Update README.md

README.md

@@ -10,7 +10,7 @@ pipeline_tag: text-generation
 
 Natural products represent a large pool of bioactive compounds of high interest in drug-discovery. However, these relationships are sparsely distributed across organisms and a growing part
 of the literature remains unannotated. This volume necessitates the development of a machine assistant to boost the completion of existing resources. Framing the task as an
-end-to-end Relation Extraction (RE), we propose a BioGPT model fined-tuned on synthetic data. See details about this procedure in the join [article](url/rXiv).
+end-to-end Relation Extraction (RE), we propose a BioGPT model fined-tuned on synthetic data. See details about this procedure in the join [article](https://arxiv.org/abs/2311.06364).
 
 The model is a derived from [microsoft/BioGPT-Large](hhttps://huggingface.co/microsoft/BioGPT-Large) and was trained on a synthetic dataset *Diversity-synt* available on zenodo.