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8d28a45 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | from __future__ import annotations
import argparse
import csv
import time
from pathlib import Path
try:
from bert_score import score as bertscore
from rouge_score import rouge_scorer
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
except Exception as exc: # pragma: no cover
raise SystemExit(
"Evaluation requires bert-score, rouge-score, torch and transformers. Install dependencies first."
) from exc
from data_utils import load_jsonl
def parse_args():
parser = argparse.ArgumentParser(description="Evaluate summarization models")
parser.add_argument("--test-path", required=True)
parser.add_argument("--model-name", default="fnlp/bart-base-chinese")
parser.add_argument("--max-source-length", type=int, default=512)
parser.add_argument("--target-length", type=int, default=120)
parser.add_argument("--tolerance", type=float, default=0.2)
parser.add_argument("--output-csv", default="metrics_report.csv")
parser.add_argument("--qafacteval-model-folder", default=None)
return parser.parse_args()
def length_hit(text: str, target_length: int, tolerance: float) -> bool:
low = int(target_length * (1 - tolerance))
high = int(target_length * (1 + tolerance))
return low <= len(text) <= high
def try_qafacteval(model_folder: str | None, sources, preds):
if not model_folder:
return [None] * len(preds)
try:
from qafacteval import QAFactEval
except Exception:
return [None] * len(preds)
metric = QAFactEval(
lerc_quip_path=f"{model_folder}/quip-512-mocha",
generation_model_path=f"{model_folder}/generation/model.tar.gz",
answering_model_dir=f"{model_folder}/answering",
lerc_model_path=f"{model_folder}/lerc/model.tar.gz",
lerc_pretrained_model_path=f"{model_folder}/lerc/pretraining.tar.gz",
cuda_device=0 if torch.cuda.is_available() else -1,
use_lerc_quip=True,
verbose=False,
generation_batch_size=8,
answering_batch_size=8,
lerc_batch_size=4,
)
results = metric.score_batch(list(sources), [[p] for p in preds], return_qa_pairs=True)
scores = []
for row in results:
item = row[0]["qa-eval"].get("lerc_quip")
scores.append(item)
return scores
def main():
args = parse_args()
examples = load_jsonl(args.test_path)
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(args.model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
scorer = rouge_scorer.RougeScorer(["rougeL"], use_stemmer=False)
sources = []
refs = []
preds = []
times_ms = []
length_flags = []
for ex in examples:
inputs = tokenizer(
ex.article,
return_tensors="pt",
truncation=True,
max_length=args.max_source_length,
)
inputs.pop("token_type_ids", None)
inputs = {k: v.to(device) for k, v in inputs.items()}
start = time.perf_counter()
with torch.no_grad():
out = model.generate(
**inputs,
max_new_tokens=max(48, min(192, int(args.target_length * 1.1))),
num_beams=4,
no_repeat_ngram_size=3,
length_penalty=1.0,
early_stopping=True,
)
elapsed_ms = (time.perf_counter() - start) * 1000
pred = tokenizer.decode(out[0], skip_special_tokens=True).strip()
sources.append(ex.article)
refs.append(ex.summary)
preds.append(pred)
times_ms.append(elapsed_ms)
length_flags.append(length_hit(pred, args.target_length, args.tolerance))
rouge_ls = [scorer.score(ref, pred)["rougeL"].fmeasure for ref, pred in zip(refs, preds)]
P, R, F1 = bertscore(preds, refs, lang="zh", verbose=False)
qafacteval_scores = try_qafacteval(args.qafacteval_model_folder, sources, preds)
rouge_l = sum(rouge_ls) / max(1, len(rouge_ls))
bert_f1 = float(F1.mean().item()) if hasattr(F1.mean(), "item") else float(F1.mean())
length_rate = sum(1 for v in length_flags if v) / max(1, len(length_flags))
avg_latency = sum(times_ms) / max(1, len(times_ms))
qafacteval_valid = [s for s in qafacteval_scores if s is not None]
qafacteval_avg = sum(qafacteval_valid) / len(qafacteval_valid) if qafacteval_valid else None
print(f"ROUGE-L: {rouge_l:.4f}")
print(f"BERTScore: {bert_f1:.4f}")
print(f"Length Hit Rate: {length_rate:.4f}")
print(f"Avg Latency(ms): {avg_latency:.2f}")
if qafacteval_avg is not None:
print(f"QAFactEval: {qafacteval_avg:.4f}")
else:
print("QAFactEval: N/A")
out_path = Path(args.output_csv)
with out_path.open("w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(["model", "rouge_l", "bertscore", "qafacteval", "length_hit_rate", "avg_latency_ms"])
writer.writerow(
[
args.model_name,
f"{rouge_l:.4f}",
f"{bert_f1:.4f}",
f"{qafacteval_avg:.4f}" if qafacteval_avg is not None else "",
f"{length_rate:.4f}",
f"{avg_latency:.2f}",
]
)
print(f"saved metrics to {out_path}")
if __name__ == "__main__":
main()
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