Update README.md

README.md

@@ -25,6 +25,29 @@ The model forms the **query-side encoder** in a **dual-encoder (DPR-style)** set
 
 ---
 
+## 🎯 Usage
+
+Example with `transformers`:
+
+```python
+from transformers import AutoModel
+import torch
+
+# Load model
+model = AutoModel.from_pretrained("s8frbroy/talk2ref_query_talk_encoder")
+
+# Example input
+title = "Attention Is All You Need"
+year = 2017
+query_text = f"The following presentation is about the paper of the title: '{title}'. Published in {year}. " + \
+              "In this talk, we introduce the Transformer architecture and discuss its impact on sequence modeling."
+
+# Compute embedding
+with torch.no_grad():
+    embedding = model([query_text])
+
+print(embedding.shape)  # (1, hidden_dim)
+
 ## 🧩 Model Overview
 
 | Property | Description |
@@ -38,17 +61,18 @@ The model forms the **query-side encoder** in a **dual-encoder (DPR-style)** set
 
 ---
 
-## 🎯 Usage
 
-Example with `transformers`:
+## Citation
 
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-model = AutoModel.from_pretrained("s8frbroy/talk2ref_query_talk_encoder")
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+If you use this dataset, please cite the following paper:
 
-text = "In this talk, we present a new transformer model for scientific retrieval..."
-inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-embeddings = model(**inputs).last_hidden_state.mean(dim=1)  # or custom weighted 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}
+}