metadata

language:
  - ur
license: cc-by-sa-4.0
pretty_name: ALIF Urdu Corpus
tags:
  - urdu
  - alif
  - orature-ai
  - text-corpus
  - pretraining
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: Data
      dtype: string
    - name: Category
      dtype: string
    - name: Source
      dtype: string
  splits:
    - name: train
      num_bytes: 14548389
      num_examples: 5000
  download_size: 6755924
  dataset_size: 14548389

ALIF_Urdu_Corpus (Preview)

This dataset, ALIF_Urdu_Corpus, is part of the ALIF الف project by Orature AI. It was curated for pretraining Urdu language models. It serves as a preview to our entire 33GB Dataset.

Dataset Description

Curated by: Orature AI (S.M Ali Naqvi, Zainab Haider, Haya Fatima, Ali M Asad, Hammad Sajid)
Supervised by: Dr. Abdul Samad (Habib University)
Language(s): Urdu (ur).
License: Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

Purpose of the Dataset:

(preview) To serve as a large-scale, diverse, and high-quality foundation for pretraining generative language models for Urdu.

Languages

The data is in Urdu.

Dataset Structure

(For Pretraining Corpus - ALIF-Urdu-Corpus): The dataset is structured as a collection of text entries, in CSV format, with the following columns:

Data	Category	Source

Data: The Data column contains the actual Urdu data
Category: The Category column refers to the type of data it is, for example CommonCrawl, Fineweb, etc.
Source: The Source column contains the actual source from where the data was taken.

Data Collection and Preprocessing

The Complete ALIF-Urdu-Corpus: The dataset was meticulously collected from a variety of sources to ensure diversity and coverage:

Common Crawl Dumps: 11.3 GB (Dump 1) and 8.1 GB (Dump 2) of filtered Urdu text.
Translation Data: 5.5 GB of educational content from the English FineWeb dataset, translated to Urdu using Google Translate API.
News Websites: 3.3 GB scraped from various Urdu news websites.
Existing Datasets: 2.9 GB from public Urdu corpora (e.g., UrduHack, other open-source).
Books (OCR Processed): 1.3 GB of text extracted from scanned Urdu books using Google Vision OCR, followed by post-OCR cleaning.
Blog Sites: 0.6 GB from various Urdu blogs.

Preprocessing Steps:

Cleaning: Removal of HTML tags, links, numbers (unless contextually relevant), email addresses, and other non-linguistic noise.
Encoding Normalization: Ensured consistent UTF-8 encoding.
Language Filtering: Non-Urdu content was filtered out using language detection tools.
Deduplication: Rigorous deduplication was performed using MinHash-based Locality Sensitive Hashing (LSH) to identify and remove near-duplicate documents and paragraphs, both within and across source datasets. Exact duplicates were also removed.
Formatting: Final data organized into a structured format (e.g., CSV), with End-of-Text (EOT) tokens used to delineate documents/segments during training.

Dataset Size

ALIF_Urdu_Corpus:
- Total Size: ~33 GB for ALIF-Urdu-Corpus, however, this dataset preview contains about 13.7MB of that data.
- Number of Rows/Examples: 5000 rows

Intended Uses

Pretraining Language Models: The ALIF-Urdu-Corpus is primarily intended for pretraining large-scale generative language models for Urdu.
Instruction Fine-tuning: The ALIF-Urdu-Instruct dataset is designed for fine-tuning pretrained models to follow instructions in Urdu.
NLP Research: Can be used for various research tasks in Urdu NLP, such as studying linguistic phenomena, bias in text, or developing new preprocessing techniques.
Benchmarking: Subsets can be used for creating benchmarks for Urdu language understanding or generation.