doc2vec

问题 I am a bit new to gensim and right now I am trying to solve the problem which involves using the doc2vec embeddings in keras. I wasn't able to find existing implementation of doc2vec in keras - as far as I see in all examples I found so far everyone just uses the gensim to get the document embeddings. Once I trained my doc2vec model in gensim I need to export embeddings weights from genim into keras somehow and it is not really clear on how to do that. I see that model.syn0 Supposedly gives

问题 I am a bit new to gensim and right now I am trying to solve the problem which involves using the doc2vec embeddings in keras. I wasn't able to find existing implementation of doc2vec in keras - as far as I see in all examples I found so far everyone just uses the gensim to get the document embeddings. Once I trained my doc2vec model in gensim I need to export embeddings weights from genim into keras somehow and it is not really clear on how to do that. I see that model.syn0 Supposedly gives

问题 Using Gensim's Doc2Vec how would I find the distance between a Doctag and an infer_vector() ? Many thanks 回答1: Doctag is the internal name for the keys to doc-vectors. The result of an infer_vector() operation is a vector. So as you've literally asked, these aren't comparable. You could ask a model for a known doc-vector, by its doc-tag key that was supplied during training, via model.docvecs[doctag] . That would be comparable to the result of an infer_vector() call. With two vectors in hand,

问题 As we know machines can't understand the text but it understands numbers so in NLP we convert text to some numeric representation and one of them is BOW representation. Here, my objective is to convert every document to some numeric representation and save it for future use. And I am following the below way to do that by converting text to BOW and saving it in a pickle file. My question is, whether we can do this in a better and reliable way? so that every document can be saved as some vector

问题 I am trying to build a document retrieval model that returns most documents ordered by their relevancy with respect to a query or a search string. For this I trained a doc2vec model using the Doc2Vec model in gensim. My dataset is in the form of a pandas dataset which has each document stored as a string on each line. This is the code I have so far import gensim, re import pandas as pd # TOKENIZER def tokenizer(input_string): return re.findall(r"[\w']+", input_string) # IMPORT DATA data = pd

来源： https://stackoverflow.com/questions/48703067/how-to-use-build-vocab-in-gensim

问题 I'm training a Doc2Vec model using the below code, where tagged_data is a list of TaggedDocument instances I set up before: max_epochs = 40 model = Doc2Vec(alpha=0.025, min_alpha=0.001) model.build_vocab(tagged_data) for epoch in range(max_epochs): print('iteration {0}'.format(epoch)) model.train(tagged_data, total_examples=model.corpus_count, epochs=model.iter) # decrease the learning rate model.alpha -= 0.001 # fix the learning rate, no decay model.min_alpha = model.alpha model.save("d2v

问题 I'm training a Doc2Vec model using the below code, where tagged_data is a list of TaggedDocument instances I set up before: max_epochs = 40 model = Doc2Vec(alpha=0.025, min_alpha=0.001) model.build_vocab(tagged_data) for epoch in range(max_epochs): print('iteration {0}'.format(epoch)) model.train(tagged_data, total_examples=model.corpus_count, epochs=model.iter) # decrease the learning rate model.alpha -= 0.001 # fix the learning rate, no decay model.min_alpha = model.alpha model.save("d2v

问题 I'm training a Doc2Vec model using the below code, where tagged_data is a list of TaggedDocument instances I set up before: max_epochs = 40 model = Doc2Vec(alpha=0.025, min_alpha=0.001) model.build_vocab(tagged_data) for epoch in range(max_epochs): print('iteration {0}'.format(epoch)) model.train(tagged_data, total_examples=model.corpus_count, epochs=model.iter) # decrease the learning rate model.alpha -= 0.001 # fix the learning rate, no decay model.min_alpha = model.alpha model.save("d2v

问题 I am trying to load my saved model from s3 using joblib import pandas as pd import numpy as np import json import subprocess import sqlalchemy from sklearn.externals import joblib ENV = 'dev' model_d2v = load_d2v('model_d2v_version_002', ENV) def load_d2v(fname, env): model_name = fname if env == 'dev': try: model=joblib.load(model_name) except: s3_base_path='s3://sd-flikku/datalake/doc2vec_model' path = s3_base_path+'/'+model_name command = "aws s3 cp {} {}".format(path,model_name).split()