What are Embedding Models?
Embedding models transform data (text, images, audio) into dense numerical vectors that capture semantic meaning. These vector representations enable similarity comparisons, clustering, and serve as inputs for downstream machine learning tasks.
How Embeddings Work
Text → Vector
"artificial intelligence" → [0.12, -0.45, 0.89, ...]
Similar concepts have similar vectors.
Embedding Dimensions
| Model | Dimensions |
|---|---|
| Word2Vec | 300 |
| BERT | 768 |
| OpenAI Ada | 1,536 |
| Cohere | 4,096 |
Types of Embeddings
Word Embeddings
- Word2Vec
- GloVe
- FastText
Sentence Embeddings
- BERT/RoBERTa
- Sentence-BERT
- Universal Sentence Encoder
Multimodal Embeddings
- CLIP (text + image)
- ImageBind
Popular Models
OpenAI
- text-embedding-ada-002
- text-embedding-3-small/large
Open Source
- all-MiniLM-L6-v2
- bge-large
- E5-large
Cloud Providers
- Cohere Embed
- Google Vertex AI
- AWS Titan
Use Cases
- Semantic search
- RAG systems
- Recommendation engines
- Clustering and classification
- Anomaly detection
- Duplicate detection
Best Practices
- Choose appropriate dimensions
- Normalize vectors
- Consider domain-specific models
- Benchmark for your use case
- Cache embeddings