Update README.md

README.md

@@ -22,6 +22,36 @@ It serves as the **key-side encoder** in a **dual-encoder (DPR-style)** retrieva
 
 ---
 
+---
+
+## 🎯 Usage
+
+Example with `transformers`:
+
+```python
+from transformers import AutoModel
+import torch
+
+# Load model
+model = AutoModel.from_pretrained("s8frbroy/talk2ref_ref_key_cited_paper_encoder")
+
+# Example input
+title = "Attention Is All You Need"
+year = 2017
+abstract = "The Transformer model replaces recurrence with attention mechanisms for ..."
+
+
+# Build input in Talk2Ref format
+key_text = f"Title: {title}. Published in {year}. Abstract: {abstract}"
+
+# Compute embedding
+with torch.no_grad():
+    embedding = model([key_text])
+
+print(embedding.shape)  # (1, hidden_dim)
+
+```
+
 ## 🧩 Model Overview
 
 | Property | Description |
@@ -63,15 +93,18 @@ Before training, a **domain adaptation stage** aligned each talk with its own pa
 
 ---
 
-## 💡 Usage Example
 
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
+## Citation
 
-model = AutoModel.from_pretrained("s8frbroy/talk2ref_ref_key_cited_paper_encoder")
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+If you use this dataset, please cite the following paper:
 
-paper_text = "We propose a retrieval architecture for linking long spoken documents to their references..."
-inputs = tokenizer(paper_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-embeddings = model(**inputs).last_hidden_state.mean(dim=1)  # mean pooling
+```bibtex
+@misc{broy2025talk2refdatasetreferenceprediction,
+  title        = {Talk2Ref: A Dataset for Reference Prediction from Scientific Talks},
+  author       = {Frederik Broy and Maike Züfle and Jan Niehues},
+  year         = {2025},
+  eprint       = {2510.24478},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.CL},
+  url          = {https://arxiv.org/abs/2510.24478}
+}