gensim

I am trying to run a program using the Gensim library of the Python with the version 3.6. Whenever I ran the program, I came across these statements: C:\Python36\lib\site-packages\gensim-2.0.0-py3.6-win32.egg\gensim\utils.py:860: UserWarning: detected Windows; aliasing chunkize to chunkize_serial warnings.warn("detected Windows; aliasing chunkize to chunkize_serial") Slow version of gensim.models.doc2vec is being used I do not understand what is the meaning behind Slow version of gensim.models.doc2vec is being used . How the gensim is selecting the slow version and if I want the fastest

I currently have following script that helps to find the best model for a doc2vec model. It works like this: First train a few models based on given parameters and then test against a classifier. Finally, it outputs the best model and classifier (I hope). Data Example data (data.csv) can be downloaded here: https://pastebin.com/takYp6T8 Note that the data has a structure that should make an ideal classifier with 1.0 accuracy. Script import sys import os from time import time from operator import itemgetter import pickle import pandas as pd import numpy as np from argparse import ArgumentParser

Word2Vec from gensim 0.13.4.1 to update the word vectors on the fly does not work. model.build_vocab(sentences, update=False) works fine; however, model.build_vocab(sentences, update=True) does not. I am using this website to try and emulate what they have done; hence I use the following script at some point: model = gensim.models.Word2Vec() sentences = gensim.models.word2vec.LineSentence("./text8/text8") model.build_vocab(sentences, keep_raw_vocab=False, trim_rule=None, progress_per=10000, update=False) model.train(sentences) However while this runs with update=False , using update=True gives

I have a Word2Vec model which is trained in Gensim . How can I use it in Tensorflow for Word Embeddings . I don't want to train Embeddings from scratch in Tensorflow. Can someone tell me how to do it with some example code? Let's assume you have a dictionary and inverse_dict list, with index in list corresponding to most common words: vocab = {'hello': 0, 'world': 2, 'neural':1, 'networks':3} inv_dict = ['hello', 'neural', 'world', 'networks'] Notice how the inverse_dict index corresponds to the dictionary values. Now declare your embedding matrix and get the values: vocab_size = len(inv_dict)

I want to use a pre-trained word2vec model, but I don't know how to load it in python. This file is a MODEL file (703 MB). It can be downloaded here: http://devmount.github.io/GermanWordEmbeddings/ just for loading import gensim # Load pre-trained Word2Vec model. model = gensim.models.Word2Vec.load("modelName.model") now you can train the model as usual. also, if you want to be able to save it and retrain it multiple times, here's what you should do model.train(//insert proper parameters here//) """ If you don't plan to train the model any further, calling init_sims will make the model much

I am currently working on word2vec model using gensim in Python, and want to write a function that can help me find the antonyms and synonyms of a given word. For example: antonym("sad")="happy" synonym("upset")="enraged" Is there a way to do that in word2vec? In word2vec you can find analogies, the following way model = gensim.models.Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) model.most_similar(positive=['good', 'sad'], negative=['bad']) [(u'wonderful', 0.6414928436279297), (u'happy', 0.6154338121414185), (u'great', 0.5803680419921875), (u'nice', 0

word2vec 包括CBOW 和 Skip-gram，它的相关原理网上很多，这里就不多说了。简单来说，word2vec是自然语言中的字词转为计算机可以理解的稠密向量，是one-hot词汇表的降维表示，代表每个词的特征以及保持住了词汇间的关系。此处记录将中文词汇转为词向量的过程。 1. 下载中文语料 中文的语料可以从维基百科下载，这些语料库经常会更新，但都很全面。中文语料下载地址：（ https://dumps.wikimedia.org/zhwikisource/20180620/ ）。因为我只是想熟悉这个过程，就只下了一个比较小的包，只有两百多兆。 2. 解析语料包 从维基百科下载到的语料包是无法直接使用的，好在有人帮我们解决了这个问题。利用WikiExtractor抽取步骤1下载得到的语料原始包。WikiExtractor下载地址：（ https://github.com/attardi/wikiextractor ）。 打开cmd，输入以下命令解析维基语料，当然首先要把路径切换到你保存预料包和WikiExtractor的路径： python WikiExtractor.py -b 400M -o extracted zhwiki-latest-pages-articles.xml.bz2 400M 代表提取出来的单个文件最大为 400M，这时会产生目录extracted