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README.md

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-The TyDIQA_ID-NLI dataset is derived from the TyDIQA_ID     question answering dataset, utilizing named     entity recognition (NER), chunking tags,     Regex, and embedding similarity techniques     to determine its contradiction sets.     Collected through this process,     the dataset comprises various columns beyond     premise, hypothesis, and label, including     properties aligned with NER and chunking tags.     This dataset is designed to facilitate Natural    Language Inference (NLI) tasks and contains     information extracted from diverse sources     to provide comprehensive coverage. Each data     instance encapsulates premise, hypothesis, label,     and additional properties pertinent to NLI evaluation.
+The TyDIQA_ID-NLI dataset is derived from the TyDIQA_IDquestion answering dataset, utilizing namedentity recognition (NER), chunking tags,Regex, and embedding similarity techniquesto determine its contradiction sets.Collected through this process,the dataset comprises various columns beyondpremise, hypothesis, and label, includingproperties aligned with NER and chunking tags.This dataset is designed to facilitate NaturalLanguage Inference (NLI) tasks and containsinformation extracted from diverse sourcesto provide comprehensive coverage. Each datainstance encapsulates premise, hypothesis, label,and additional properties pertinent to NLI evaluation.
 
 
 ## Languages
@@ -21,25 +21,25 @@ ind
 ## Supported Tasks
 
 Textual Entailment
-    
+
 ## Dataset Usage
 ### Using `datasets` library
 ```
-    from datasets import load_dataset
-    dset = datasets.load_dataset("SEACrowd/tydiqa_id_nli", trust_remote_code=True)
+from datasets import load_dataset
+dset = datasets.load_dataset("SEACrowd/tydiqa_id_nli", trust_remote_code=True)
 ```
 ### Using `seacrowd` library
 ```import seacrowd as sc
 # Load the dataset using the default config
-    dset = sc.load_dataset("tydiqa_id_nli", schema="seacrowd")
+dset = sc.load_dataset("tydiqa_id_nli", schema="seacrowd")
 # Check all available subsets (config names) of the dataset
-    print(sc.available_config_names("tydiqa_id_nli"))
+print(sc.available_config_names("tydiqa_id_nli"))
 # 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