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README.md

@@ -11,7 +11,7 @@ tags:
 ---
 
 
-The Vietnamese dataset for social context summarization     The dataset contains 141 open-domain articles along with     3,760 sentences, 2,448 extracted standard sentences and     comments as standard summaries and 6,926 comments in 12     events. This dataset was manually annotated by human.     Note that the extracted standard summaries also include comments.    The label of a sentence or comment was generated based on the     voting among social annotators. For example, given a sentence,     each annotator makes a binary decision in order to indicate     that whether this sentence is a summary candidate (YES) or not     (NO). If three annotators agree yes, this sentences is labeled by 3.     Therefore, the label of each sentence or comment ranges from 1 to 5    (1: very poor, 2: poor, 3: fair, 4: good; 5: perfect). The standard     summary sentences are those which receive at least three agreements     from annotators. The inter-agreement calculated by Cohen's Kappa     after validation among annotators is 0.685.
+The Vietnamese dataset for social context summarizationThe dataset contains 141 open-domain articles along with3,760 sentences, 2,448 extracted standard sentences andcomments as standard summaries and 6,926 comments in 12events. This dataset was manually annotated by human.Note that the extracted standard summaries also include comments.The label of a sentence or comment was generated based on thevoting among social annotators. For example, given a sentence,each annotator makes a binary decision in order to indicatethat whether this sentence is a summary candidate (YES) or not(NO). If three annotators agree yes, this sentences is labeled by 3.Therefore, the label of each sentence or comment ranges from 1 to 5(1: very poor, 2: poor, 3: fair, 4: good; 5: perfect). The standardsummary sentences are those which receive at least three agreementsfrom annotators. The inter-agreement calculated by Cohen's Kappaafter validation among annotators is 0.685.
 
 
 ## Languages
@@ -21,25 +21,25 @@ vie
 ## Supported Tasks
 
 Summarization
-    
+
 ## Dataset Usage
 ### Using `datasets` library
 ```
-    from datasets import load_dataset
-    dset = datasets.load_dataset("SEACrowd/vsolscsum", trust_remote_code=True)
+from datasets import load_dataset
+dset = datasets.load_dataset("SEACrowd/vsolscsum", trust_remote_code=True)
 ```
 ### Using `seacrowd` library
 ```import seacrowd as sc
 # Load the dataset using the default config
-    dset = sc.load_dataset("vsolscsum", schema="seacrowd")
+dset = sc.load_dataset("vsolscsum", schema="seacrowd")
 # Check all available subsets (config names) of the dataset
-    print(sc.available_config_names("vsolscsum"))
+print(sc.available_config_names("vsolscsum"))
 # 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