Word2Vec Explained: The Moment Words Became Relations

After the training phase in Word2Vec, the model's primary purpose shifts from generating predictions for context words during training to providing pre-trained vector representations of individual words. These vectors capture semantic and syntactic relationships between words based on their contexts within the training corpus.

Here’s a detailed breakdown of how inference works:

1. Pre-Trained Embeddings

• After training, the first weight matrix (input-to-hidden) contains learned word embeddings for each word in the vocabulary.
• Each row of this matrix represents an embedding vector for a specific word.

2. Querying Word Vectors

• During inference, users typically query these pre-trained vectors rather than generating text.
• For example, if you want to get the vector representation of "cat", you simply look up its corresponding row in the first weight matrix.

3. Similarity and Analogy Tasks

• Word2Vec embeddings are often used for tasks like word similarity and analogy questions.
  • Word Similarity: Measure cosine similarity between vectors to find words that have similar meanings or contexts.

    python
     
    1from sklearn.metrics.pairwise import cosine_similarity
    2
    3cat_vector = model.wv['cat']
    4dog

Read the full article at Towards AI - Medium

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