pinecone
/

ConstBERT

@@ -1,199 +1,90 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- retrieval
+- constbert
+- colbert
+- multi-vector
+- embedding
+license: apache-2.0
+language:
+- en
+base_model:
+- google-bert/bert-base-uncased
 ---
+# ConstBERT
+ConstBERT (Constant-Space BERT) is a multi-vector retrieval model designed for efficient and effective passage retrieval. It modifies the ColBERT architecture by encoding documents into a fixed number of learned embeddings, rather than one embedding per token. This approach significantly reduces storage costs and can improve OS paging management due to fixed-size document representations, while retaining most of the original effectiveness of multi-vector models.
+## Details
+ConstBERT addresses the high storage cost associated with traditional multi-vector retrieval methods like ColBERT, where each token in a document collection is stored as a vector. Instead, ConstBERT proposes a learned pooling mechanism that projects the token-level embeddings of a document into a smaller, fixed number (`C`) of document-level embeddings. Each of these `C` embeddings captures distinct semantic facets of the document. This projection is achieved through an additional linear transformation layer learned end-to-end during training. The relevance score between a query and a document is then computed using a late interaction mechanism (MaxSim) over these `C` document embeddings and the query's token embeddings.
+This approach offers a trade-off between storage/computational efficiency and retrieval effectiveness, configurable by the choice of `C`. The paper demonstrates that ConstBERT can achieve performance comparable to ColBERT on benchmarks like MSMARCO and BEIR, with substantially smaller index sizes.
+This model has been trained to produce 32 vectors of size 128.
+### Model Sources
+For more details, please refer to our [official repository](https://github.com/pisa-engine/ConstBERT), [paper](https://www.pinecone.io/research/efficient-constant-space-multi-vector-retrieval/) and [blog](https://www.pinecone.io/blog/cascading-retrieval-with-multi-vector-representations/)!
 ### Direct Use
+ConstBERT is intended for semantic search and passage retrieval tasks. It can be used for:
+- First-stage retrieval in large document collections.
+- Reranking candidates produced by another retrieval system.
+The model produces fixed-size multi-vector representations for documents, which can be indexed efficiently. Queries are represented as sets of token embeddings.
+Example code:
+```python
+from transformers import AutoModel
+import numpy as np
+def max_sim(q: np.ndarray, d: np.ndarray) -> float:
+    # Ensure the dimensions are correct
+    assert q.ndim == 2, "Q must be a 2-dimensional array"
+    assert d.ndim == 2, "d must be a 2-dimensional array"
+    scores = np.dot(d, q.T)
+    max_scores = np.max(scores, axis=0)
+    return float(np.sum(max_scores))
+model = AutoModel.from_pretrained("pinecone/ConstBERT", trust_remote_code=True)
+# Example queries and documents
+queries = ["What is the capital of France?", "latest advancements in AI"]
+documents = [
+    "Paris is the capital and most populous city of France.",
+    "Artificial intelligence is rapidly evolving with new breakthroughs.",
+    "The Eiffel Tower is a famous landmark in Paris."
+]
+# Encode queries and documents
+query_embeddings = model.encode_queries(queries).numpy()
+document_embeddings = model.encode_documents(documents).numpy()
+max_sim(query_embeddings[0], document_embeddings[0]) > max_sim(query_embeddings[0], document_embeddings[1])
+# Returns: True
+```
+## Citation (BibTeX)
+```
+@inproceedings{macavaney2025constbert,
+author = {MacAvaney, Sean and Mallia, Antonio and Tonellotto, Nicola},
+title = {Efficient Constant-Space Multi-vector Retrieval},
+year = {2025},
+isbn = {978-3-031-88713-0},
+publisher = {Springer-Verlag},
+address = {Berlin, Heidelberg},
+url = {https://doi.org/10.1007/978-3-031-88714-7_22},
+doi = {10.1007/978-3-031-88714-7_22},
+booktitle = {Advances in Information Retrieval: 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6–10, 2025, Proceedings, Part III},
+pages = {237–245},
+numpages = {9},
+keywords = {Multi-Vector Retrieval, Efficiency, Dense Retrieval},
+location = {Lucca, Italy}
+}
+```