Gensim word2vecで1文字の語彙を取得するのはなぜですか？

私は次のようにword2vecモデルを構築しています。

from gensim.models import word2vec, Phrases
documents = ["the mayor of new york was there", "human computer interaction and machine learning has now become a trending research area","human computer interaction is interesting","human computer interaction is a pretty interesting subject", "human computer interaction is a great and new subject", "machine learning can be useful sometimes","new york mayor was present", "I love machine learning because it is a new subject area", "human computer interaction helps people to get user friendly applications"]
sentence_stream = [doc.split(" ") for doc in documents]

bigram = Phrases(sentence_stream, min_count=1, delimiter=b' ')
trigram = Phrases(bigram[sentence_stream], min_count=1, delimiter=b' ')

for sent in sentence_stream:
    bigrams_ = bigram[sent]
    trigrams_ = trigram[bigram[sent]]

    print(bigrams_)
    print(trigrams_)


# Set values for various parameters
num_features = 10    # Word vector dimensionality                      
min_word_count = 1   # Minimum word count                        
num_workers = 4       # Number of threads to run in parallel
context = 5          # Context window size                                                                                    
downsampling = 1e-3   # Downsample setting for frequent words


model = word2vec.Word2Vec(trigrams_, workers=num_workers, \
            size=num_features, min_count = min_word_count, \
            window = context, sample = downsampling)

vocab = list(model.wv.vocab.keys())
print(vocab[:10])

ただし、モデルの語彙に対して取得する出力は、次のように1文字です。

['h', 'u', 'm', 'a', 'n', ' ', 'c', 'o', 'p', 't']

バイグラムとトリグラムを正しく取得しています。したがって、コードを間違った場所で混乱しているだけです。何が問題なのか教えてください。