Doc2Vec is an unsupervised algorithm used to convert documents in vectors ("dense embeddings"). It is based on the "Paragraph Vector" paper and implemented in the Gensim Python library and elsewhere. The algorithm can work in either a "Distributed Bag Of Words" mode (PV-DBOW, which works somewhat analogously to skip-gram mode in Word2Vec) or a "Distributed Memory" mode (PV-DM, which is more analogous to CBOW mode in Word2Vec.)
Questions tagged [doc2vec]
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gensim/docs/notebooks/doc2vec-lee.ipynb results are not repeatable
According to this github tutorial: gensim/docs/notebooks/doc2vec-lee.ipynb
I am supposed to be getting about 96% accuracy.
Here is the code using gensim 0.13.4 on jupyter 4.3.1 notebook all from Anaconda Navigator.
import gensim
import os
import…
yxs8495
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