README.md

---
license: apache-2.0
language:
- en
base_model:
- intfloat/e5-base-unsupervised
pipeline_tag: sentence-similarity
---


# cadet-embed-base-v1

**cadet-embed-base-v1** is a BERT-base embedding model fine-tuned **from `intfloat/e5-base-unsupervised`** with  

* **cross-encoder listwise distillation** (teachers: `RankT5-3B` and `BAAI/bge-reranker-v2.5-gemma2-lightweight`)  
* **purely synthetic queries** (Llama-3.1 8B generated: questions, claims, titles, keywords, zero-shot & few-shot web queries) over 400k passages total from MSMARCO, DBPedia and Wikipedia corpora.  

The result: highly effective BERT-base retrieval.


We provide our training code and scripts to generate synthetic queries at https://github.com/manveertamber/cadet-dense-retrieval. 

---

## Quick start
```python
from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("manveertamber/cadet-embed-base-v1")

query = "query: capital of France"

passages = [
    "passage: Paris is the capital and largest city of France.",
    "passage: Berlin is known for its vibrant art scene.",
    "passage: The Eiffel Tower is located in Paris, France."
]

# Encode
q_emb   = model.encode(query,    normalize_embeddings=True)
p_embs  = model.encode(passages, normalize_embeddings=True)     # shape (n_passages, dim)

scores = np.dot(p_embs, q_emb)                                  # shape (n_passages,)

# Rank passages by score
for passage, score in sorted(zip(passages, scores), key=lambda x: x[1], reverse=True):
    print(f"{score:.3f}\t{passage}")


```



If you use this model, please cite:

```
@article{tamber2025conventionalcontrastivelearningfalls,
  title={Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data}, 
  author={Manveer Singh Tamber and Suleman Kazi and Vivek Sourabh and Jimmy Lin},
  journal={arXiv:2505.19274},
  year={2025}
}
```