【NLP 计算句子之间的BLEU和ROUGE分数】

安装依赖

pip install nltk rouge-score

批量处理代码

from nltk.translate.bleu_score import corpus_bleu, SmoothingFunction
from rouge_score import rouge_scorer
import nltk# 下载必要的资源（第一次运行需要）
nltk.download('wordnet')  # 用于 BLEU 的 tokenizer 等
nltk.download('punkt')     # 用于 tokenizedef batch_bleu(references, candidates):"""计算批量 BLEU 分数 (BLEU-4)Args:references: List of lists of reference sentences (每项是多个参考答案列表)candidates: List of candidate sentences (模型生成的句子列表)Returns:float: 平均 BLEU-4 分数"""smoothing = SmoothingFunction()# 将每个参考句子 tokenizetokenized_references = [[nltk.word_tokenize(sent) for sent in ref] for ref in references]# 将每个候选句子 tokenizetokenized_candidates = [nltk.word_tokenize(sent) for sent in candidates]# 计算 corpus BLEUbleu_score = corpus_bleu(tokenized_references,tokenized_candidates,weights=(0.25, 0.25, 0.25, 0.25),smoothing_function=smoothing.method1)return bleu_scoredef batch_rouge(references, candidates):"""计算批量 ROUGE 分数 (ROUGE-1, ROUGE-2, ROUGE-L)Args:references: List of reference sentences (每个样本一个参考句)candidates: List of candidate sentencesReturns:dict: {'rouge1': f1, 'rouge2': f1, 'rougeL': f1}"""scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)scores = {'rouge1': [], 'rouge2': [], 'rougeL': []}for ref, cand in zip(references, candidates):score = scorer.score(ref, cand)scores['rouge1'].append(score['rouge1'].fmeasure)scores['rouge2'].append(score['rouge2'].fmeasure)scores['rougeL'].append(score['rougeL'].fmeasure)avg_scores = {k: sum(v)/len(v) for k, v in scores.items()}return avg_scoresdef evaluate_all(references, candidates):"""同时计算 BLEU 和 ROUGE 的批量评估函数Args:references: List of reference sentencescandidates: List of candidate sentencesReturns:dict: 包含 BLEU 和 ROUGE 的平均分数"""bleu = batch_bleu([[ref] for ref in references], candidates)rouge = batch_rouge(references, candidates)return {'BLEU': round(bleu, 4),'ROUGE-1': round(rouge['rouge1'], 4),'ROUGE-2': round(rouge['rouge2'], 4),'ROUGE-L': round(rouge['rougeL'], 4)}

测试代码

# 示例数据：批量输入
references = ["the cat is on the mat","a dog is playing in the garden"
]candidates = ["the cat sat on the mat","a dog plays in the garden"
]# 调用评估函数
results = evaluate_all(references, candidates)
print("Evaluation Results:", results)

输出

Evaluation Results: {'BLEU': 0.1966, 'ROUGE-1': 0.8782, 'ROUGE-2': 0.6636, 'ROUGE-L': 0.8782}