余弦相似度 - ValueError：解包的值太多（预期2）

Question

我已经阅读了一些类似的帖子，但没有找到与我的问题完全相同的内容。

我正在计算tfidf值的余弦相似度，并且正在获得ValueError: too many values to unpack (expected 2)

据我所知，这基本上是说预期有2个值，并且有超过2个值被输入，导致错误。但我不确定如何从这里前进。

这是完整的代码

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

def find_similar(tfidf_matrix, index, top_n = 5):
    cosine_similarities = linear_kernel(tfidf_matrix[index:index+1], tfidf_matrix).flatten()
    related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]
    return [(index, cosine_similarities[index]) for index in related_docs_indices][0:top_n]

corpus = []
for doc in nlp.pipe(df['TIP_all_txt'].astype('unicode').values, batch_size=9845,
                        n_threads=3):
    if doc.is_parsed:
        corpus.append([n.text for n in doc if not n.is_punct and not n.is_stop and not n.is_space])
    else:
        corpus.append(None)

tf = TfidfVectorizer(analyzer='word', ngram_range=(1,3), min_df = 0, stop_words = 'english')
tfidf_matrix =  tf.fit_transform([content for doc, doc in corpus])

for me_index, item in enumerate(corpus):
        similar_documents =  [(corpus[index], score) for index, score in find_similar(tfidf_matrix, me_index)]
        me = corpus[me_index]

        document_id = me[0].split("/")[1].split(".")[0]

        for ((raw_similar_document_id, title), score) in similar_documents:
            similar_document_id = raw_similar_document_id.split("/")[1].split(".")[0]
            writer.writerow([document_id, me[1], similar_document_id, title, score])

和错误消息：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-75-8522d91e96f8> in <module>()
      8 
      9 tf = TfidfVectorizer(analyzer='word', ngram_range=(1,3), min_df = 0, stop_words = 'english')
---> 10 tfidf_matrix =  tf.fit_transform([content for doc, doc in corpus])
     11 
     12 for me_index, item in enumerate(corpus):

<ipython-input-75-8522d91e96f8> in <listcomp>(.0)
      8 
      9 tf = TfidfVectorizer(analyzer='word', ngram_range=(1,3), min_df = 0, stop_words = 'english')
---> 10 tfidf_matrix =  tf.fit_transform([content for doc, doc in corpus])
     11 
     12 for me_index, item in enumerate(corpus):

ValueError: too many values to unpack (expected 2)