Update README.md

README.md

@@ -6,23 +6,46 @@ tags:
 - dense
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- answerdotai/ModernBERT-base
 ---
 
-# SentenceTransformer
+<p align="center">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/kbQOAarw0eaApow3M9HIl.png" alt="LightOn" width="512">
+</p>
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+<h1 align="center">DenseOn-unsupervised</h1>
+
+<h3 align="center">Unsupervised contrastive pre-training checkpoint by LightOn</h3>
+
+<p align="center">
+<a href="https://huggingface.co/lightonai/DenseOn">DenseOn</a> |
+<a href="https://huggingface.co/lightonai/LateOn">LateOn</a> |
+<a href="https://github.com/lightonai/pylate">PyLate</a> |
+<a href="https://github.com/lightonai/fast-plaid">FastPLAID</a>
+</p>
+
+---
+
+**DenseOn-unsupervised** is an unsupervised contrastive pre-training checkpoint built on ModernBERT (149M parameters), trained by [LightOn](https://lighton.ai). It serves as the foundation for building [DenseOn](https://huggingface.co/lightonai/DenseOn), a dense (single-vector) retrieval model that encodes queries and documents independently using cosine similarity with `query:`/`document:` prefixes and CLS pooling.
+
+For the final dense retrieval model, use [DenseOn](https://huggingface.co/lightonai/DenseOn), which adds supervised fine-tuning with mined hard negatives on top of this checkpoint. See our [blog post](TODO) for full results and analysis.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Base model:** [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) (149M parameters)
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Pooling:** CLS token
+- **Prompts:** `query:` for queries, `document:` for documents
+- **Language:** English
+- **License:** Apache 2.0
 
 ### Model Sources
 
@@ -53,63 +76,37 @@ Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
 
-# Download from the 🤗 Hub
-model = SentenceTransformer("lightonai/LateOn-unsupervised")
+# Download from the Hub
+model = SentenceTransformer("lightonai/DenseOn-unsupervised")
+
 # Run inference
 queries = [
     "Which planet is known as the Red Planet?",
 ]
 documents = [
     "Venus is often called Earth's twin because of its similar size and proximity.",
-    'Mars, known for its reddish appearance, is often referred to as the Red Planet.',
-    'Saturn, famous for its rings, is sometimes mistaken for the Red Planet.',
+    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
+    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
 ]
-query_embeddings = model.encode_query(queries)
-document_embeddings = model.encode_document(documents)
+
+query_embeddings = model.encode(queries, prompt_name="query")
+document_embeddings = model.encode(documents, prompt_name="document")
 print(query_embeddings.shape, document_embeddings.shape)
 # [1, 768] [3, 768]
 
 # Get the similarity scores for the embeddings
 similarities = model.similarity(query_embeddings, document_embeddings)
 print(similarities)
-# tensor([[0.3464, 0.4823, 0.5147]])
 ```
 
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+## Related Models
 
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+| Model | Description | Link |
+|-------|-------------|------|
+| **DenseOn** | Supervised dense model | [lightonai/DenseOn](https://huggingface.co/lightonai/DenseOn) |
+| **DenseOn-unsupervised** | Pre-training-only checkpoint (this model) | [lightonai/DenseOn-unsupervised](https://huggingface.co/lightonai/DenseOn-unsupervised) |
+| **LateOn** | Supervised ColBERT model | [lightonai/LateOn](https://huggingface.co/lightonai/LateOn) |
+| **LateOn-unsupervised** | Pre-training-only checkpoint | [lightonai/LateOn-unsupervised](https://huggingface.co/lightonai/LateOn-unsupervised) |
 
 ## Training Details
 
@@ -128,11 +125,12 @@ You can finetune this model on your own dataset.
 
 ```bibtex
 @misc{sourty2025denseonlateon,
-  title={DenseOn and LateOn: State-of-the-Art LightOn Retrieval Models},
-  author={Sourty, Rapha{\"e}l and Chaffin, Antoine and Weller, Orion and Demoura, Paulo and Chatelain, Amelie},
+  title={DenseOn with LateOn: Open State-of-the-Art Single and Multi-Vector Models},
+  author={Sourty, Raphael and Chaffin, Antoine and Weller, Orion and Moura Junior, Paulo Roberto and Chatelain, Amelie},
   year={2026},
   howpublished={\url{https://huggingface.co/blog/lightonai/denseon-lateon}},
 }
+
 ```
 
 ```bibtex
@@ -161,6 +159,24 @@ You can finetune this model on your own dataset.
 }
 ```
 
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
 <!--
 ## Glossary