Hi, @rambo-yuanbo! Maybe this will clear things up a bit:
I believe that multiple tags allows to build a document vector which contains not only one document's ID, but multiple tags IDs instead. Example in the SO question is quite descriptive.
Hi, @anotherbugmaster , still trying to understand the tags here. Reading the stockoverflow link, if one tag is provided over multiple training documents , like the 'action' tag in that post, then model.docvecs['action'] is some feature vector that is about/shared by all those documents with that tag? and thinking about the model, does the model try to predict the current word with the context and any one of the tag's vector, as the random gradient descent algorithm would like to pick, or with the multiple tag's vector all together? Thanks a lot !
a guess since the NN's structure is fixed, only a single tag's vector is selected with the context words' vectors to predict the current word ?
Let's say we have a set of documents with tags ((a, b), (b, c), (c, d)). Then the vectors will be ((1, 1, 0, 0), (0, 1, 1, 0), (0, 0, 1, 1))
Just like with a word context vector
hi, @anotherbugmaster , i don't think there's a single vector for the the list of tags of the document, unless there's a problem in gojomo's replay in that post you gave. The replay suggested that you can get a vector for each of the tags, like model.docvecs['UID_1'], model.docvecs['action']
When you train a classic doc2vec model, you give it word id's vector + document id's vector as an input, right? In the case of multiple tags your input vector is word id's vector + tag vector basically
@anotherbugmaster hi , I kinda started to got your point here. so for a list of tags on a document, say ['UID_1', 'action'], the unique tags each got a 0-1 encoding in the tag/doc-vector, so maybe a doc with ['UID_1', 'action'] got a vector like [1,1,0,0], and a doc with ['UID_1'] got a vector like [1,0,0,0], right?
another issue here, the doc id in the similarity tuple returned by model.docvecs.most_similar() is of type int64, and i got an error when i query my doc corpus with the returned tag, because my corpus is taged with simple python int (int32). So, First, how do I find about what data type is returned by a gensim function, besides by debugging and stepping into the gensim code to find out?
Hello! I would like to build a type of regression suite for Gensim models i.e. ensure that pairs or pairs of pairs of words have expected semantic relationships as in the original Google paper. So far I have found 'evaluate_word_pairs' and 'accuracy' for this purpose. Is there anything else that I could use? Thanks
Hi guys, when i write a trim_rule for the Doc2Vec class's constructor, I discard all the numbers (plain numbers and percents) and those words with length 1 (a consideration that might only apply to Chinese words). HOWEVER, i doubt is it a good practice to discard all numbers? What's the effect of discarding a word ? is it the same as if I cut away all those appearances of the word from all the corpus ? If that's right, do i have better not discard the number words, and maybe map all the number words to a single special word, say, "NUMBRE" ? and is there an built-in way to do that with Doc2Vec, instead of replacing all the number words in the corpus myself ?
@menshikh-iv
Hi everyone. I tried implementing the awesome new Soft Cosine Similarity model. I had a problem with the command
similarity_matrix = w2v_model.similarity_matrix(dictionary). The error I got was AttributeError: 'KeyedVectors' object has no attribute 'similarity_matrix'. I couldn't find references to similarity_matrix in the docs, but could be wrong. Anyone better versed can help me?
Hey; I have been trying to use doc2vec on a corpus of company names. The model seems to perform well generally, but fails for names of a single word. Now this may be an issue with the clustering algorithm (HDBSCAN) but I don't see how it could be. Is there a trick to training / infering when expecting one-word documents?
same with LSI
oh with LSI I see now I can use the [ ] method to get the vector embedding
you can use
model[['my', 'document']]
for
FastText - infer vector for each word and calculate vector of document as average of word-vectors
@ixxie Corpus in bag-of-words format, look at tutorial https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Topics_and_Transformations.ipynb
@ixxie
from gensim.corpora import Dictionary
from gensim.models import LsiModel
data = [["a", "a", "b"], ["c", "d"]]
dictionary = Dictionary(data)
corpus = [dictionary.doc2bow(doc) for doc in data]
model = LsiModel(corpus, id2word=dictionary)
list(model[corpus]) # [[(0, 2.236067977499789)], [(1, -1.4142135623730951)]]
I recently retrained Doc2Vec with a pretty large corpus around 30 million documents (about 1 page long on the average). I used 20 epochs and it took like 2.5-3 days. How many epochs are usually good enough? Thanks.