Upload README.md with huggingface_hub

README.md

@@ -14,7 +14,7 @@ tags:
 - word-list
 ---
 
-    WEATHub is a dataset containing 24 languages. It contains words organized into groups of (target1, target2, attribute1, attribute2) to measure the association target1:target2 :: attribute1:attribute2. For example target1 can be insects, target2 can be flowers. And we might be trying to measure whether we find insects or flowers pleasant or unpleasant. The measurement of word associations is quantified using the WEAT metric from their paper. It is a metric that calculates an effect size (Cohen's d) and also provides a p-value (to measure statistical significance of the results). In their paper, they use word embeddings from language models to perform these tests and understand biased associations in language models across different languages.
+WEATHub is a dataset containing 24 languages. It contains words organized into groups of (target1, target2, attribute1, attribute2) to measure the association target1:target2 :: attribute1:attribute2. For example target1 can be insects, target2 can be flowers. And we might be trying to measure whether we find insects or flowers pleasant or unpleasant. The measurement of word associations is quantified using the WEAT metric from their paper. It is a metric that calculates an effect size (Cohen's d) and also provides a p-value (to measure statistical significance of the results). In their paper, they use word embeddings from language models to perform these tests and understand biased associations in language models across different languages.
 
 
 ## Languages
@@ -24,25 +24,25 @@ tha, tgl, vie, cmn, eng
 ## Supported Tasks
 
 Word List
-    
+
 ## Dataset Usage
 ### Using `datasets` library
 ```
-    from datasets import load_dataset
-    dset = datasets.load_dataset("SEACrowd/weathub", trust_remote_code=True)
+from datasets import load_dataset
+dset = datasets.load_dataset("SEACrowd/weathub", trust_remote_code=True)
 ```
 ### Using `seacrowd` library
 ```import seacrowd as sc
 # Load the dataset using the default config
-    dset = sc.load_dataset("weathub", schema="seacrowd")
+dset = sc.load_dataset("weathub", schema="seacrowd")
 # Check all available subsets (config names) of the dataset
-    print(sc.available_config_names("weathub"))
+print(sc.available_config_names("weathub"))
 # 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