`Doc2Vec` is a Python implementation of the Document Embedding model from the `gensim` library. This model is designed to generate fixed-length numeric representations, known as embeddings, for documents such as sentences, paragraphs, or entire documents.
`Doc2Vec` uses a combination of Continuous Bag of Words (CBOW) and Skip-Gram models to learn the textual features of documents. It learns to represent words and documents in a dense vector space where semantically similar words and documents are close to each other.
To train the `Doc2Vec` model, a large corpus of labeled documents is required. The model can then be used to infer the document embeddings for new, unseen documents. These embeddings can be leveraged to perform various natural language processing tasks such as document classification, similarity analysis, and information retrieval.
The `Doc2Vec` model in `gensim` provides an easy-to-use interface to train and utilize document embeddings efficiently, making it a valuable tool in many text analysis applications.
Python Doc2Vec.Doc2Vec - 30 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.