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@@ -4,31 +4,238 @@ configs:
   data_files:
   - split: train
     path:
-      - "nepberta/clean_date_categories/chunk_1.txt"
-      - "nepberta/clean_date_categories/chunk_2.txt"
-      - "nepberta/clean_date_categories/chunk_3.txt"
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-      - "nepberta/clean_date_categories/chunk_18.txt"
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   - split: test
     path:
-      - "nepberta/clean_date_categories/chunk_19.txt"
-      - "nepberta/clean_date_categories/chunk_20.txt"
-      - "nepberta/clean_date_categories/chunk_21.txt"
-      - "nepberta/clean_date_categories/chunk_22.txt"
-      - "nepberta/clean_date_categories/chunk_23.txt"
+    - nepberta/clean_date_categories/chunk_58.txt
+    - nepberta/clean_date_categories/chunk_59.txt
+    - nepberta/clean_date_categories/chunk_6.txt
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+    - nepberta/clean_date_categories/chunk_7.txt
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 - config_name: scrapy_engine
   data_files:
   - split: train
@@ -46,7 +253,7 @@ This repository contains two configurations of Nepali LLM datasets:
 - Files: [List any specific files or formats]
 
 ### 2. Nepberta
-- Description: This dataset is derived from the Nepberta project and contains cleaned data specifically related to the project. The dataset combines all articles into a single string, with each article ending in <|endoftext|>. This long string is then segmented into chunks, each approximately 500 MB in size.
+- Description: This dataset is derived from the Nepberta project and contains cleaned data specifically related to the project. The dataset contains **cleaned text chunks of size ~50 mb ** of all articles into a single giant string, with each article ending in <|endoftext|>. This long string is then segmented into chunks, each approximately 500 MB in size.
 - Files: contains 23 files each ~500Mb (chunk_1.txt, chunk_2.txt, ... chunk_23.txt)
 - split:train
   * files: chunk_1.txt to chunk_18.txt
@@ -69,7 +276,7 @@ len(nepberta_train['text']) # 18 : number of chunks
 len(nepberta_train['text'][0])  # length of large text equivalent to 500 MB text
 
 # use streaming=True to avoid downloading entire dataset
-nepberta_train = load_dataset("Aananda-giri/nepali_llm_datasets", name="nepberta", split="train", streaming=True)
+nepberta_train = load_dataset("Aananda-giri/nepali_llm_datasets", name="nepberta", streaming=True)['train']
 
 # using next
 next(iter(nepberta_train))