Bi-Encoder vs Cross-Encoder
Bi-encoders and cross-encoders are two different model architectures used in natural language tasks, especially when dealing with information retrieval, semantic matching, and similarity search.
First… what the heck is an Encoder 🤔?
Imagine an encoder as a machine that takes human text (e.g., “Where is my API key?”) and turns it into a long list of numbers (vectors). Why? Because computers don’t understand English, but they love numbers — especially long, weird arrays like:
[0.342, -1.24, 0.98, ..., 0.001]Encoders are basically:
🏭 Text → Math Soup → Useful Magic
⚔️ Bi-Encoder vs Cross-Encoder
Two Heroes in the NLP Universe
🥒 1. Bi-Encoder: “The Lazy Efficiency King”
How it works
- It encodes query and documents separately.
- No chatting between them.
- They meet only at the very end when calculating similarity.
Like this:
Query → Encoder → Vector A
Document → Encoder → Vector B
Similarity = “How much do A and B like each other?”Real-world analogy
Bi-encoder is like:
Online dating where you don’t talk to the person first. You swipe based on profile photos (vectors) only.
Fast? Yes. Accurate? Well… depends on your luck, champ.
When to use Bi-Encoders
✔️ You need speed, not perfection
✔️ You’re doing Search / Retrieval / RAG with millions of documents
✔️ You want embeddings stored in a Vector DB (Pinecone, Weaviate, Chroma, FAISS, etc.)
Example for my Noob Developer
- Encode all your text chunks once (offline).
- At query time encode the user query.
- Use cosine similarity / dot product.
query_vec = model.encode("How to fix server?")
doc_vecs = store.get_all_embeddings()
scores = cosine_similarity(query_vec, doc_vecs)🧙♂️ 2. Cross-Encoder: “The Genius But Slow Guy”
How it works
- Query + document go together into the model.
- The model reads both and decides how similar they are.
Input: "[QUERY] Where is my API key? [DOC] Check .env file"
→ Encoder → Single similarity scoreReal-world analogy
Cross-encoder is like:
Going on a full date before deciding if you like someone. Slow but far more accurate.
It actually reads both texts together, thinks, judges, overthinks like an ex.
When to use Cross-Encoders
✔️ You want accuracy, not speed
✔️ You’re reranking the top 20–200 results from a bi-encoder
✔️ Perfect for RAG reranking
✔️ Perfect for semantic matching tasks where precision is critical
Technical Example
Use bi-encoder to get top-50 first:
Vector Search → 50 resultsThen rerank those 50 using a cross-encoder:
score = cross_encoder.predict([query, doc_text])This gives much better final ranking.
🚀 Why use both? (The secret RAG combo)
Step 1:
Use Bi-Encoder to search your database FAST.
Step 2:
Send the top results to a Cross-Encoder to rerank ACCURATELY.
This is like:
Bi-Encoder brings 50 people to the party.Cross-Encoder picks the actually useful 3.
This is why most RAG pipelines look like:
User Query → Bi-Encoder → Top-K Chunks → Cross-Encoder → LLM🍕 Real-World Example: Pizza Recommendation
You ask:
“Best pizza place near me?”
Bi-encoder:
“Here are 40 pizza shops based on matching the word ‘pizza’. Good luck.”
Cross-encoder:
“Taste-wise, based on the query, these 3 match what you actually want.”
Which one feels more accurate? Exactly.
🧑💻 Technical Comparison Table
| Feature | Bi-Encoder | Cross-Encoder |
|---|---|---|
| Speed | ⚡⚡⚡ Fast | 🐌 Slow |
| Accuracy | ⚠️ Medium | ✅ High |
| Use-case | Vector Search, RAG | Reranking, Relevance scoring |
| Compute cost | Low | High |
| Input form | Encode separately | Encode together |
| Real-world analogy | Swiping | Dating |
🔗 Useful References
Bi-Encoder vs Cross-Encoder explained (SentenceTransformers docs) https://www.sbert.net/examples/applications/cross-encoder/README.html
HuggingFace Sentence Transformers https://huggingface.co/sentence-transformers
Reranking in RAG https://www.pinecone.io/learn/series/rag/rerankers/
Cross-Encoder model list https://huggingface.co/models?library=sentence-transformers&pipeline_tag=text-classification