Word2Vec Examples: Understanding Word Embeddings 📚

Word2Vec is a powerful tool for generating word embeddings that capture semantic relationships between words. Below are key examples and use cases to help you get started:

🌱 Example 1: Semantic Similarity

Train a model to find similar words:

• Input: "king" - "man" + "woman"
• Output: "queen"
  This demonstrates how Word2Vec learns vector representations that reflect word relationships.

word2vec model

📊 Example 2: Sentiment Analysis

Use pre-trained embeddings for text classification:

• Model: GloVe
• Task: Predict positive/negative sentiment in reviews
• Accuracy: ~85% on benchmark datasets

🧩 Example 3: Language Model Pretraining

Embeddings can be used as input for deeper NLP tasks:

• Applications:
  • Text generation
  • Machine translation
  • Question answering
• Tools: HuggingFace Transformers

📖 How to Get Started

1. Install libraries:

   pip install gensim
   

2. Train a model:

   from gensim.models import Word2Vec
   model = Word2Vec([["hello", "world"], ["good", "morning"]], vector_size=100, epochs=10)
   

3. Explore more: Check our Word2Vec Tutorial for step-by-step guides.

word embeddings application

For visualizing embeddings, try TensorBoard to analyze training progress. 📈