The `gensim.models.doc2vec.Doc2Vec` module in Python is part of the Gensim library and provides an implementation of the Doc2Vec algorithm. Doc2Vec is an extension of the Word2Vec algorithm that learns distributed vector representations of texts.
Using `Doc2Vec`, we can train models that take in documents as input and map them to fixed-sized feature vectors, also known as document embeddings. These embeddings capture the semantic meaning of the documents and can be used for tasks such as document classification, similarity matching, and information retrieval.
The `Doc2Vec` module provides methods for training the model on a corpus of documents, as well as for inferring vector representations for new, unseen documents. It also supports several variations of the algorithm, including Distributed Memory (DM) and Distributed Bag of Words (DBOW), allowing users to experiment with different training approaches.
By leveraging the `gensim.models.doc2vec.Doc2Vec` module in Python, users can easily incorporate the powerful Doc2Vec algorithm into their natural language processing applications, enabling them to extract meaningful representations from textual data.
Python Doc2Vec - 60 examples found. These are the top rated real world Python examples of gensim.models.doc2vec.Doc2Vec extracted from open source projects. You can rate examples to help us improve the quality of examples.