---
configs:
- config_name: nepberta
  data_files:
  - split: train
    path:
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    - nepberta/clean_date_categories/chunk_57.txt
  - split: test
    path:
    - nepberta/clean_date_categories/chunk_58.txt
    - nepberta/clean_date_categories/chunk_59.txt
    - nepberta/clean_date_categories/chunk_6.txt
    - nepberta/clean_date_categories/chunk_60.txt
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    - nepberta/clean_date_categories/chunk_77.txt
    - nepberta/clean_date_categories/chunk_78.txt
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    - nepberta/clean_date_categories/chunk_80.txt
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- config_name: scrapy_engine
  data_files:
  - split: train
    path:
      - "scrapy_engine/cleaned_data.csv"
- config_name: pre_tokenized
  data_files:
  - split: train
    path:
      - pre_tokenized/nepberta.parquet
- config_name: iriisnepal_u_nepberta_512
  data_files:
  - split: train
    path:
      - "pre_tokenized/iriisnepal_u_nepberta_train_512.parquet"
  - split: test
    path:
      - "pre_tokenized/iriisnepal_u_nepberta_test_512.parquet"
---
# 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 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
- split:test
  * files: chunk_19.txt to chunk_23.txt

## Usage

To load the datasets:

```python
# it loads entire dataset first
from datasets import load_dataset

# Load nepberta configuration
nepberta_dataset = load_dataset("Aananda-giri/nepali_llm_datasets", name="nepberta", split='train') # use `streaming=True` 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", streaming=True)['train']

# using next
next_text_chunk = next(iter(nepberta_train))
print(len(next_text_chunk['text']))

# using for loop
for large_chunk in nepberta_train:
  print(len(large_chunk['text']))
  break

  # code to process large_chunk['text']

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


## pre-tokenized
**IRIISNEPAL_U_Nepberta**
* these files use context len. 512 and stride 384 (.75 * context_length)
* `pre_tokenized/iriisnepal_u_nepberta_test_512.parquet`
* `pre_tokenized/iriisnepal_u_nepberta_train_512.parquet`