Update README.md

README.md

@@ -6,6 +6,9 @@ language:
 - en
 pipeline_tag: token-classification
 ---
+
+# Cross-lingual argument mining
+
 This model is a fine-tuned version of mBERT for the argument mining task using AbstRCT data in English and Spanish.  
 The dataset consists of abstracts of 5 disease types for argument component detection and argument relation classification:
 
@@ -21,6 +24,13 @@ The results achieved for each test set:
  Glaucoma |   80.52  |  75.22   |   84.86     
    Mixed  |   81.69  |  75.06   |   88.57  
 
+You can find more information:
+
+ - 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527)
+ - Code: [https://github.com/ragerri/abstrct-projections/tree/final](https://github.com/ragerri/abstrct-projections/tree/final) 
+
+You can load the model as follows:
+
 ```python
 from transformers import AutoModelForSequenceClassification
 
@@ -28,3 +38,15 @@ model = AutoModelForSequenceClassification.from_pretrained('HiTZ/mbert-argument-
 ```   
 
 
+## Citation
+
+````bibtex
+@misc{yeginbergen2024crosslingual,
+      title={Cross-lingual Argument Mining in the Medical Domain}, 
+      author={Anar Yeginbergen and Rodrigo Agerri},
+      year={2024},
+      eprint={2301.10527},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+````