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Embeddings 101: Unlocking Semantic Relationships in Text
- Embeddings revolutionized how machines understand language by addressing limitations in previous methods like one-hot encoding, bag-of-words, N-grams, and TF-IDF.
- Early embedding models like Word2Vec captured semantic relationships by training neural networks to predict words based on context or context based on words.
- Models like Word2Vec and GloVe offered single vectors for words, but contextual embeddings like BERT and GPT provided dynamic word representations based on context.
- Embeddings are numerical representations in a continuous vector space that capture semantic relationships, allowing machines to process language.
- Dimensions in embedding vectors represent abstract concepts that emerge during training.
- Embeddings enable semantic similarity, preserve context, allow mathematical operations, and work efficiently at scale.
- Dense vector representations in embeddings use fewer dimensions for efficiency and richer semantic content.
- Embeddings are built on distributional semantics principle and have remarkable mathematical properties for analogical reasoning.
- Transfer learning with pre-trained embeddings like BERT reduces data requirements for new applications.
- Methods like Mean Pooling, Max Pooling, Weighted Mean Pooling, and Last Token Pooling offer different approaches to create embeddings for various tasks.
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