README.md

metadata

configs:
  - config_name: nepberta
    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
          - nepberta/clean_date_categories/chunk_4.txt
          - nepberta/clean_date_categories/chunk_5.txt
          - nepberta/clean_date_categories/chunk_6.txt
          - nepberta/clean_date_categories/chunk_7.txt
          - nepberta/clean_date_categories/chunk_8.txt
          - nepberta/clean_date_categories/chunk_9.txt
          - nepberta/clean_date_categories/chunk_10.txt
          - nepberta/clean_date_categories/chunk_11.txt
          - nepberta/clean_date_categories/chunk_12.txt
          - nepberta/clean_date_categories/chunk_13.txt
          - nepberta/clean_date_categories/chunk_14.txt
          - nepberta/clean_date_categories/chunk_15.txt
          - nepberta/clean_date_categories/chunk_16.txt
          - nepberta/clean_date_categories/chunk_17.txt
          - nepberta/clean_date_categories/chunk_18.txt
      - 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
  - config_name: scrapy_engine
    data_files:
      - split: train
        path:
          - scrapy_engine/cleaned_data.csv

Nepali LLM Datasets

This repository contains two configurations of Nepali LLM datasets:

Configurations

1. Scrapy Engine

• Description: Contains data collected using a web scraping engine.
• 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.
• 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
• split:test
  • files: chunk_19.txt to chunk_23.txt

Usage

To load the datasets:

# it loads entire dataset first
from datasets import load_dataset

# Load nepberta configuration
nepberta_train = load_dataset("Aananda-giri/nepali_llm_datasets", name="nepberta", split='train[0:2]') # load 2 chunks, streaming mode to avoid downloading all the dataset

# length of chunks
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)

# using next
next(iter(nepberta_train))

# using for loop
for large_chunk in nepberta_train:
  pass
  # code to process large_chunk['text']

# Load scrapy engine data
scrapy_train = load_dataset("Aananda-giri/nepali_llm_datasets", name="scrapy_engine" split="train")