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README.md

@@ -45,12 +45,12 @@ tags:
 ---
 
 
-    SEA MADLAD is a subset of MADLAD-400 (Multilingual Audited Dataset: Low-resource And Document-level), which is a document-level multilingual dataset based on Common Crawl.
-    SEA MADLAD only filters the language of the "clean" subset, which covers 36 languages indigenous to SEA from 419 languages in total.
-    As a result, some of SEA lang codes aren't available in this version because those belongs to the languages whose decision was to "remove from its clean version" based on MADLAD auditing process.
-    MADLAD uses all snapshots of CommonCrawl available as of August 1, 2022.
-    The primary advantage of this dataset over similar datasets is that it is more multilingual, it is audited and more highly filtered, and it is document-level.
-    The main disadvantage is also its strength -- being more filtered, it may lack the recall needed for some applications.
+SEA MADLAD is a subset of MADLAD-400 (Multilingual Audited Dataset: Low-resource And Document-level), which is a document-level multilingual dataset based on Common Crawl.
+SEA MADLAD only filters the language of the "clean" subset, which covers 36 languages indigenous to SEA from 419 languages in total.
+As a result, some of SEA lang codes aren't available in this version because those belongs to the languages whose decision was to "remove from its clean version" based on MADLAD auditing process.
+MADLAD uses all snapshots of CommonCrawl available as of August 1, 2022.
+The primary advantage of this dataset over similar datasets is that it is more multilingual, it is audited and more highly filtered, and it is document-level.
+The main disadvantage is also its strength -- being more filtered, it may lack the recall needed for some applications.
 
 
 ## Languages
@@ -60,25 +60,25 @@ ace, akb, ban, bbc, bew, btx, ceb, fil, gor, hil, iba, ilo, ind, jav, kac, khm,
 ## Supported Tasks
 
 Self Supervised Pretraining
-    
+
 ## Dataset Usage
 ### Using `datasets` library
 ```
-    from datasets import load_dataset
-    dset = datasets.load_dataset("SEACrowd/sea_madlad", trust_remote_code=True)
+from datasets import load_dataset
+dset = datasets.load_dataset("SEACrowd/sea_madlad", trust_remote_code=True)
 ```
 ### Using `seacrowd` library
 ```import seacrowd as sc
 # Load the dataset using the default config
-    dset = sc.load_dataset("sea_madlad", schema="seacrowd")
+dset = sc.load_dataset("sea_madlad", schema="seacrowd")
 # Check all available subsets (config names) of the dataset
-    print(sc.available_config_names("sea_madlad"))
+print(sc.available_config_names("sea_madlad"))
 # 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