# Embedding models


📂 model-architectures

## MODEL ARCHITECTURES

# Embedding models

Map text, images, audio, users, or items into vectors whose geometry is trained to preserve a useful notion of similarity.

## MENTAL MODEL

A learned coordinate system. Nearness means “similar under the training objective,” not universally equivalent or factually related.

## DATA FLOW

- Input item

- Encoder

- Pooling or projection

- Fixed-length vector

- Similarity search, clustering, or classifier

## How it trains

Contrastive, metric-learning, classification, or paired-data objectives pull useful matches together and push negatives apart. The negative-sampling strategy and domain data define what similarity means.

## How inference runs

Each item is encoded in one forward pass. Stored vectors enable fast approximate-nearest-neighbor search; a cross-encoder can rerank the small candidate set for finer interaction.

## Strengths

- •
Efficient semantic retrieval over large collections

- •
Reusable features for clustering, routing, recommendations, and deduplication

- •
Candidate vectors can be computed and indexed ahead of time

## Trade-offs

- •
A single vector compresses away token-level detail

- •
Similarity degrades under domain, language, or time drift

- •
Scores are model- and index-specific, not calibrated probabilities

## Use it when

- You need semantic candidate retrieval

- The corpus is too large for pairwise scoring

- You can evaluate recall on production-shaped queries

## Avoid or challenge it when

- Exact lexical matching is the only requirement

- You need a generated response rather than a representation

- A similarity threshold would be deployed without calibration

## Illustrative published families

- • BERT-derived sentence encoders

- • The separate text and image towers used by CLIP

## Commonly combines with

## PRIMARY REFERENCES

## Model Architectures

## Model Architectures