gensim | 易学教程

How to initialize a new word2vec model with pre-trained model weights?

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问题 I am using Gensim Library in python for using and training word2vector model. Recently, I was looking at initializing my model weights with some pre-trained word2vec model such as (GoogleNewDataset pretrained model). I have been struggling with it couple of weeks. Now, I just searched out that in gesim there is a function that can help me to initialize the weights of my model with pre-trained model weights. That is mentioned below: reset_from(other_model) Borrow shareable pre-built structures

Gensim: how to retrain doc2vec model using previous word2vec model

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问题 With Doc2Vec modelling, I have trained a model and saved following files: 1. model 2. model.docvecs.doctag_syn0.npy 3. model.syn0.npy 4. model.syn1.npy 5. model.syn1neg.npy However, I have a new way to label the documents and want to train the model again. since the word vectors already obtained from previous version. Is there any way to reuse that model (e.g., taking the previous w2v results as initial vectors for training)? Any one know how to do it? 回答1: I've figured out that, we can just

Convert a column in a dask dataframe to a TaggedDocument for Doc2Vec

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问题 Intro Currently I am trying to use dask in concert with gensim to do NLP document computation and I'm running into an issue when converting my corpus into a "TaggedDocument". Because I've tried so many different ways to wrangle this problem I'll list my attempts. Each attempt at dealing with this problem is met with slightly different woes. First some initial givens. The Data df.info() <class 'dask.dataframe.core.DataFrame'> Columns: 5 entries, claim_no to litigation dtypes: object(2), int64

Why the similarity beteween two bag-of-words in gensim.word2vec calculated this way?

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问题 def n_similarity(self, ws1, ws2): v1 = [self[word] for word in ws1] v2 = [self[word] for word in ws2] return dot(matutils.unitvec(array(v1).mean(axis=0)), matutils.unitvec(array(v2).mean(axis=0))) This is the code I excerpt from gensim.word2Vec, I know that two single words' similarity can be calculated by cosine distances, but what about two word sets? The code seems to use the mean of each wordvec and then calculated on the two mean vectors' cosine distance. I know few in word2vec, is there

How to convert gensim Word2Vec model to FastText model?

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问题 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? 回答1: FastText is able to create vectors for subword fragments

How is TF-IDF implemented in gensim tool in python?

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问题 From the documents which i found out from the net i figured out the expression used to determine the Term Frequency and Inverse Document frequency weights of terms in a corpus to be tf-idf(wt)= tf * log(|N|/d); I was going through the implementation of tf-idf mentioned in gensim. The example given in the documentation is >>> doc_bow = [(0, 1), (1, 1)] >>> print tfidf[doc_bow] # step 2 -- use the model to transform vectors [(0, 0.70710678), (1, 0.70710678)] Which apparently does not follow the

Using freebase vectors with gensim

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问题 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

'utf-8' decode error when loading a word2vec module

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问题 I have to use a word2vec module containing tons of Chinese characters. The module was trained by my coworkers using Java and is saved as a bin file. I installed gensim and tries to load the module, but following error occurred: In [1]: import gensim In [2]: model = gensim.models.Word2Vec.load_word2vec_format('/data5/momo-projects/user_interest_classification/code/word2vec/vectors_groups_1105.bin', binary=True) UnicodeDecodeError: 'utf-8' codec can't decode bytes in position 96-97: unexpected

Pipeline and GridSearch for Doc2Vec

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问题 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

5 词袋和词向量模型

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词袋模型（Bag of Words Model）词袋模型的概念先来看张图，从视觉上感受一下词袋模型的样子。词袋模型看起来像一个口袋把所有词都装进去，但却不完全如此。在自然语言处理和信息检索中作为一种简单假设，词袋模型把文本（段落或者文档）被看作是无序的词汇集合，忽略语法甚至是单词的顺序，把每一个单词都进行统计，同时计算每个单词出现的次数，常被用在文本分类中，如贝叶斯算法、LDA 和 LSA等。动手实战词袋模型（1）词袋模型本例中，我们自己动手写代码看看词袋模型是如何操作的。首先，引入 jieba 分词器、语料和停用词（标点符号集合，自己可以手动添加或者用一个文本字典代替）。 import jieba #定义停用词、标点符号 punctuation = ["，","。", "：", "；", "？"] #定义语料 content = ["机器学习带动人工智能飞速的发展。", "深度学习带动人工智能飞速的发展。", "机器学习和深度学习带动人工智能飞速的发展。" ] 接下来，我们先对语料进行分词操作，这里用到 lcut() 方法： #分词 segs_1 = [jieba.lcut(con) for con in content] print(segs_1) 得到分词后的结果如下： [['机器', '学习', '带动', '人工智能', '飞速', '的', '发展', '