gensim

【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 am trying doc2vec for 600000 rows of sentences and my code is below: model = gensim.models.doc2vec.Doc2Vec(size= 100, min_count = 5,window=4, iter = 50, workers=cores) model.build_vocab(res) model.train(res, total_examples=model.corpus_count, epochs=model.iter) #len(res) = 663406 #length of unique words 15581 print(len(model.wv.vocab)) #length of doc vectors is 10 len(model.docvecs) # each of length 100 len(model.docvecs[1]) How do I interpret this result? why is the length of vector only 10 with each of size 100? when the length of 'res' is 663406, it does not make sense. I know something

I want to compute Cosine Similarity between LDA topics. In fact, gensim function .matutils.cossim can do it but I dont know which parameter (vector ) I can use for this function? Here is a snap of code : import numpy as np import lda from sklearn.feature_extraction.text import CountVectorizer cvectorizer = CountVectorizer(min_df=4, max_features=10000, stop_words='english') cvz = cvectorizer.fit_transform(tweet_texts_processed) n_topics = 8 n_iter = 500 lda_model = lda.LDA(n_topics=n_topics, n_iter=n_iter) X_topics = lda_model.fit_transform(cvz) n_top_words = 6 topic_summaries = [] topic_word =

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 am training a word2vec model from the tensorflow tutorial. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py After training I get the embedding matrix. I would like to save this and import it as a trained model in gensim. To load a model in gensim, the command is: model = Word2Vec.load_word2vec_format(fn, binary=True) But how do I generate the fn file from Tensorflow? Thanks One way to is save the file in the non-binary Word2Vec format, which essentially looks like this: num_words vector_size # this is the header label0 x00 x01 ...

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.

I am trying to use the freebase word embeddings released by Google, but I have a hard time getting the words from the freebase name. model = gensim.models.Word2Vec.load_word2vec_format('freebase-vectors-skipgram1000.bin',binary=True) model.vocab.keys()[:10] Out[22]: [u'/m/026tg5z', u'/m/018jz8', u'/m/04klsk', u'/m/08gd39', u'/m/0kt94', u'/m/05mtf0t', u'/m/05tjjb', u'/m/01m3vn', u'/m/0h7p35', u'/m/03ggvg3'] Does anyone know if it exist some kind of table to map the freebase representations into the words they represent ? Regards, Hedi Someone has actually done a nice thing for us all and mapped