Which library contains the Word2Vec model in Python

The Word2Vec model in Python is commonly implemented in the `gensim` library. `gensim` is a popular open-source Python library for topic modeling, document similarity analysis, and other natural language processing (NLP) tasks. It provides an implementation of Word2Vec as well as other algorithms for word embeddings and text analysis.

To use Word2Vec in Python using the `gensim` library, you can follow these steps:

1. Install the `gensim` library if you haven't already:
   ```
   pip install gensim
   ```

2. Import the `Word2Vec` class from the `gensim.models` module:
   ```python
   from gensim.models import Word2Vec
   ```

3. Create and train a Word2Vec model using your text data:
   ```python
   # Example text data (list of tokenized sentences)
   sentences = [["this", "is", "an", "example", "sentence"],
                ["another", "example", "sentence"],
                ...]

   # Train Word2Vec model
   model = Word2Vec(sentences, min_count=1)  # Example parameters; adjust as needed
   ```

4. Use the trained Word2Vec model to obtain word embeddings:
   ```python
   # Get the vector representation of a word
   vector = model.wv['example']

   # Find similar words
   similar_words = model.wv.most_similar('example')
   ```

The `gensim` library provides additional functionalities for working with Word2Vec models, such as saving/loading models, fine-tuning, and visualization. It's a versatile tool for implementing and experimenting with word embeddings in Python.