Deconstructing the text embedding models — Kacper Łukawski

Text embedding models convert text input into vectors, with similar texts producing similar vector representations

Tokenization is a critical but often overlooked component that impacts model performance:

• Splits text into subword tokens using algorithms like WordPiece
• Fixed vocabulary size limits representation capability
• Handling of non-English text, numbers, and special characters can be problematic
• Unknown tokens significantly impact model quality

Input token embeddings are context-independent and learned during training:

• Each token has a fixed embedding regardless of surrounding context
• Similar words/concepts cluster together in the embedding space
• Model performance heavily depends on training data coverage

Common challenges with embedding models:

• Poor handling of typos and misspellings
• Limited support for dates, prices, and numeric data
• Struggles with multilingual content
• Fixed training cutoff means newer terms/concepts aren’t represented

Practical recommendations:

• Consider fine-tuning both model and tokenizer for domain-specific use
• Use hybrid search approaches combining semantic and exact matching
• Monitor unknown token rates in both documents and queries
• Evaluate tokenizer performance on your specific data before deployment

Model architecture details:

• Uses attention mechanisms to create context-aware representations
• Typically encoder-only transformers
• Final vector created through pooling of token embeddings
• Positional encodings capture sequence order information