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README.md

@@ -18,16 +18,16 @@ tags:
 - self-supervised-pretraining
 ---
 
-        This corpus is an attempt to recreate the dataset used for training
-        XLM-R. This corpus comprises of monolingual data for 100+ languages and
-        also includes data for romanized languages (indicated by *_rom). This
-        was constructed using the urls and paragraph indices provided by the
-        CC-Net repository by processing January-December 2018 Commoncrawl
-        snapshots. Each file comprises of documents separated by
-        double-newlines and paragraphs within the same document separated by a
-        newline. The data is generated using the open source CC-Net repository.
-        No claims of intellectual property are made on the work of preparation
-        of the corpus.
+This corpus is an attempt to recreate the dataset used for training
+XLM-R. This corpus comprises of monolingual data for 100+ languages and
+also includes data for romanized languages (indicated by *_rom). This
+was constructed using the urls and paragraph indices provided by the
+CC-Net repository by processing January-December 2018 Commoncrawl
+snapshots. Each file comprises of documents separated by
+double-newlines and paragraphs within the same document separated by a
+newline. The data is generated using the open source CC-Net repository.
+No claims of intellectual property are made on the work of preparation
+of the corpus.
 
 
 ## Languages
@@ -37,25 +37,25 @@ ind, jav, sun, mya, mya_zaw, lao, khm, tgl, vie, tha, zlm
 ## Supported Tasks
 
 Self Supervised Pretraining
-    
+
 ## Dataset Usage
 ### Using `datasets` library
 ```
-    from datasets import load_dataset
-    dset = datasets.load_dataset("SEACrowd/cc100", trust_remote_code=True)
+from datasets import load_dataset
+dset = datasets.load_dataset("SEACrowd/cc100", trust_remote_code=True)
 ```
 ### Using `seacrowd` library
 ```import seacrowd as sc
 # Load the dataset using the default config
-    dset = sc.load_dataset("cc100", schema="seacrowd")
+dset = sc.load_dataset("cc100", schema="seacrowd")
 # Check all available subsets (config names) of the dataset
-    print(sc.available_config_names("cc100"))
+print(sc.available_config_names("cc100"))
 # 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