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import json
import re
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
@dataclass
class Span:
label: str
text: str
start: int
end: int
bio: str = "B"
score: float = 1.0
class StructuredPostProcessor:
def __init__(self, model_dir: str | Path):
self.model_dir = Path(model_dir)
with open(self.model_dir / "resume_config.json", encoding="utf-8") as fh:
self.config = json.load(fh)
companies_path = self.model_dir / "companies.json"
self.companies = set()
if companies_path.exists():
with open(companies_path, encoding="utf-8") as fh:
data = json.load(fh)
self.companies = {company.lower() for companies in data.values() for company in companies}
self.multi_word_skills = {skill.lower() for skill in self.config.get("multi_word_skills", [])}
pp = self.config.get("post_processing", {})
self.span_merge_max_gap = pp.get("span_merge_max_gap", 3)
self.span_merge_labels = set(pp.get("span_merge_labels", ["TITLE", "COMPANY"]))
self.entity_rules = pp.get("entity_rules", {})
self.skill_aliases = self.entity_rules.get("SKILL", {}).get("aliases", {})
self.cert_aliases = self.entity_rules.get("CERT", {}).get("aliases", {})
self.date_words = set(pp.get("date_words", []))
self.present_words = set(pp.get("present_words", ["present", "current"]))
self.gazetteer_match_max_words = pp.get("company_gazetteer_match_max_words", 3)
self.title_company_separators = pp.get("title_company_separators", [" at "])
self.max_experience_months = pp.get("max_experience_months", 600)
self.space_collapse_pairs = pp.get("space_collapse_pairs", [])
self.country_name_aliases = self.config.get("country_name_aliases", {})
self.seniority_by_exp_count = self.config.get("seniority_by_experience_count", {"Senior": 4, "Mid": 2, "Junior": 0})
self.city_country_map = {}
ccm_file = self.config.get("city_country_map_file")
if ccm_file:
ccm_path = self.model_dir / ccm_file
if ccm_path.exists():
with open(ccm_path) as fh:
data = json.load(fh)
for region in data.values():
self.city_country_map.update(region)
if not self.city_country_map:
self.city_country_map = self.config.get("city_country_map", {})
def build_structured_resume_from_spans(self, spans: list[Span], raw_text: str = "") -> dict:
spans = [Span(**{**span.__dict__, "text": self.clean_entity(span.label, span.text) or ""}) for span in spans]
spans = [span for span in spans if span.text]
spans = self.apply_post_processing(spans)
grouped = self.group_into_entries(spans)
years = self.compute_years(grouped["experience"])
seniority = self.infer_seniority(grouped["experience"], years)
country = self.infer_country(grouped["personal"].get("location"), grouped["personal"].get("phone"))
return {
"personal": {
**grouped["personal"],
"name": self.clean_spaces(grouped["personal"].get("name")) if grouped["personal"].get("name") else None,
},
"experience": grouped["experience"],
"education": grouped["education"],
"skills": [self.clean_spaces(skill) for skill in grouped["skills"]],
"certifications": grouped["certifications"],
"seniority": seniority,
"country": country,
"experience_years": years,
"_rawText": raw_text,
}
@staticmethod
def _merge_same_label_neighbors(spans: list[Span], labels: set[str], max_gap: int = 3) -> list[Span]:
merged: list[Span] = []
for span in spans:
if merged and span.label in labels and merged[-1].label == span.label and span.start - merged[-1].end <= max_gap:
merged[-1] = Span(
label=merged[-1].label,
text=f"{merged[-1].text} {span.text}",
start=merged[-1].start,
end=span.end,
bio=merged[-1].bio,
score=max(merged[-1].score, span.score),
)
else:
merged.append(span)
return merged
def clean_entity(self, label: str, raw: str) -> str | None:
cleaned = re.sub(r"\s+", " ", raw).strip()
cleaned = re.sub(r"^[,.;:|/\-\s]+|[,.;:|/\-\s]+$", "", cleaned)
if not cleaned or (len(cleaned) == 1 and not re.search(r"[a-zA-Z]", cleaned)):
return None
if re.fullmatch(r"[\W_]+", cleaned):
return None
rules = self.entity_rules.get(label, {})
if label == "EMAIL":
cleaned = re.sub(r"\s+", "", cleaned)
for prefix in rules.get("strip_prefixes", []):
cleaned = re.sub(rf"^{re.escape(prefix)}\s*", "", cleaned, flags=re.I)
cleaned = re.sub(r"^[^a-zA-Z0-9]+", "", cleaned)
if rules.get("require") and rules["require"] not in cleaned:
return None
for pattern in rules.get("reject_patterns", []):
if pattern.lower() in cleaned.lower():
return None
elif label == "SKILL":
cleaned = re.sub(r"[,.;:]+$", "", cleaned)
elif label == "COMPANY":
if rules.get("strip_trailing_state_code"):
cleaned = re.sub(r",?\s+[A-Z]{2}$", "", cleaned).strip()
elif label == "DATE":
cleaned = re.sub(r"^[| ]+|[| ]+$", "", cleaned)
if not cleaned:
return None
min_len = rules.get("min_length", 2)
exceptions = {e.lower() for e in rules.get("exceptions", [])}
blocked = {w.lower() for w in rules.get("blocked_words", [])}
if cleaned.lower() in blocked:
return None
if cleaned.lower() in exceptions:
return cleaned
if len(cleaned) < min_len:
if rules.get("gazetteer_bypass") and cleaned.lower() in self.companies:
return cleaned
if rules.get("uppercase_bypass") and cleaned.isupper():
return cleaned
return None
return cleaned
def apply_post_processing(self, spans: list[Span]) -> list[Span]:
spans = self._merge_same_label_neighbors(spans, self.span_merge_labels, self.span_merge_max_gap)
result = [Span(**{**span.__dict__, "label": "COMPANY"}) if span.label == "TITLE" and span.text.lower().strip() in self.companies else span for span in spans]
stripped = []
for span in result:
if span.label != "COMPANY":
stripped.append(span)
continue
words = span.text.split()
while len(words) > 1 and (words[-1].lower() in self.date_words or re.fullmatch(r"\d{4}", words[-1])):
words.pop()
stripped.append(Span(**{**span.__dict__, "text": " ".join(words)}))
result = []
for span in stripped:
if span.label != "TITLE":
result.append(span)
continue
words = span.text.split()
split_done = False
for length in range(min(self.gazetteer_match_max_words, len(words)), 0, -1):
prefix = " ".join(words[:length])
if prefix.lower() in self.companies:
result.append(Span(**{**span.__dict__, "label": "COMPANY", "text": prefix}))
suffix = " ".join(words[length:])
if len(suffix) > 1:
result.append(Span(**{**span.__dict__, "label": "TITLE", "text": suffix}))
split_done = True
break
if not split_done:
result.append(span)
merged = []
i = 0
while i < len(result):
current = result[i]
if current.label == "SKILL" and i + 1 < len(result) and result[i + 1].label == "SKILL":
combined = f"{current.text} {result[i + 1].text}".rstrip(",.")
if combined.lower() in self.multi_word_skills:
merged.append(Span(**{**current.__dict__, "text": combined, "end": result[i + 1].end}))
i += 2
continue
merged.append(current)
i += 1
return merged
def normalize_skill(self, text: str) -> str:
normalized = self.clean_spaces(text.strip().rstrip(",."))
alias = self.skill_aliases.get(normalized.lower())
return alias if alias else normalized
def normalize_certification(self, text: str) -> str:
normalized = self.clean_spaces(text.strip().rstrip(",."))
normalized = re.sub(r"^the\s+", "", normalized, flags=re.I)
alias = self.cert_aliases.get(normalized.lower())
return alias if alias else normalized
@staticmethod
def _dedupe_dict_items(items: list[dict]) -> list[dict]:
seen = set()
deduped = []
for item in items:
key = tuple(sorted((k, v) for k, v in item.items() if v))
if key and key not in seen:
seen.add(key)
deduped.append(item)
return deduped
def clean_experiences(self, experiences: list[dict]) -> list[dict]:
cleaned = []
for exp in experiences:
exp = {k: self.clean_spaces(v) if isinstance(v, str) else v for k, v in exp.items() if v}
if "title" in exp and "company" not in exp:
for sep in self.title_company_separators:
if sep.lower() in exp["title"].lower():
title, company = re.split(re.escape(sep.strip()), exp["title"], maxsplit=1, flags=re.I)
exp["title"] = self.clean_spaces(title)
exp["company"] = self.clean_spaces(company)
break
cleaned.append(exp)
if any(exp.get("company") or exp.get("start_date") for exp in cleaned[1:]):
while cleaned and cleaned[0].get("title") and not cleaned[0].get("company") and not cleaned[0].get("start_date"):
cleaned.pop(0)
return self._dedupe_dict_items(cleaned)
def clean_education(self, education: list[dict]) -> list[dict]:
cleaned = []
for edu in education:
item = {k: self.clean_spaces(v) if isinstance(v, str) else v for k, v in edu.items() if v}
if item:
cleaned.append(item)
return self._dedupe_dict_items(cleaned)
def group_into_entries(self, spans: list[Span]) -> dict:
personal = {"name": None, "email": None, "phone": None, "location": None}
for span in spans:
if span.label == "NAME" and not personal["name"]:
personal["name"] = span.text
elif span.label == "EMAIL" and not personal["email"]:
cleaned = self.clean_entity("EMAIL", span.text)
if cleaned:
personal["email"] = cleaned
elif span.label == "PHONE" and not personal["phone"]:
personal["phone"] = self.clean_phone(span.text)
elif span.label == "LOCATION" and not personal["location"]:
personal["location"] = self.clean_spaces(span.text)
exp_spans = sorted([span for span in spans if span.label in {"TITLE", "COMPANY", "DATE"}], key=lambda span: span.start)
experiences = []
current = {}
for span in exp_spans:
if span.label == "TITLE":
if current.get("title") and (current.get("company") or current.get("start_date")):
experiences.append(current)
current = {}
current["title"] = self.clean_spaces(span.text)
elif span.label == "COMPANY":
if current.get("company") and (current.get("title") or current.get("start_date")):
experiences.append(current)
current = {}
current["company"] = self.clean_spaces(self.clean_entity("COMPANY", span.text) or "")
elif span.label == "DATE":
date_text = re.sub(r"^[| ]+|[| ]+$", "", span.text)
if not date_text:
continue
present_pattern = "|".join(re.escape(w) for w in self.present_words)
present_match = re.match(rf"^(.+?)\s+({present_pattern})$", date_text, flags=re.I)
if present_match and not current.get("start_date"):
current["start_date"] = present_match.group(1).strip()
current["end_date"] = present_match.group(2)
continue
if current.get("start_date") and not current.get("end_date") and re.fullmatch(r"[a-zA-Z]+", current["start_date"]) and re.match(r"^\d{4}", date_text):
year_match = re.match(r"^(\d{4})\s*(.*)", date_text)
if year_match:
current["start_date"] = f"{current['start_date']} {year_match.group(1)}"
if year_match.group(2):
current["end_date"] = year_match.group(2).strip()
continue
if current.get("start_date") and current.get("end_date"):
if current.get("title") or current.get("company"):
experiences.append(current)
current = {}
if not current.get("start_date"):
current["start_date"] = date_text
elif not current.get("end_date"):
current["end_date"] = date_text
if current.get("title") or current.get("company"):
experiences.append(current)
experiences = self.clean_experiences(experiences)
edu_spans = sorted([span for span in spans if span.label in {"DEGREE", "FIELD", "INSTITUTION"}], key=lambda span: span.start)
education = []
current_edu = {}
for span in edu_spans:
if span.label == "DEGREE":
if current_edu.get("degree"):
education.append(current_edu)
current_edu = {}
current_edu["degree"] = self.clean_spaces(span.text)
elif span.label == "FIELD":
current_edu["field"] = self.clean_spaces(span.text)
elif span.label == "INSTITUTION":
current_edu["institution"] = re.sub(r",?\s*\d{4}\s*$", "", self.clean_spaces(span.text))
education.append(current_edu)
current_edu = {}
if current_edu.get("degree") or current_edu.get("institution"):
education.append(current_edu)
education = self.clean_education(education)
skill_rules = self.entity_rules.get("SKILL", {})
skill_min = skill_rules.get("min_length", 2)
skills = []
seen = set()
for span in spans:
if span.label != "SKILL":
continue
for part in re.split(r",\s*", span.text):
clean = self.normalize_skill(part)
if not clean or clean.lower() in seen:
continue
if len(clean) < skill_min and not clean.isupper() and clean.lower() not in {e.lower() for e in skill_rules.get("exceptions", [])}:
continue
seen.add(clean.lower())
skills.append(clean)
certifications = []
cert_seen = set()
for span in spans:
if span.label != "CERT":
continue
clean = self.normalize_certification(span.text)
if len(clean) > 1 and clean.lower() not in cert_seen:
cert_seen.add(clean.lower())
certifications.append(clean)
return {"personal": personal, "experience": experiences, "education": education, "skills": skills, "certifications": certifications}
def infer_seniority(self, experiences: list[dict], years: int | None) -> str:
keywords = self.config["seniority_keywords"]
titles = [(exp.get("title") or "").lower() for exp in experiences if exp.get("title")]
for level, level_keywords in keywords.items():
for title in titles:
for keyword in level_keywords:
if keyword in title:
return level
if years is not None:
bounds = self.config["seniority_by_years"]
if years >= bounds["Staff"]:
return "Staff"
if years >= bounds["Senior"]:
return "Senior"
if years >= bounds["Mid"]:
return "Mid"
return "Junior"
for level, min_count in sorted(self.seniority_by_exp_count.items(), key=lambda x: -x[1]):
if len(experiences) >= min_count:
return level
return "Junior"
def infer_country(self, location: str | None, phone: str | None) -> str | None:
if phone:
clean = re.sub(r"[\s\-()]", "", phone)
for prefix, country in self.config["phone_country_prefixes"].items():
if clean.startswith(prefix):
return country
if location:
loc = location.lower()
for alias, country in self.country_name_aliases.items():
if alias in loc:
return country
for city, country in self.city_country_map.items():
if city in loc:
return country
for part in loc.replace(",", " ").split():
if part.upper() in self.config["us_states"]:
return "United States"
return None
def compute_years(self, experiences: list[dict]) -> int | None:
total_months = 0
now = datetime.now()
present_re = "|".join(re.escape(w) for w in self.present_words)
for exp in experiences:
if not exp.get("start_date"):
continue
start = self.parse_date(exp["start_date"])
if not start:
continue
if not exp.get("end_date") or re.search(present_re, exp["end_date"], flags=re.I):
end = now
else:
end = self.parse_date(exp["end_date"])
if not end:
continue
months = (end.year - start.year) * 12 + (end.month - start.month)
if 0 < months < self.max_experience_months:
total_months += months
return total_months // 12 if total_months > 0 else None
@staticmethod
def parse_date(text: str) -> datetime | None:
months = {
"january": 1, "february": 2, "march": 3, "april": 4, "may": 5, "june": 6, "july": 7, "august": 8,
"september": 9, "october": 10, "november": 11, "december": 12, "jan": 1, "feb": 2, "mar": 3,
"apr": 4, "jun": 6, "jul": 7, "aug": 8, "sep": 9, "oct": 10, "nov": 11, "dec": 12,
}
lower = text.lower().strip()
for name, month in months.items():
match = re.search(rf"{name}\s+(\d{{4}})", lower)
if match:
return datetime(int(match.group(1)), month, 1)
year_match = re.search(r"\b(19|20)\d{2}\b", text)
if year_match:
return datetime(int(year_match.group(0)), 6, 1)
return None
def clean_spaces(self, text: str) -> str:
result = text
for old, new in self.space_collapse_pairs:
result = result.replace(old, new)
return result.rstrip(",").strip()
@staticmethod
def clean_phone(phone: str) -> str:
return re.sub(r"\s+", " ", re.sub(r"\+\s+", "+", re.sub(r"\s+-\s+", "-", re.sub(r"\s+\)", ")", re.sub(r"\(\s+", "(", phone))))).strip()
def build_text_and_spans(tokens: list[str], ner_tags: list[int], id2label: dict[int, str]) -> tuple[str, list[Span]]:
text = ""
positions = []
for token in tokens:
start = len(text)
text += (" " if text else "") + token
real_start = start + (1 if start else 0)
positions.append((real_start, real_start + len(token)))
spans = []
current = None
for i, token in enumerate(tokens):
label = id2label[ner_tags[i]]
if label == "O":
if current:
spans.append(current)
current = None
continue
bio, base = label.split("-", 1)
start, end = positions[i]
if current is None or bio == "B" or current.label != base:
if current:
spans.append(current)
current = Span(label=base, text=token, start=start, end=end, bio=bio, score=1.0)
else:
current.text += f" {token}"
current.end = end
if current:
spans.append(current)
return text, spans
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