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README.md

@@ -11,14 +11,14 @@ tags:
 - question-answering
 ---
 
-    TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
-    The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
-    expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
-    in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
-    information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
-    don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language
-    without the use of translation (unlike MLQA and XQuAD).
-    
+TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
+The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
+expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
+in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
+information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
+don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language
+without the use of translation (unlike MLQA and XQuAD).
+
 
 ## Languages
 
@@ -27,25 +27,25 @@ ind, tha
 ## Supported Tasks
 
 Question Answering
-    
+
 ## Dataset Usage
 ### Using `datasets` library
 ```
-    from datasets import load_dataset
-    dset = datasets.load_dataset("SEACrowd/tydiqa", trust_remote_code=True)
+from datasets import load_dataset
+dset = datasets.load_dataset("SEACrowd/tydiqa", trust_remote_code=True)
 ```
 ### Using `seacrowd` library
 ```import seacrowd as sc
 # Load the dataset using the default config
-    dset = sc.load_dataset("tydiqa", schema="seacrowd")
+dset = sc.load_dataset("tydiqa", schema="seacrowd")
 # Check all available subsets (config names) of the dataset
-    print(sc.available_config_names("tydiqa"))
+print(sc.available_config_names("tydiqa"))
 # 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