The repository contains some python scripts for training and inferring test document vectors using paragraph vectors or doc2vec.

Requirements

Python2: Pre-trained models and scripts all support Python2 only.
Gensim: Best to use my forked version of gensim; the latest gensim has changed its Doc2Vec methods a little and so would not load the pre-trained models.

Pre-Trained Doc2Vec Models

For reproducibility we also released the pre-trained word2vec skip-gram models on Wikipedia and AP News:

train_model.py: example python script to train some toy data
infer_test.py: example python script to infer test document vectors using trained model
toy_data: directory containing some toy train/test documents and pre-trained word embeddings

sample: this is the sub-sampling threshold to downsample frequent words; 10e-5 is usually good for DBOW, and 10e-6 for DMPV
hs: 1 turns on hierarchical sampling; this is rarely turned on as negative sampling is in general better
dm: 0 = DBOW; 1 = DMPV
negative: number of negative samples; 5 is a good value
dbow_words: 1 turns on updating of word embeddings. In DBOW, word embeddings are technically not learnt (only document embeddings are learnt). To learn word vectors, DBOW runs a step of skip-gram before the DBOW step to update the word embeddings. With dbow_words turned off, this means DBOW will randomly initialise word embeddings and keep them randomly initialised. This is rather bad in practice (as the model does not see relationships between words in the embedding space), so it should be turned on
dm_concat: 1 = concatenate input word vectors for DMPV; 0 = sum/average input word vectors. This setting is only used for DMPV since DBOW has only one input word
dm_mean: 1 = average input word vectors; 0 = sum input word vectors. Again, this setting is only used for DMPV. The original paragraph vector paper concatenates input word vectors for DMPV, and that's the setting we used in our paper
iter: number of iterations/epochs to train the model

Jey Han Lau and Timothy Baldwin (2016). An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation. In Proceedings of the 1st Workshop on Representation Learning for NLP, 2016.