word2vec

import gensim, logging, os logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) import nltk corpus = nltk.corpus.brown.sents() fname = 'brown_skipgram.model' if os.path.exists(fname): # load the file if it has already been trained, to save repeating the slow training step below model = gensim.models.Word2Vec.load(fname) else: # can take a few minutes, grab a cuppa model = gensim.models.Word2Vec(corpus, size=100, min_count=5, workers=2, iter=50) model.save(fname) words = "woman women man girl boy green blue".split() for w1 in words: for w2 in words: print

I'm running a VM on Google Cloud Platform using Jupyter notebook with word2vec models. I have the following code snippet: from gensim.models import Word2Vec amazon_word2vec = Word2Vec(model, min_count=1, size=100) And it results in the error: AttributeError: module 'boto' has no attribute 'plugin' What is the solution to the above problem? pip install google-compute-engine install google compute engine and restart your vm and it works fine. 来源： https://stackoverflow.com/questions/52414249/attributeerror-module-boto-has-no-attribute-plugin

【一】整体流程综述 gensim底层封装了Google的Word2Vec的c接口，借此实现了word2vec。使用gensim接口非常方便，整体流程如下： 1. 数据预处理（分词后的数据） 2. 数据读取 3.模型定义与训练 4.模型保存与加载 5.模型使用（相似度计算，词向量获取） 【二】gensim提供的word2vec主要功能 【三】gensim接口使用示例 1. 使用jieba进行分词。 文本数据：《人民的名义》的小说原文作为语料 百度云盘：https://pan.baidu.com/s/1ggA4QwN # -*- coding:utf-8 -*- import jieba def preprocess_in_the_name_of_people(): with open("in_the_name_of_people.txt",mode='rb') as f: doc = f.read() doc_cut = jieba.cut(doc) result = ' '.join(doc_cut) result = result.encode('utf-8') with open("in_the_name_of_people_cut.txt",mode='wb') as f2: f2.write(result) 2. 使用原始text8.zip进行词向量训练 from

I have a Word2Vec model which was trained on a huge corpus. While using this model for Neural network application I came across quite a few "Out of Vocabulary" words. Now I need to find word embeddings for these "Out of Vocabulary" words. So I did some googling and found that Facebook has recently released a FastText library for this. Now my question is how can I convert my existing word2vec model or Keyedvectors to FastText model? FastText is able to create vectors for subword fragments by including those fragments in the initial training, from the original corpus. Then, when encountering an

I have a list of ~10 million sentences, where each of them contains up to 70 words. I'm running gensim word2vec on every word, and then taking the simple average of each sentence. The problem is that I use min_count=1000, so a lot of words are not in the vocab. To solve that, I intersect the vocab array (that contains about 10000 words) with every sentence, and if there's at least one element left in that intersection, it returns its the simple average, otherwise, it returns a vector of zeros. The issue is that calculating every average takes a very long time when I run it on the whole dataset

I am new to doc2vec. I was initially trying to understand doc2vec and mentioned below is my code that uses Gensim. As I want I get a trained model and document vectors for the two documents. However, I would like to know the benefits of retraining the model in several epoches and how to do it in Gensim? Can we do it using iter or alpha parameter or do we have to train it in a seperate for loop ? Please let me know how I should change the following code to train the model for 20 epoches. Also, I am interested in knowing is the multiple training iterations are needed for word2vec model as well.