Gensim is an open-source library for unsupervised machine learning, natural language processing (NLP), and topic modeling. It specializes in efficiently processing large text corpora and extracting semantic meanings. Gensim is widely used for building scalable vector space models, topic models, and document similarity queries.

Features of Gensim:

• Efficient algorithms for topic modeling, document similarity, and word embeddings.
• Supports popular models like LDA (Latent Dirichlet Allocation), Word2Vec, Doc2Vec, and FastText.
• Built for scalability to handle large datasets in-memory and on disk.
• Fast, memory-efficient algorithms suitable for large text corpora.
• Works seamlessly with other libraries and tools like Numpy, Scipy, and Pandas.

To check Gensim version: python -c "import gensim; print(gensim.__version__)"

Disclaimer: Gensim is an independent open-source project maintained by the community. It is not affiliated with, endorsed by, or sponsored by any specific corporation or entity. It is provided "as is," without any warranty, express or implied. Users are responsible for understanding and complying with licensing terms, data policies, and regulations while using Gensim.