How to train a Doc2Vec model in Gensim?

To train a Doc2Vec model in Gensim, you can follow these steps:

1. Prepare your corpus of documents. This can be a list of sentences or paragraphs.
2. Tokenize the text and convert it to a list of tagged documents. Each document should be a list of words, and each document should have a unique tag.
3. Initialize and train the Doc2Vec model using the Doc2Vec class in Gensim. You should specify the size of the vector representations, the window size, the minimum count of words, and the number of epochs.
4. You can then use the trained model to infer vector representations of new documents or to find documents similar to a given query.

Here’s an example code snippet to train a Doc2Vec model in Gensim:

from gensim.models.doc2vec import Doc2Vec, TaggedDocument

tagged_data = [TaggedDocument(words=doc, tags=[str(i)]) for i, doc in enumerate(docs)]

model = Doc2Vec(vector_size=300, window=5, min_count=5, epochs=50)
model.build_vocab(tagged_data)

model.train(tagged_data, total_examples=model.corpus_count, epochs=model.epochs)

In this code, docs is the list of documents, and we first convert it to a list of tagged documents using the TaggedDocument class. We then initialize the Doc2Vec model with the specified parameters and build the vocabulary. Finally, we train the model on the tagged data. After training, you can use the infer_vector() method of the model to infer a vector representation of a new document, or the docvecs.most_similar() method to find documents most similar to a given query.