Update README.md

README.md

@@ -191,9 +191,8 @@ size_categories:
 
 MultiFin – a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families.
 The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class.
-## Dataset Details
 
-### Dataset Description
+## Dataset Description
 
 The MULTIFIN dataset is a multilingual corpus, consisting of real-world article headlines covering 15
 languages. The corpus is annotated using hierarchical label structure, providing two classification tasks:
@@ -204,7 +203,7 @@ multi-class and multi-label classification.
 - **Language(s) (NLP):** English, Spanish, Polish, Hungarian, Greek, Danish, Turkish, Japanese, Swedish, Finnish, Norwegian, Russian, Italian, Hebrew, Icelandic.
 - **License:** [More Information Needed]
 
-### Dataset Sources [optional]
+## Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
 
@@ -226,6 +225,7 @@ multi-class and multi-label classification.
 
 **BibTeX:**
 
+```
 @inproceedings{jorgensen-etal-2023-multifin,
     title = "{M}ulti{F}in: A Dataset for Multilingual Financial {NLP}",
     author = "J{\o}rgensen, Rasmus  and
@@ -246,7 +246,7 @@ multi-class and multi-label classification.
     pages = "894--909",
     abstract = "Financial information is generated and distributed across the world, resulting in a vast amount of domain-specific multilingual data. Multilingual models adapted to the financial domain would ease deployment when an organization needs to work with multiple languages on a regular basis. For the development and evaluation of such models, there is a need for multilingual financial language processing datasets. We describe MultiFin {--} a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class. We develop our annotation schema based on a real-world application and annotate our dataset using both {`}label by native-speaker{'} and {`}translate-then-label{'} approaches. The evaluation of several popular multilingual models, e.g., mBERT, XLM-R, and mT5, show that although decent accuracy can be achieved in high-resource languages, there is substantial room for improvement in low-resource languages.",
 }
-
+```