File size: 5,748 Bytes
4129d85 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | from __future__ import annotations
import argparse
import json
from collections import Counter, defaultdict
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
from training.benchmark_structured import ALLOWED_INPUTS, flatten_resume, predicted_spans_from_text
from training.benchmark_utils import classify_resume_noise
from training.labels import ID2LABEL
from training.structured_postprocess import StructuredPostProcessor, build_text_and_spans
def counter_diff_size(gold: list[str], pred: list[str]) -> tuple[int, int, int]:
gold_counter = Counter(gold)
pred_counter = Counter(pred)
overlap = gold_counter & pred_counter
tp = sum(overlap.values())
fp = sum((pred_counter - overlap).values())
fn = sum((gold_counter - overlap).values())
return tp, fp, fn
def main() -> None:
parser = argparse.ArgumentParser(description="Analyze per-resume structured extraction errors and outliers")
parser.add_argument("--model-dir", default=".")
parser.add_argument("--val-path", default="training/data/ner_val.json")
parser.add_argument("--top-k", type=int, default=10)
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)
per_resume = []
field_error_counts = Counter()
bucket_counts = Counter()
total_fp = 0
total_fn = 0
total_tp = 0
for idx, example in enumerate(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_counts[str(bucket_info["bucket"])] += 1
tokenized = tokenizer(gold_text, return_tensors="pt", return_offsets_mapping=True, truncation=True, max_length=512)
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]
pred_text, pred_spans = predicted_spans_from_text(gold_text, offsets, pred_ids[1:-1])
pred_structured = postprocessor.build_structured_resume_from_spans(pred_spans, pred_text)
gold_flat = flatten_resume(gold_structured)
pred_flat = flatten_resume(pred_structured)
resume_fp = 0
resume_fn = 0
mismatched_fields = []
for field in sorted(set(gold_flat) | set(pred_flat)):
tp, fp, fn = counter_diff_size(gold_flat.get(field, []), pred_flat.get(field, []))
total_tp += tp
total_fp += fp
total_fn += fn
resume_fp += fp
resume_fn += fn
if fp or fn:
field_error_counts[field] += fp + fn
mismatched_fields.append(
{
"field": field,
"gold": gold_flat.get(field, []),
"pred": pred_flat.get(field, []),
"fp": fp,
"fn": fn,
}
)
per_resume.append(
{
"index": idx,
"text_preview": gold_text[:300],
"bucket": bucket_info["bucket"],
"noise_signals": bucket_info["signals"],
"gold_field_count": sum(len(v) for v in gold_flat.values()),
"pred_field_count": sum(len(v) for v in pred_flat.values()),
"tp": sum(min(Counter(gold_flat.get(f, []))[k], Counter(pred_flat.get(f, []))[k]) for f in set(gold_flat) | set(pred_flat) for k in (Counter(gold_flat.get(f, [])) & Counter(pred_flat.get(f, [])))),
"fp": resume_fp,
"fn": resume_fn,
"errors": resume_fp + resume_fn,
"mismatched_fields": mismatched_fields,
}
)
error_values = [item["errors"] for item in per_resume]
avg_errors = sum(error_values) / len(error_values) if error_values else 0.0
median_errors = sorted(error_values)[len(error_values) // 2] if error_values else 0
zero_error = sum(1 for value in error_values if value == 0)
one_or_less = sum(1 for value in error_values if value <= 1)
three_or_more = sum(1 for value in error_values if value >= 3)
outliers = sorted(per_resume, key=lambda item: (-item["errors"], item["index"]))[: args.top_k]
summary = {
"examples": len(per_resume),
"avg_errors_per_resume": avg_errors,
"median_errors_per_resume": median_errors,
"zero_error_resumes": zero_error,
"zero_error_rate": zero_error / len(per_resume) if per_resume else 0.0,
"one_or_less_error_resumes": one_or_less,
"one_or_less_error_rate": one_or_less / len(per_resume) if per_resume else 0.0,
"three_or_more_error_resumes": three_or_more,
"three_or_more_error_rate": three_or_more / len(per_resume) if per_resume else 0.0,
"micro": {
"tp": total_tp,
"fp": total_fp,
"fn": total_fn,
},
"bucket_counts": dict(bucket_counts),
"top_error_fields": field_error_counts.most_common(10),
"outliers": outliers,
"note": "errors = fp + fn over flattened structured fields per resume",
}
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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