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README.md

@@ -19,10 +19,10 @@ tags:
 Universal Dependencies (UD) is a project that is developing cross-linguistically consistent treebank annotation
  for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and 
  parsing research from a language typology perspective. The annotation scheme is based on an evolution of (universal)
-   Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags 
-   (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). 
-   The general philosophy is to provide a universal inventory of categories and guidelines to facilitate consistent
-     annotation of similar constructions across languages, while allowing language-specific extensions when necessary.
+Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags 
+(Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). 
+The general philosophy is to provide a universal inventory of categories and guidelines to facilitate consistent
+annotation of similar constructions across languages, while allowing language-specific extensions when necessary.
 
 
 ## Languages
@@ -32,25 +32,25 @@ ind, vie, tgl
 ## Supported Tasks
 
 Pos Tagging, Dependency Parsing, Machine Translation
-    
+
 ## Dataset Usage
 ### Using `datasets` library
 ```
-    from datasets import load_dataset
-    dset = datasets.load_dataset("SEACrowd/ud", trust_remote_code=True)
+from datasets import load_dataset
+dset = datasets.load_dataset("SEACrowd/ud", trust_remote_code=True)
 ```
 ### Using `seacrowd` library
 ```import seacrowd as sc
 # Load the dataset using the default config
-    dset = sc.load_dataset("ud", schema="seacrowd")
+dset = sc.load_dataset("ud", schema="seacrowd")
 # Check all available subsets (config names) of the dataset
-    print(sc.available_config_names("ud"))
+print(sc.available_config_names("ud"))
 # Load the dataset using a specific config
-    dset = sc.load_dataset_by_config_name(config_name="<config_name>")
+dset = sc.load_dataset_by_config_name(config_name="<config_name>")
 ```
-    
-    More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
-    
+
+More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
+
 
 ## Dataset Homepage