gensim

准备工作 当我们下载了anaconda后，可以在命令窗口通过命令 conda install gensim 安装gensim gensim介绍 gensim是一款强大的自然语言处理工具，里面包括N多常见模型，我们体验一下： interfaces – Core gensim interfaces utils – Various utility functions matutils – Math utils corpora .bleicorpus – Corpus in Blei’s LDA-C format corpora .dictionary – Construct word<->id mappings corpora .hashdictionary – Construct word<->id mappings corpora .lowcorpus – Corpus in List-of-Words format corpora .mmcorpus – Corpus in Matrix Market format corpora .svmlightcorpus – Corpus in SVMlight format corpora .wikicorpus – Corpus from a Wikipedia dump corpora .textcorpus – Building

What can cause loss from model.get_latest_training_loss() increase on each epoch? Code, used for training: class EpochSaver(CallbackAny2Vec): '''Callback to save model after each epoch and show training parameters ''' def __init__(self, savedir): self.savedir = savedir self.epoch = 0 os.makedirs(self.savedir, exist_ok=True) def on_epoch_end(self, model): savepath = os.path.join(self.savedir, "model_neg{}_epoch.gz".format(self.epoch)) model.save(savepath) print( "Epoch saved: {}".format(self.epoch + 1), "Start next epoch ... ", sep="\n" ) if os.path.isfile(os.path.join(self.savedir, "model_neg{

首先是读取数据集，并将csv中ExtractedBodyText为空的给去除掉 import pandas as pd import re import os dir_path=os.path.dirname(os.path.abspath(__file__)) data_path=dir_path+"/Database/HillaryEmails.csv" df=pd.read_csv(data_path) df=df[['Id','ExtractedBodyText']].dropna() 对于这些邮件信息，并不是所有的词都是有意义的，也就是先要去除掉一些噪声数据： def clean_email_text(text): text = text.replace('\n'," ") #新行，我们是不需要的 text = re.sub(r"-", " ", text) #把 "-" 的两个单词，分开。（比如：july-edu ==> july edu） text = re.sub(r"\d+/\d+/\d+", "", text) #日期，对主体模型没什么意义 text = re.sub(r"[0-2]?[0-9]:[0-6][0-9]", "", text) #时间，没意义 text = re.sub(r"[\w]+@[\.\w]+", "", text) #邮件地址，没意义

I have a gensim Word2Vec model computed in Python 2 like that: from gensim.models import Word2Vec from gensim.models.word2vec import LineSentence model = Word2Vec(LineSentence('enwiki.txt'), size=100, window=5, min_count=5, workers=15) model.save('w2v.model') However, I need to use it in Python 3. If I try to load it, import gensim from gensim.models import Word2Vec model = Word2Vec.load('w2v.model') it results in an error: UnicodeDecodeError: 'ascii' codec can't decode byte 0xf9 in position 0: ordinal not in range(128) I suppose the problem is in differences in encoding between Python2 and

I was following the instructions on this link (" http://radimrehurek.com/2014/03/tutorial-on-mallet-in-python/ "), however I came across an error when I tried to train the model: model = models.LdaMallet(mallet_path, corpus, num_topics =10, id2word = corpus.dictionary) IOError: [Errno 2] No such file or directory: 'c:\\users\\brlu\\appdata\\local\\temp\\c6a13a_state.mallet.gz' Please share any thoughts you might have. Thanks. This can happen for two reasons: 1. You have space in your mallet path. 2. There is no MALLET_HOME environment variable. Make sure that mallet properly works from command

I have been struggling to understand the use of size parameter in the gensim.models.Word2Vec From the Gensim documentation, size is the dimensionality of the vector. Now, as far as my knowledge goes, word2vec creates a vector of the probability of closeness with the other words in the sentence for each word. So, suppose if my vocab size is 30 then how does it create a vector with the dimension greater than 30? Can anyone please brief me on the optimal value of Word2Vec size? Thank you. size is, as you note, the dimensionality of the vector. Word2Vec needs large, varied text examples to create