将矩阵中的余弦相似嵌入转换为熊猫数据

Question

我在将数组中的余弦相似性分配给熊猫Dataframe时遇到了问题。我使用下面的代码测试了余弦相似矩阵

# Find the closest 5 sentences of the corpus for each query sentence based on cosine similarity
top_k = min(5, len(corpus))
for query in queries:
    query_embedding = model.encode(query, convert_to_tensor=True)

    # We use cosine-similarity and torch.topk to find the highest 5 scores
    cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
    top_results = torch.topk(cos_scores, k=top_k)

    print("\n\n======================\n\n")
    print("Query:", query)
    print("\nTop 5 most similar sentences in corpus:")

    for score, idx in zip(top_results[0], top_results[1]):
        print(corpus[idx], "(Score: {:.4f})".format(score))

下面是代码产生的输出

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不过，我想将相似性评分写回结构如下的Dataframe

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复制示例的虚拟数据代码

df1 = pd.DataFrame(columns=['Query','Corpus'])
df1['Query'] = ["A man is eating pasta","A man is eating pasta","A man is eating pasta","A man is eating pasta","A man is eating pasta"]
df1['Corpus'] = ["A man is eating food","A man is eating a piece of bread.","A man is riding a horse","A man is riding a white horse on an enclosed ground","A cheetah is running behind its prey"]

df1

**详细示例可在此处找到https://www.codegrepper.com/code-examples/python/sentence+transformers **

我确实提到了类似的问题Cosine Similarity for Sentences in Dataframe & Cosine similarity of rows in pandas DataFrame，但是它们没有回答我的问题。任何指示都会有帮助。

Answer 1

我不知道你试过什么，但是一本简单的字典会是我想要的。

dc = {'Query':[],'Corpus':[],'Cos_sim':[]} 

    for query in queries:
        query_embedding = model.encode(query, convert_to_tensor=True)
        cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
        top_results = torch.topk(cos_scores, k=top_k)
        dc['Query'] = [query] * 5
        for score, idx in zip(top_results[0], top_results[1]):
            dc['Corpus'] = corpus[idx]
            dc['Cos_sim'] = score

在那之后就做吧

df = pd.DataFrame(dc)

会给你通缉的df