split(r"\n{2,}", text) if block.strip()]
def chunked_predicted_spans(
text: str,
model,
tokenizer,
max_length: int = 512,
) -> tuple[str, list]:
"""Run inference with section-aware chunking for texts exceeding max_length.
Splits at paragraph boundaries so entities are never cut mid-span.
Each chunk is a group of consecutive sections that fits within max_length.
Character offsets are mapped back to the original text.
"""
num_tokens = len(tokenizer(text, truncation=False)["input_ids"])
if num_tokens <= max_length:
tokenized = tokenizer(text, return_tensors="pt", return_offsets_mapping=True, truncation=True, max_length=max_length)
encoded = {k: v for k, v in tokenized.items() if k in ALLOWED_INPUTS}
with torch.no_grad():
pred_ids = model(**encoded).logits.argmax(dim=-1).squeeze(0).cpu().tolist()
offsets = [tuple(pair) for pair in tokenized["offset_mapping"].squeeze(0).cpu().tolist()][1:-1]
return predicted_spans_from_text(text, offsets, pred_ids[1:-1])
sections = _split_into_sections(text)
chunks: list[str] = []
chunk_offsets: list[int] = []
current_sections: list[str] = []
current_offset = 0
for section in sections:
candidate = "\n\n".join(current_sections + [section]) if current_sections else section
tok_len = len(tokenizer(candidate, truncation=False)["input_ids"])
if tok_len > max_length and current_sections:
chunk_text = "\n\n".join(current_sections)
chunks.append(chunk_text)
chunk_offsets.append(current_offset)
current_offset = text.index(section, current_offset)
current_sections = [section]
else:
if not current_sections:
current_offset = text.index(section, current_offset)
current_sections.append(section)
if current_sections:
chunks.append("\n\n".join(current_sections))
chunk_offsets.append(current_offset)
all_spans = []
for chunk_text, char_offset in zip(chunks, chunk_offsets):
tokenized = tokenizer(chunk_text, return_tensors="pt", return_offsets_mapping=True, truncation=True, max_length=max_length)
encoded = {k: v for k, v in tokenized.items() if k in ALLOWED_INPUTS}
with torch.no_grad():
pred_ids = model(**encoded).logits.argmax(dim=-1).squeeze(0).cpu().tolist()
offsets = [tuple(pair) for pair in tokenized["offset_mapping"].squeeze(0).cpu().tolist()][1:-1]
_, spans = predicted_spans_from_text(chunk_text, offsets, pred_ids[1:-1])
for span in spans:
from training.structured_postprocess import Span
all_spans.append(Span(
label=span.label,
text=span.text,
start=span.start + char_offset,
end=span.end + char_offset,
bio=span.bio,
score=span.score,
))
return text, all_spans
def normalize_value(field: str, value: str | None) -> str | None:
if not value:
return None
normalized = " ".join(value.lower().split()).strip()
if not normalized:
return None
if "phone" in field:
normalized = normalized.replace("+", "plus")
normalized = "".join(ch for ch in normalized if ch.isdigit() or ch.isalpha())
if "email" in field:
normalized = normalized.replace(" ", "")
if "date" in field:
month_map = {
"jan": "january",
"feb": "february",
"mar": "march",
"apr": "april",
"jun": "june",
"jul": "july",
"aug": "august",
"sep": "september",
"oct": "october",
"nov": "november",
"dec": "december",
}
for short, full in month_map.items():
normalized = normalized.replace(short, full)
normalized = normalized.replace(" - ", "-")
return normalized.strip(" ,.;:|/-")
def flatten_resume(parsed: dict) -> dict[str, list[str]]:
flat: dict[str, list[str]] = defaultdict(list)
def push(field: str, value: str | None) -> None:
normalized = normalize_value(field, value)
if normalized:
flat[field].append(normalized)
personal = parsed["personal"]
push("personal.name", personal.get("name"))
push("personal.email", personal.get("email"))
push("personal.phone", personal.get("phone"))
push("personal.location", personal.get("location"))
for exp in parsed["experience"]:
push("experience.title", exp.get("title"))
push("experience.company", exp.get("company"))
push("experience.start_date", exp.get("start_date"))
push("experience.end_date", exp.get("end_date"))
for edu in parsed["education"]:
push("education.degree", edu.get("degree"))
push("education.field", edu.get("field"))
push("education.institution", edu.get("institution"))
for skill in parsed["skills"]:
push("skills", skill)
for cert in parsed["certifications"]:
push("certifications", cert)
push("country", parsed.get("country"))
push("seniority", parsed.get("seniority"))
return flat
def score_field(gold: list[str], pred: list[str]) -> Counter:
gold_counter = Counter(gold)
pred_counter = Counter(pred)
overlap = gold_counter & pred_counter
return Counter(
tp=sum(overlap.values()),
fp=sum((pred_counter - overlap).values()),
fn=sum((gold_counter - overlap).values()),
)
def metrics_from_counts(counts: Counter) -> dict[str, float]:
tp = counts["tp"]
fp = counts["fp"]
fn = counts["fn"]
precision = tp / (tp + fp) if tp + fp else 0.0
recall = tp / (tp + fn) if tp + fn else 0.0
f1 = 2 * precision * recall / (precision + recall) if precision + recall else 0.0
return {"precision": precision, "recall": recall, "f1": f1}
def main() -> None:
parser = argparse.ArgumentParser(
description="Structured extraction benchmark using in-repo post-processing. Better than raw span proxy, still internal-facing."
)
parser.add_argument("--model-dir", default=".")
parser.add_argument("--val-path", default="training/data/ner_val.json")
args = parser.parse_args()
payload = json.load(open(args.val_path))
examples = payload["data"]
tokenizer = AutoTokenizer.from_pretrained(args.model_dir)
model = AutoModelForTokenClassification.from_pretrained(args.model_dir)
model.eval()
postprocessor = StructuredPostProcessor(args.model_dir)
totals_by_field: dict[str, Counter] = {}
bucket_totals: dict[str, Counter] = defaultdict(lambda: Counter(tp=0, fp=0, fn=0, examples=0))
for example in examples:
gold_text, gold_spans = build_text_and_spans(example["tokens"], example["ner_tags"], ID2LABEL)
gold_structured = postprocessor.build_structured_resume_from_spans(gold_spans, gold_text)
bucket_info = classify_resume_noise(gold_text)
bucket = str(bucket_info["bucket"])
bucket_totals[bucket]["examples"] += 1
pred_text, pred_spans = chunked_predicted_spans(gold_text, model, tokenizer)
pred_structured = postprocessor.build_structured_resume_from_spans(pred_spans, pred_text)
gold_flat = flatten_resume(gold_structured)
pred_flat = flatten_resume(pred_structured)
for field in sorted(set(gold_flat) | set(pred_flat)):
counts = score_field(gold_flat.get(field, []), pred_flat.get(field, []))
totals_by_field.setdefault(field, Counter(tp=0, fp=0, fn=0)).update(counts)
bucket_totals[bucket].update(counts)
micro = Counter(tp=0, fp=0, fn=0)
macro_f1 = 0.0
per_field = {}
for field in sorted(totals_by_field):
counts = totals_by_field[field]
micro.update(counts)
metrics = metrics_from_counts(counts)
macro_f1 += metrics["f1"]
per_field[field] = {**counts, **metrics}
output = {
"examples": len(examples),
"micro": {**micro, **metrics_from_counts(micro)},
"macro_f1": macro_f1 / len(per_field) if per_field else 0.0,
"by_bucket": {
bucket: {
"examples": counts["examples"],
"tp": counts["tp"],
"fp": counts["fp"],
"fn": counts["fn"],
**metrics_from_counts(counts),
}
for bucket, counts in sorted(bucket_totals.items())
},
"per_field": per_field,
"note": "Uses in-repo structured post-processing for gold spans and predictions. Better than raw span matching, but still internal regression metric.",
}
print(json.dumps(output, indent=2))
if __name__ == "__main__":
main()
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