Update README.md

README.md

@@ -12,24 +12,17 @@ base_model:
 
 # ModernBERT-embed-large + InSeNT
 
-This is a contextual model finetuned from [modernbert-embed-large](https://huggingface.co/answerdotai/ModernBERT-large) on the ConTEB training dataset. It was trained using the InSeNT training approach, detailed in the corresponding paper.
+[![arXiv](https://img.shields.io/badge/arXiv-2505.24782-b31b1b.svg?style=for-the-badge)](https://arxiv.org/abs/2505.24782)
+[![GitHub](https://img.shields.io/badge/Code_Repository-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/illuin-tech/contextual-embeddings)
+[![Hugging Face](https://img.shields.io/badge/ConTEB_HF_Page-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/illuin-conteb)
 
-## Model Details
+<img src="https://cdn-uploads.huggingface.co/production/uploads/60f2e021adf471cbdf8bb660/jq_zYRy23bOZ9qey3VY4v.png" width="800">
 
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Maximum Sequence Length:**  8192 tokens
-- **Output Dimensionality:** 768 dimensions
-- **Similarity Function:** cosine
-- **Training Dataset:**
-    - train
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
+This is a contextual model finetuned from [modernbert-embed-large](https://huggingface.co/answerdotai/ModernBERT-large) on the ConTEB training dataset. It was trained using the InSeNT training approach, detailed in the corresponding paper.
 
-- **Repository:** [Contextual Embeddings](https://github.com/illuin-tech/contextual-embeddings)
-- **Hugging Face:** [Contextual Embeddings](https://huggingface.co/illuin-conteb)
+> [!WARNING]
+> This experimental model stems from the paper [*Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings*](https://arxiv.org/abs/2505.24782).
+> While results are promising, we have seen regression on standard embedding tasks, and using it in production will probably require further work on extending the training set to improve robustness and OOD generalization.
 
 ## Usage
 
@@ -69,9 +62,26 @@ print("Length of first document embedding:", len(embeddings[0])) # 3
 print(f"Shape of first chunk embedding: {embeddings[0][0].shape}") # torch.Size([768])
 ```
 
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Maximum Sequence Length:**  8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** cosine
+- **Training Dataset:**
+    - train
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [Contextual Embeddings](https://github.com/illuin-tech/contextual-embeddings)
+- **Hugging Face:** [Contextual Embeddings](https://huggingface.co/illuin-conteb)
+
 ## Citation
 
-### BibTeX
 
 ```bibtex
 @misc{conti2025contextgoldgoldpassage,