Update README.md

README.md

@@ -1,10 +1,8 @@
 ---
 datasets:
-- hgissbkh/BERTJudge-Dataset-Train
+- hgissbkh/BERTJudge-Dataset
 language:
 - en
-metrics:
-- accuracy
 base_model:
 - EuroBERT/EuroBERT-210m
 ---
@@ -13,7 +11,8 @@ base_model:
 BERT-as-a-Judge is a family of encoder-based models designed for efficient, reference-based evaluation of LLM outputs. Moving beyond rigid lexical extraction and matching, these models evaluate semantic correctness, accommodating variations in phrasing and formatting while using only a fraction of the computational resources required by LLM-as-a-Judge approaches.
 
 ## Model Summary
-- **Paper:** [BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation](URL_TO_PAPER)
+- **Paper:** [BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation](https://arxiv.org/abs/2604.09497)
+- **Code:** [https://github.com/artefactory/BERT-as-a-Judge](https://github.com/artefactory/BERT-as-a-Judge)
 - **Model Type:** Encoder-based Judge (EuroBERT-210m backbone)
 - **Language:** English
 
@@ -55,4 +54,21 @@ with torch.no_grad():
     logits = model(**inputs).logits
     prediction = torch.argmax(logits, dim=-1)
 
-print("Correct" if prediction.item() == 1 else "Incorrect")
+print("Correct" if prediction.item() == 1 else "Incorrect")
+```
+
+## Citation
+
+If you find this model useful for your research, please consider citing:
+
+```bibtex
+@article{gisserotboukhlef2026bertasajudgerobustalternativelexical,
+  title={BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation},
+  author={Gisserot-Boukhlef, Hippolyte and Boizard, Nicolas and Malherbe, Emmanuel and Hudelot, C{\'e}line and Colombo, Pierre},
+  year={2026},
+  eprint={2604.09497},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2604.09497}
+}
+```