I have around 20k documents with 60 - 150 words. Out of these 20K documents, there are 400 documents for which the similar document are known. These 400 documents serve as my test data.

At present I am removing those 400 documents and using remaining 19600 documents for training the doc2vec. Then I extract the vectors of train and test data. Now for each test data document, I find it's cosine distance with all the 19600 train documents and select the top 5 with least cosine distance. If the similar document marked is present in these top 5 then take it to be accurate. Accuracy% = No. of Accurate records / Total number of Records.

The other way I find similar documents is by using the doc2Vec most similiar method. Then calculate accuracy using the above formula.

The above two accuracy doesn't match. With each epoch one increases other decreases.

I am using the code given here: https://medium.com/scaleabout/a-gentle-introduction-to-doc2vec-db3e8c0cce5e. For training the Doc2Vec.

I would like to know how to tune the hyperparameters so that I can get making accuracy by using above-mentioned formula. Should I use cosine distance to find the most similar documents or shall I use the gensim's most similar function?


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