text2vec | Fast vectorization , topic modeling | Natural Language Processing library

I have been tasked with putting a document vector model into production. I am an R user, and so my original model is in R. One of the avenues we have is to recreate the code and the models in Python.

I am confused by the Gensim implementation of Doc2vec.

The process that works in R goes like this:

Offline

• Word vectors are trained using the functions in the text2vec package, namely GloVe or GlobalVectors, on a large corpus This gives me a large Word Vector text file.

• Before the ML step takes place, the Doc2Vec function from the TextTinyR library is used to turn each piece of text from a smaller, more specific training corpus into a vector. This is not a machine learning step. No model is trained. The Doc2Vec function effectively aggregates the word vectors in the sentence, in the same sense that finding the sum or mean of vectors does, but in a way that preserves information about word order.

• Various models are then trained on these smaller text corpuses.

Online

• The new text is converted to Document Vectors using the pretrained word vectors.
• The Document Vectors are fed into the pretrained model to obtain the output classification.

The example code I have found for Gensim appears to be a radical departure from this.

It appears in gensim that Doc vectors are a separate class of model from word vectors that you can train. It seems in some cases, the word vectors and doc vectors are all trained at once. Here are some examples from tutorials and stackoverflow answers:

https://medium.com/@mishra.thedeepak/doc2vec-simple-implementation-example-df2afbbfbad5

How to use Gensim doc2vec with pre-trained word vectors?

How to load pre-trained model with in gensim and train doc2vec with it?

gensim(1.0.1) Doc2Vec with google pretrained vectors

So my questions are these:

Is the gensim implementation of Doc2Vec fundamentally different from the TextTinyR implementation?

Or is the gensim doc2vec model basically just encapsulating the word2vec model and the doc2vec process into a single object?

Is there anything else I'm missing about the process?

I set my model and data to the same device,

Here's the situation, one whose solution seemed to be simple at first, but that has turned out to be more complicated than I expected.

I have an R data frame with three columns: an ID, a column with texts (reviews), and one with numeric values which I want to predict based on the text.

I have already done some preprocessing on the text column, so it is free of punctuation, in lower case, and ready to be tokenized and turned into a matrix so I can train a model on it. The problem is I can't figure out how to remove the stop words from that text.

Here's what I am trying to do with the text2vec package. I was planning on doing the stop-word removal before this chunk at first. But anywhere will do.

I am using {text2vec} word embeddings to build a dictionary of similar terms pertaining to a certain semantic category.

Is it OK to compound some tokens in the corpus, but not all? For example, I want to calculate terms similar to “future generation” or “rising generation”, but these collocations occur as separate terms in the original corpus of course. I am wondering if it is bad practice to gsub "rising generation" --> "rising_generation", without compounding all other terms that occur frequently together such as “climate change.”

Thanks!

I am trying to run through text2vec's example on this page. However, whenever I try to see what the vocab_vectorizer function returned, it's just an output of the function itself. In all my years of R coding, I've never seen this before, but it also feels funky enough to extend beyond just this function. Any pointers?

I am trying to implement one of the solutions to the question about How to align two GloVe models in text2vec?. I don't understand what are the proper values for input at GlobalVectors$new(..., init = list(w_i, w_j). How do I ensure the values for w_i and w_j are correct?

Here's a minimal reproducible example. First, prepare some corpora to compare, taken from the quanteda tutorial. I am using dfm_match(all_words) to try and ensure all words are present in each set, but this doesn't seem to have the desired effect.

Here's what I am doing:

1. Loading sparse matrix from a file.
2. Extracting indices(col, row) which have the values in this sparse matrix.
3. Use these indices and the values for further computation.

This works fine when I am executing the steps on R command prompt. But when its done inside a function of a package, step 2 throws the following error:

I'm building docker containers with R, with lines like: