# 创建并保存为 JSON bm25_mixed_json = create_bm25(mixed_corpus, 'mixed') bm25_mixed_json.save(os.path.join(output_dir, 'bm25_mixed.json')) #从 JSON 加载并搜索 loaded_bm25_mixed_json = load_bm25(os.path.join(output_dir, 'bm25_mixed.json'), mixed_corpus) print("混合语言查询(JSON...
def __init__(self, corpus, k1=1.5, b=0.75): self.k1 = k1 self.b = b self.corpus = corpus self.doc_lengths = [len(doc) for doc in corpus] self.avg_doc_length = sum(self.doc_lengths) / len(self.doc_lengths) self.doc_count = len(corpus) self.doc_term_freqs = [Counter(do...
sorted_scores =sorted(scores, key=lambdax: x[1], reverse=True)returnsorted_scores# Example usagecorpus = ["The quick brown fox jumps over the lazy dog","A quick brown dog outpaces a swift fox","The dog is lazy but the fox is swift","Lazy dogs and swift foxes"] bm25 = BM25(corp...
def test_gensim_bm25(): corpus = [ ['来', '问', '几', '个', '问题', '第1', '个', '就', '是', '60', '岁', '60', '岁', '的', '时候', '退休', '是', '时间', '到', '了', '一定', '要', '退休', '还是', '觉得', '应该', '差', '不', '多'], ...
2. 计算每个查询词在文档中的出现频率(term frequency)和在整个文集中的出现频率(corpus term frequency)。 3. 使用BM25公式计算每个查询词的得分(score): score(qi, D) = idf(qi) * (tf(qi, D) * (k + 1)) / (tf(qi, D) + k * (1 - b + b * ,D, / avgdl)) 其中,qi是查询词,D是...
def_calc_idf(self,nd):raiseNotImplementedError()defget_scores(self,query):raiseNotImplementedError()defget_batch_scores(self,query,doc_ids):raiseNotImplementedError()defget_top_n(self,query,documents,n=5):assert self.corpus_size==len(documents),"The documents given don't match the index corpus!
PARAM_K1=1.5PARAM_B=0.75EPSILON=0.25classBM25(object):def__init__(self,corpus):self.corpus_size=len(corpus)self.avgdl=sum(map(lambda x:float(len(x)),corpus))/self.corpus_size self.corpus=corpus self.f=[]self.df={}self.idf={}self.initialize()definitialize(self):fordocumentinself.corp...
self.avgdl = num_doc / self.corpus_size return nd def _tokenize_corpus(self, corpus): pool = Pool(cpu_count()) tokenized_corpus = pool.map(self.tokenizer, corpus) return tokenized_corpus def _calc_idf(self, nd): raise NotImplementedError() ...
The 2nd argument to theCorpusconstructor is an options object, which can contain the following properties: processor(function) - A function to convert each document to an array of strings. k1(number between 1.2 and 2, default: 1.5) - Controls the impact of term frequency saturation. ...
安装pip install rank-bm25from rank_bm25 import BM25Okapicorpus = [ "Hello there good man j 原创 TechOnly 2022-07-19 11:51:08 404阅读 用python实现bm25算法 # 用 Python 实现BM25 算法的入门指南 BM25(Best Matching 25)是一种用于信息检索的排名函数,广泛应用于文档检索和推荐系统中。本文将教你...