This library implements the Paragraph Vectors algorithm (also known as doc2vec) for creating dense vector representations of variable-length text documents. It minimizes the Negative Sampling objective as proposed by Mikolov et al., enabling efficient sparse updates during training. The implementation supports parallel batch generation on CPU while training on GPU, providing a flexible tool for unsupervised document embedding generation.