Model.min_alpha = model.alpha # fix the learning rate, no decay Model.alpha -= 0.002 # decrease the learning rate Model = doc2vec.Doc2Vec(alpha=0.025, min_alpha=0.025) # use fixed learning rate # Comes from reducing the learning rate alpha during the training iteration, so here we use the latter. # In the official document of Gensim, the author pointed out that the best effect either comes from randomly arranging the input sentences, or # Print an example to see the shape of all_docs Sen =''.join(ch for ch in sen if ch not in exclude)Īll_docs.append(LabelDoc(sen.split(), tag)) # Complete, so remove this type of words to purify our input. # When a sentence is less than three words, we think its meaning is not LabelDoc = namedtuple('LabelDoc','words tags') # Demonstration, we are not interested in rigorous clauses, everyone can
#Paragraph vector code code#
# Mr.Wang will be divided into two sentences, but because the code is # It is worth noting that the basis is not very rigorous, such as English We use periods, question marks and exclamation marks as the basis for clauses. Reader = csv.reader(open("wikipedia.csv")) # Select wikipedia as input, and enter a part of wikipedia's csv document let's take a look at how the Gensim code is expressed: For detailed instructions, you can read the following link.
The purpose of the model is to add some longer sequence meanings to words while unsupervised classification of sentences, paragraphs or articles to obtain an effect similar to Word2Vec. The training method of the model is equivalent to Word2Vec. This new dimension exists in a space different from the word dimension, so everyone should be careful not to confuse the concept of word dimension and this new dimension. The meaning of the dimension is the meaning that people who need to use the model need to represent, that is, sentence classification, paragraph classification or article classification. From the perspective of the model's framework, its structure is basically the same as the CBOW or Skip-Gram model, but the biggest difference is that a new dimension equal to the word dimension is added as a sentence dimension, paragraph dimension or article dimension. The model starts from the CBOW and Skip-Gram models in Word2Vec. Before we start the code, it is necessary to introduce a little bit about this model. I will introduce the Gensim writing method of the model, and then, under the thinking of the Gensim model, we will try to use Tensorflow to write this model.
#Paragraph vector code how to#
Here, let's not discuss whether the effect is good or bad, just discuss how to build the model. At the same time, many places on the Internet also pointed out that the effect of this model is not as good as that of its previous model Word2Vec.
#Paragraph vector code movie#
At present, Mikolov and Bengio's latest paper Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews has introduced this model as a method for users to comment on film and television works. In this issue, let’s take a look at another model of Mikolov, the Paragraph Vector model. The last issue discussed the construction and comparison of Tensorflow and Gensim's Word2Vec model.
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