| --- |
| 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 |
| --- |
| |
| # 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 for Cross-Lingual Document Alignment (CLDA) and Machine Translation (MT) 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** ([Indian Press Information Bureau (PIB)](https://pib.gov.in)) and **Podcast Scripts** ([Mann Ki Baat](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 per English-Indic language pair, ensuring a good distribution of varying document lengths. After excluding these sampled documents, we use the remaining documents 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 ([raj.dabre@cse.iitm.ac.in](mailto:raj.dabre@cse.iitm.ac.in)) |
| - Sanjay Suryanarayanan ([sanj.ai@outlook.com](mailto:sanj.ai@outlook.com)) |
| - Haiyue Song ([haiyue.song@nict.go.jp](mailto:haiyue.song@nict.go.jp)) |
| - Mohammed Safi Ur Rahman Khan ([safikhan2000@gmail.com](mailto:safikhan2000@gmail.com)) |
|
|
| Please get in touch with us for any copyright concerns. |
