README.md

---
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: dev
  features:
  - name: src_text
    dtype: string
  - name: tgt_text
    dtype: string
  splits:
  - name: eng_ben
    num_bytes: 11878032
    num_examples: 1000
  - name: eng_guj
    num_bytes: 12114408
    num_examples: 1000
  - name: eng_hin
    num_bytes: 11866493
    num_examples: 1000
  - name: eng_kan
    num_bytes: 12737616
    num_examples: 1000
  - name: eng_mal
    num_bytes: 13282361
    num_examples: 1000
  - name: eng_mar
    num_bytes: 12562695
    num_examples: 1000
  - name: eng_ori
    num_bytes: 12440443
    num_examples: 1000
  - name: eng_pan
    num_bytes: 11887954
    num_examples: 1000
  - name: eng_tam
    num_bytes: 10889623
    num_examples: 1000
  - name: eng_tel
    num_bytes: 12862241
    num_examples: 1000
  - name: eng_urd
    num_bytes: 9313209
    num_examples: 1000
  download_size: 49754255
  dataset_size: 131835075
- config_name: test
  features:
  - name: src_text
    dtype: string
  - name: tgt_text
    dtype: string
  splits:
  - name: eng_ben
    num_bytes: 11326293
    num_examples: 1000
  - name: eng_guj
    num_bytes: 11754732
    num_examples: 1000
  - name: eng_hin
    num_bytes: 11572603
    num_examples: 1000
  - name: eng_kan
    num_bytes: 12210417
    num_examples: 1000
  - name: eng_mal
    num_bytes: 12750095
    num_examples: 1000
  - name: eng_mar
    num_bytes: 12260214
    num_examples: 1000
  - name: eng_ori
    num_bytes: 11926414
    num_examples: 1000
  - name: eng_pan
    num_bytes: 11534797
    num_examples: 1000
  - name: eng_tam
    num_bytes: 11072385
    num_examples: 1000
  - name: eng_tel
    num_bytes: 12530011
    num_examples: 1000
  - name: eng_urd
    num_bytes: 9196555
    num_examples: 1000
  download_size: 49449543
  dataset_size: 128134516
- config_name: unalignable
  features:
  - name: n_id
    dtype: string
  - name: doc_id
    dtype: string
  - name: lang
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: ben
    num_bytes: 273391595
    num_examples: 47906
  - name: eng
    num_bytes: 420307531
    num_examples: 149055
  - name: guj
    num_bytes: 214351582
    num_examples: 33923
  - name: hin
    num_bytes: 683869386
    num_examples: 102404
  - name: kan
    num_bytes: 189633814
    num_examples: 30999
  - name: mal
    num_bytes: 192394324
    num_examples: 33880
  - name: mar
    num_bytes: 428715921
    num_examples: 67650
  - name: ori
    num_bytes: 111986274
    num_examples: 23083
  - name: pan
    num_bytes: 328564948
    num_examples: 54229
  - name: tam
    num_bytes: 614171222
    num_examples: 74818
  - name: tel
    num_bytes: 372531108
    num_examples: 55038
  - name: urd
    num_bytes: 644995094
    num_examples: 110212
  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: dev
  data_files:
  - split: eng_ben
    path: dev/eng_ben-*
  - split: eng_guj
    path: dev/eng_guj-*
  - split: eng_hin
    path: dev/eng_hin-*
  - split: eng_kan
    path: dev/eng_kan-*
  - split: eng_mal
    path: dev/eng_mal-*
  - split: eng_mar
    path: dev/eng_mar-*
  - split: eng_ori
    path: dev/eng_ori-*
  - split: eng_pan
    path: dev/eng_pan-*
  - split: eng_tam
    path: dev/eng_tam-*
  - split: eng_tel
    path: dev/eng_tel-*
  - split: eng_urd
    path: dev/eng_urd-*
- config_name: test
  data_files:
  - split: eng_ben
    path: test/eng_ben-*
  - split: eng_guj
    path: test/eng_guj-*
  - split: eng_hin
    path: test/eng_hin-*
  - split: eng_kan
    path: test/eng_kan-*
  - split: eng_mal
    path: test/eng_mal-*
  - split: eng_mar
    path: test/eng_mar-*
  - split: eng_ori
    path: test/eng_ori-*
  - split: eng_pan
    path: test/eng_pan-*
  - split: eng_tam
    path: test/eng_tam-*
  - split: eng_tel
    path: test/eng_tel-*
  - split: eng_urd
    path: test/eng_urd-*
- config_name: unalignable
  data_files:
  - split: ben
    path: unalignable/ben-*
  - split: eng
    path: unalignable/eng-*
  - split: guj
    path: unalignable/guj-*
  - split: hin
    path: unalignable/hin-*
  - split: kan
    path: unalignable/kan-*
  - split: mal
    path: unalignable/mal-*
  - split: mar
    path: unalignable/mar-*
  - split: ori
    path: unalignable/ori-*
  - split: pan
    path: unalignable/pan-*
  - split: tam
    path: unalignable/tam-*
  - split: tel
    path: unalignable/tel-*
  - split: urd
    path: unalignable/urd-*
tags:
- parallel-corpus
- document-alignment
- machine-translation
task_categories:
- translation
---

# Pralekha: Cross-Lingual Document Alignment for Indic Languages

<div style="display: flex; gap: 10px;">
  <a href="https://arxiv.org/abs/2411.19096">
    <img src="https://img.shields.io/badge/arXiv-2411.19096-B31B1B" alt="arXiv">
  </a>
  <a href="https://huggingface.co/datasets/ai4bharat/Pralekha">
    <img src="https://img.shields.io/badge/huggingface-Pralekha-yellow" alt="HuggingFace">
  </a>
  <a href="https://github.com/AI4Bharat/Pralekha">
    <img src="https://img.shields.io/badge/github-Pralekha-blue" alt="GitHub">
  </a>
  <a href="https://creativecommons.org/licenses/by/4.0/">
    <img src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey" alt="License: CC BY 4.0">
  </a>
</div>

**Pralekha** is a large-scale parallel document dataset spanning across **11 Indic languages** and **English**. It comprises over **3 million** document pairs, with **1.5 million** being English-centric. This dataset serves both as a benchmark for evaluating **Cross-Lingual Document Alignment (CLDA)** techniques and as a domain-specific parallel corpus for training document-level **Machine Translation (MT)** models in Indic Languages.

---

## 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** ([Indian Press Information Bureau (PIB)](https://pib.gov.in)) and **Podcast Scripts** ([Mann Ki Baat (MKB)](https://www.pmindia.gov.in/en/mann-ki-baat)), offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.

While this accounts for `alignable` (parallel) documents, In real-world scenarios, multilingual corpora often include `unalignable` documents. To simulate this for CLDA evaluation, we sample `unalignable` documents from [Sangraha Unverified](https://huggingface.co/datasets/ai4bharat/sangraha/viewer/unverified), selecting 50% of Pralekha’s size to maintain a 1:2 ratio of `unalignable` to `alignable` documents. 

For Machine Translation (MT) tasks, we first randomly sample 1,000 documents from the `alignable` subset per English-Indic language pair for each development (dev) and test set, ensuring a good distribution of varying document lengths. After excluding these sampled documents, we use the remaining documents as the training set for training document-level machine translation models.

---

## Data Fields

### Alignable & Unalignable Set:

- **`n_id`:** Unique identifier for `alignable` document pairs (Random `n_id`'s are assigned for the `unalignable` set.)
- **`doc_id`:** Unique identifier for individual documents.
- **`lang`:** Language of the document (ISO 639-3 code).
- **`text`:** The textual content of the document.

### Train, Dev & Test Set:

- **`src_lang`:** Source Language (eng)
- **`src_text`:** Source Language Text
- **`tgt_lang`:** Target Language (ISO 639-3 code)
- **`tgt_text`:** Target Language Text

---

## Usage

You can load specific **subsets** and **splits** from this dataset using the `datasets` library.

### Load an entire subset

```python
from datasets import load_dataset

dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>")
# <subset> = alignable, unalignable, train, dev & test.
```

### Load a specific split within a subset

```python
from datasets import load_dataset

dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>/<lang>")
# <subset> = alignable, unalignable ; <lang> = ben, eng, guj, hin, kan, mal, mar, ori, pan, tam, tel, urd.
```
```python
from datasets import load_dataset

dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>/eng_<lang>")
# <subset> = train, dev & test ; <lang> = ben, guj, hin, kan, mal, mar, ori, pan, tam, tel, urd.
```

---

## Data Size Statistics

| Split         | Number of Documents | Size (bytes)       |
|---------------|---------------------|--------------------|
| **Alignable**   | 1,566,404           | 10,274,361,211     |
| **Unalignable** | 783,197             | 4,466,506,637      |
| **Total**     | 2,349,601           | 14,740,867,848     |

## Language-wise Statistics

| Language (`ISO-3`) | Alignable Documents | Unalignable 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         |

---

# Citation
If you use Pralekha in your work, please cite us:

```
@article{suryanarayanan2024pralekha,
  title={Pralekha: An Indic Document Alignment Evaluation Benchmark},
  author={Suryanarayanan, Sanjay and Song, Haiyue and Khan, Mohammed Safi Ur Rahman and Kunchukuttan, Anoop and Khapra, Mitesh M and Dabre, Raj},
  journal={arXiv preprint arXiv:2411.19096},
  year={2024}
}
```

## License

This dataset is released under the [**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) license.


## Contact

For any questions or feedback, please contact:

- Raj Dabre ([[email protected]](mailto:[email protected]))  
- Sanjay Suryanarayanan ([[email protected]](mailto:[email protected]))  
- Haiyue Song ([[email protected]](mailto:[email protected]))  
- Mohammed Safi Ur Rahman Khan ([[email protected]](mailto:[email protected]))  

Please get in touch with us for any copyright concerns.