AttributeError: 'list' object has no attribute 'words' in python gensim module
AttributeError: 'list' object has no attribute 'words' in python gensim module
在使用 doc2vec 进行训练时，出现此错误：
AttributeError: 'list' object has no attribute 'words' in python gensim module
这是我的代码：
# Extracting titles from csv to list
with open(query+'_titles.csv', 'rb') as f:
reader = csv.reader(f)
titlelist = list(reader)
# build
model = doc2vec.Doc2Vec(size=30, window=1, alpha=0.01, min_count=2, sample=1e-5, workers=100)
model.build_vocab(titlelist)
titlearray = np.asarray(titlelist)
print 'Training Model...'
我使用 python 2.7.11 并且 gensim 版本是 3.2.0 如果有帮助的话。必须有一些我真的很想念的东西。
Doc2Vec 不仅需要句子列表，还需要 标记的 句子列表。来自 this discussion on DS.SE:
In word2vec there is no need to label the words, because every word
has their own semantic meaning in the vocabulary. But in case of
doc2vec, there is a need to specify that how many number of words or
sentences convey a semantic meaning, so that the algorithm could
identify it as a single entity. For this reason, we are specifying
labels or tags to sentence or paragraph depending on the level of
semantic meaning conveyed.
因此，Gensim 需要以下输入：
sentences = [doc2vec.TaggedDocument(sentence, 'tag') for sentence in titlelist]
model.build_vocab(sentences)
显然，您可能希望根据句子设置不同的标签以获得有意义的向量。顺便问一下，您确定要以二进制模式读取 CSV 文件吗？
在使用 doc2vec 进行训练时，出现此错误：
AttributeError: 'list' object has no attribute 'words' in python gensim module
这是我的代码：
# Extracting titles from csv to list
with open(query+'_titles.csv', 'rb') as f:
reader = csv.reader(f)
titlelist = list(reader)
# build
model = doc2vec.Doc2Vec(size=30, window=1, alpha=0.01, min_count=2, sample=1e-5, workers=100)
model.build_vocab(titlelist)
titlearray = np.asarray(titlelist)
print 'Training Model...'
我使用 python 2.7.11 并且 gensim 版本是 3.2.0 如果有帮助的话。必须有一些我真的很想念的东西。
Doc2Vec 不仅需要句子列表，还需要 标记的 句子列表。来自 this discussion on DS.SE:
In
word2vecthere is no need to label the words, because every word has their own semantic meaning in the vocabulary. But in case ofdoc2vec, there is a need to specify that how many number of words or sentences convey a semantic meaning, so that the algorithm could identify it as a single entity. For this reason, we are specifying labels or tags to sentence or paragraph depending on the level of semantic meaning conveyed.
因此，Gensim 需要以下输入：
sentences = [doc2vec.TaggedDocument(sentence, 'tag') for sentence in titlelist]
model.build_vocab(sentences)
显然，您可能希望根据句子设置不同的标签以获得有意义的向量。顺便问一下，您确定要以二进制模式读取 CSV 文件吗？