Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- codon
+- cds
+- CDS
+- mRNA
+- RNA
+- Codon
+---
+# Dataset Card for Dataset Name
+
+<!-- Provide a quick summary of the dataset. -->
+
+This is the dataset that was used to train SynCodonLM. 
+It is made up of roughly 66 million CDS sequences (without introns), from roughyl 35,000 species.
+The github of our model SynCodonLM can be [found here].(https://github.com/Boehringer-Ingelheim/SynCodonLM)
+The Hugging Face of our learned model weights can be [found here] (https://huggingface.co/jheuschkel/SynCodonLM).
+
+
+
+## Citation
+If you use this work, please cite:
+```bibtex
+@article {Heuschkel2025.08.19.671089,
+	author = {Heuschkel, James and Kingsley, Laura and Pefaur, Noah and Nixon, Andrew and Cramer, Steven},
+	title = {Advancing Codon Language Modeling with Synonymous Codon Constrained Masking},
+	elocation-id = {2025.08.19.671089},
+	year = {2025},
+	doi = {10.1101/2025.08.19.671089},
+	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Codon language models offer a promising framework for modeling protein-coding DNA sequences, yet current approaches often conflate codon usage with amino acid semantics, limiting their ability to capture DNA-level biology. We introduce SynCodonLM, a codon language model that enforces a biologically grounded constraint: masked codons are only predicted from synonymous options, guided by the known protein sequence. This design disentangles codon-level from protein-level semantics, enabling the model to learn nucleotide-specific patterns. The constraint is implemented by masking non-synonymous codons from the prediction space prior to softmax. Unlike existing models, which cluster codons by amino acid identity, SynCodonLM clusters by nucleotide properties, revealing structure aligned with DNA-level biology. Furthermore, SynCodonLM outperforms existing models on 6 of 7 benchmarks sensitive to DNA-level features, including mRNA and protein expression. Our approach advances domain-specific representation learning and opens avenues for sequence design in synthetic biology, as well as deeper insights into diverse bioprocesses.Competing Interest StatementThe authors have declared no competing interest.},
+	URL = {https://www.biorxiv.org/content/early/2025/08/24/2025.08.19.671089},
+	eprint = {https://www.biorxiv.org/content/early/2025/08/24/2025.08.19.671089.full.pdf},
+	journal = {bioRxiv}
+}
+```