Datasets:
language:
- bn
- en
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
- ur
license: cc-by-4.0
size_categories:
- 1M<n<10M
pretty_name: Pralekha
dataset_info:
- config_name: alignable
features:
- name: n_id
dtype: string
- name: doc_id
dtype: string
- name: lang
dtype: string
- name: text
dtype: string
splits:
- name: ben
num_bytes: 651961117
num_examples: 95813
- name: eng
num_bytes: 1048149692
num_examples: 298111
- name: guj
num_bytes: 549286108
num_examples: 67847
- name: hin
num_bytes: 1754308559
num_examples: 204809
- name: kan
num_bytes: 567860764
num_examples: 61998
- name: mal
num_bytes: 498894372
num_examples: 67760
- name: mar
num_bytes: 961277740
num_examples: 135301
- name: ori
num_bytes: 397642857
num_examples: 46167
- name: pan
num_bytes: 872586190
num_examples: 108459
- name: tam
num_bytes: 858335433
num_examples: 149637
- name: tel
num_bytes: 914832899
num_examples: 110077
- name: urd
num_bytes: 1199225480
num_examples: 220425
download_size: 3954199760
dataset_size: 10274361211
- config_name: unalignable
features:
- name: n_id
dtype: string
- name: doc_id
dtype: string
- name: lang
dtype: string
- name: text
dtype: string
splits:
- name: eng
num_bytes: 420307531
num_examples: 149055
- name: tel
num_bytes: 372531108
num_examples: 55038
- name: mar
num_bytes: 428715921
num_examples: 67650
- name: guj
num_bytes: 214351582
num_examples: 33923
- name: hin
num_bytes: 683869386
num_examples: 102404
- name: ori
num_bytes: 111986274
num_examples: 23083
- name: tam
num_bytes: 614171222
num_examples: 74818
- name: urd
num_bytes: 644995094
num_examples: 110212
- name: kan
num_bytes: 189633814
num_examples: 30999
- name: mal
num_bytes: 192394324
num_examples: 33880
- name: ben
num_bytes: 273391595
num_examples: 47906
- name: pan
num_bytes: 328564948
num_examples: 54229
download_size: 1855179179
dataset_size: 4474912799
configs:
- config_name: alignable
data_files:
- split: ben
path: alignable/ben-*
- split: eng
path: alignable/eng-*
- split: guj
path: alignable/guj-*
- split: hin
path: alignable/hin-*
- split: kan
path: alignable/kan-*
- split: mal
path: alignable/mal-*
- split: mar
path: alignable/mar-*
- split: ori
path: alignable/ori-*
- split: pan
path: alignable/pan-*
- split: tam
path: alignable/tam-*
- split: tel
path: alignable/tel-*
- split: urd
path: alignable/urd-*
- config_name: unalignable
data_files:
- split: eng
path: unalignable/eng-*
- split: tel
path: unalignable/tel-*
- split: mar
path: unalignable/mar-*
- split: guj
path: unalignable/guj-*
- split: hin
path: unalignable/hin-*
- split: ori
path: unalignable/ori-*
- split: tam
path: unalignable/tam-*
- split: urd
path: unalignable/urd-*
- split: kan
path: unalignable/kan-*
- split: mal
path: unalignable/mal-*
- split: ben
path: unalignable/ben-*
- split: pan
path: unalignable/pan-*
tags:
- data-mining
- document-alignment
- parallel-corpus
Pralekha: An Indic Document Alignment Evaluation Benchmark
PRALEKHA is a large-scale parallel document dataset for evaluating cross-lingual document alignment techniques across 11 Indic languages and English. It comprises over 3 million document pairs, with 1.5 million being English-centric.
Dataset Description
PRALEKHA covers 12 languages—Bengali (ben), Gujarati (guj), Hindi (hin), Kannada (kan), Malayalam (mal), Marathi (mar), Odia (ori), Punjabi (pan), Tamil (tam), Telugu (tel), Urdu (urd), and English (eng). It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: news bulletins and podcast scripts, offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.
The dataset has a 1:2 ratio of aligned to unaligned document pairs, making it ideal for benchmarking cross-lingual document alignment techniques.
Data Fields
Each data sample includes:
n_id: Unique identifier for aligned document pairs.doc_id: Unique identifier for individual documents.lang: Language of the document (ISO-3 code).text: The textual content of the document.
Data Sources
- News Bulletins: Data was custom-scraped from the Indian Press Information Bureau (PIB) website. Documents were aligned by matching bulletin IDs, which interlink bulletins across languages.
- Podcast Scripts: Data was sourced from Mann Ki Baat, a radio program hosted by the Indian Prime Minister. This program, originally spoken in Hindi, was manually transcribed and translated into various Indian languages.
Dataset Size Statistics
| Split | Number of Documents | Size (bytes) |
|---|---|---|
| Aligned | 1,566,404 | 10,274,361,211 |
| Unaligned | 783,197 | 4,466,506,637 |
| Total | 2,349,601 | 14,740,867,848 |
Language-wise Statistics
Language (ISO-3) |
Aligned Documents | Unaligned Documents | Total Documents |
|---|---|---|---|
Bengali (ben) |
95,813 | 47,906 | 143,719 |
English (eng) |
298,111 | 149,055 | 447,166 |
Gujarati (guj) |
67,847 | 33,923 | 101,770 |
Hindi (hin) |
204,809 | 102,404 | 307,213 |
Kannada (kan) |
61,998 | 30,999 | 92,997 |
Malayalam (mal) |
67,760 | 33,880 | 101,640 |
Marathi (mar) |
135,301 | 67,650 | 202,951 |
Odia (ori) |
46,167 | 23,083 | 69,250 |
Punjabi (pan) |
108,459 | 54,229 | 162,688 |
Tamil (tam) |
149,637 | 74,818 | 224,455 |
Telugu (tel) |
110,077 | 55,038 | 165,115 |
Urdu (urd) |
220,425 | 110,212 | 330,637 |
Usage
You can use the following commands to download and explore the dataset:
Downloading the Entire Dataset
from datasets import load_dataset
dataset = load_dataset("ai4bharat/pralekha")
Downloading a Specific Split
from datasets import load_dataset
dataset = load_dataset("ai4bharat/pralekha", split="<split_name>")
# For example: dataset = load_dataset("ai4bharat/pralekha", split="aligned")
License
This dataset is released under the CC BY 4.0 license.
Contact
For any questions or feedback, please contact:
- Raj Dabre (raj.dabre@cse.iitm.ac.in)
- Sanjay Suryanarayanan (sanj.ai@outlook.com)
- Haiyue Song (haiyue.song@nict.go.jp)
- Mohammed Safi Ur Rahman Khan (safikhan2000@gmail.com)
Please get in touch with us for any copyright concerns.