Update README.md

README.md

@@ -1,3 +1,62 @@
----
-license: apache-2.0
----
+---
+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.
+
+---
+
+## 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 (embeddings are already L2-normalised by default)
+q_emb   = model.encode(query,    normalize_embeddings=True)
+p_embs  = model.encode(passages, normalize_embeddings=True)     # shape (n_passages, dim)
+
+# Cosine similarity = dot product of normalised vectors
+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{tamber2025teaching,
+  title={Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation},
+  author={Tamber, Manveer Singh and Kazi, Suleman and Sourabh, Vivek and Lin, Jimmy},
+  journal={arXiv preprint arXiv:2502.19712},
+  year={2025}
+}
+```