Datasets:
pretty_name: PIB Parallel
language:
- en
- ur
- hi
- pa
- mr
- te
- gu
- kn
- ml
- ta
- bn
- or
- as
- mni
- ne
- gom
- kha
license: other
license_name: pib-copyright-policy-and-cc-by-4.0
license_link: https://huggingface.co/datasets/micahchoo/pib-parallel#terms
task_categories:
- translation
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
configs:
- config_name: sentences
default: true
data_files:
- split: jul2019
path: sentences/2019-07.parquet
- split: may2021
path: sentences/2021-05.parquet
- split: aug2023
path: sentences/2023-08.parquet
- split: nov2025
path: sentences/2025-11.parquet
- config_name: documents
data_files:
- split: jul2019
path: documents/2019-07.parquet
- split: may2021
path: documents/2021-05.parquet
- split: aug2023
path: documents/2023-08.parquet
- split: nov2025
path: documents/2025-11.parquet
- config_name: regional
data_files:
- split: gom
path: regional/sentences/gom.parquet
- split: kha
path: regional/sentences/kha.parquet
- split: mni
path: regional/sentences/mni.parquet
- split: ne
path: regional/sentences/ne.parquet
- config_name: regional_documents
data_files:
- split: gom
path: regional/documents/gom.parquet
- split: kha
path: regional/documents/kha.parquet
- split: mni
path: regional/documents/mni.parquet
- split: ne
path: regional/documents/ne.parquet
PIB Parallel
English sentences beside their official translations into 16 Indian languages, from the press releases India's Press Information Bureau (PIB) published, one month to a split: 451,815 sentence pairs and 24,618 document pairs from 4 months so far, each pair traceable to the two releases it came from.
PIB is the Government of India's press office. It publishes each release in English and links the same release translated by its offices. Earlier PIB corpora (CVIT-PIB, PMIndia) end around 2019; this one takes whole months, from every office that publishes in its own language, with release IDs. The months are chosen to differ in time, subject and how much of the translation is machine-made. The pipeline that made it, and fetches any other month, is github.com/micahchoo/pib-parallel.
Before you use it as a test set, read How much is machine translation: part of PIB's translation is a near copy of Google Translate.
Load it
from datasets import load_dataset
sentences = load_dataset("micahchoo/pib-parallel", "sentences", split="nov2025")
documents = load_dataset("micahchoo/pib-parallel", "documents", split="nov2025")
every_month = load_dataset("micahchoo/pib-parallel", "sentences") # one split per month
tamil = sentences.filter(lambda r: r["lang"] == "ta" and r["similarity"] >= 0.85)
A release is at https://www.pib.gov.in/PressReleasePage.aspx?PRID=<prid>.
What is in it
sentences: one row per distinct sentence pair.
| Field | Meaning |
|---|---|
lang |
the translation's language (ISO 639) |
en, text |
the English sentence and its translation |
similarity |
cosine similarity of the two: LaBSE's, 0.70 or more; for Manipuri, LASER3's, 0.80 or more, on a higher scale that is not comparable with LaBSE's |
numbers_agree |
whether both sides hold the same numbers (lakh, crore, million and billion read as values); null when neither has any |
confidence |
low for Konkani, Khasi and Manipuri, which LaBSE does not know or knows only through a related language |
google_copy |
for a sample of pairs only: whether PIB's text is within chrF 90 of Google Translate's for the same English; null where not checked |
occurrences |
how often PIB published this exact pair ("Friends," opens hundreds of speeches) |
en_prid, text_prid |
the English release and the translated release |
office |
the crawl pass that found the pair: delhi-en (English releases and every translation they link) or a regional office (its releases and their English) |
date, posted_by |
the release's date and the office PIB names as its publisher |
documents: one row per English release and one translation, with both titles and bodies (en_title, en_body, title, body), both PRIDs, ministry, office, date, posted_by. Use these to align with another aligner, or to work at document level.
Months
| Split | Month | Sentence pairs | Document pairs | English releases |
|---|---|---|---|---|
jul2019 |
July 2019 | 27,478 | 2,592 | 1,103 |
may2021 |
May 2021 | 93,256 | 6,573 | 1,009 |
aug2023 |
August 2023 | 144,566 | 7,157 | 1,620 |
nov2025 |
November 2025 | 186,515 | 8,296 | 1,786 |
PIB publishes nothing in Bodo, Dogri, Kashmiri, Maithili, Sanskrit, Santali or Sindhi. The offices that publish only their own Hindi (Lucknow, Patna, Bhopal and others) are not crawled.
November 2025
| Language | Code | Sentence pairs | Similarity ≥ 0.85 | Document pairs |
|---|---|---|---|---|
| Hindi | hi |
34,372 | 21,809 | 1,428 |
| Urdu | ur |
31,840 | 22,106 | 1,354 |
| Gujarati | gu |
16,773 | 12,035 | 561 |
| Marathi | mr |
13,712 | 7,186 | 714 |
| Malayalam | ml |
13,313 | 9,483 | 530 |
| Kannada | kn |
12,881 | 9,373 | 434 |
| Punjabi | pa |
11,165 | 7,742 | 457 |
| Telugu | te |
9,962 | 4,649 | 445 |
| Bengali | bn |
9,586 | 4,256 | 518 |
| Odia | or |
9,522 | 5,916 | 306 |
| Assamese | as |
8,166 | 2,824 | 369 |
| Tamil | ta |
7,145 | 3,704 | 785 |
| Manipuri (low confidence) | mni |
4,702 | 2,380 | 228 |
| Konkani (low confidence) | gom |
1,467 | 458 | 71 |
| Nepali | ne |
1,351 | 966 | 32 |
| Khasi (low confidence) | kha |
558 | 207 | 64 |
| All | 186,515 | 115,094 | 8,296 |
August 2023
| Language | Code | Sentence pairs | Similarity ≥ 0.85 | Document pairs |
|---|---|---|---|---|
| Urdu | ur |
28,202 | 18,892 | 1,355 |
| Hindi | hi |
21,120 | 13,205 | 991 |
| Telugu | te |
14,067 | 8,414 | 717 |
| Marathi | mr |
10,844 | 5,773 | 688 |
| Tamil | ta |
10,518 | 6,374 | 812 |
| Kannada | kn |
9,657 | 6,774 | 363 |
| Punjabi | pa |
9,633 | 6,525 | 461 |
| Gujarati | gu |
8,879 | 6,261 | 325 |
| Malayalam | ml |
6,974 | 4,980 | 285 |
| Odia | or |
6,970 | 4,026 | 295 |
| Assamese | as |
6,059 | 2,244 | 241 |
| Manipuri (low confidence) | mni |
5,252 | 2,560 | 282 |
| Bengali | bn |
5,118 | 1,589 | 270 |
| Nepali | ne |
1,104 | 630 | 34 |
| Khasi (low confidence) | kha |
169 | 22 | 38 |
| All | 144,566 | 88,269 | 7,157 |
May 2021
| Language | Code | Sentence pairs | Similarity ≥ 0.85 | Document pairs |
|---|---|---|---|---|
| Hindi | hi |
19,201 | 10,984 | 938 |
| Punjabi | pa |
14,490 | 9,678 | 758 |
| Urdu | ur |
14,171 | 7,236 | 888 |
| Telugu | te |
8,714 | 3,652 | 614 |
| Marathi | mr |
7,667 | 3,238 | 612 |
| Kannada | kn |
5,526 | 2,919 | 352 |
| Tamil | ta |
4,708 | 1,354 | 704 |
| Gujarati | gu |
4,293 | 2,263 | 250 |
| Bengali | bn |
3,732 | 711 | 504 |
| Malayalam | ml |
3,598 | 1,589 | 321 |
| Odia | or |
3,253 | 1,091 | 237 |
| Manipuri (low confidence) | mni |
2,171 | 838 | 225 |
| Assamese | as |
1,732 | 299 | 170 |
| All | 93,256 | 45,852 | 6,573 |
July 2019
| Language | Code | Sentence pairs | Similarity ≥ 0.85 | Document pairs |
|---|---|---|---|---|
| Urdu | ur |
8,682 | 4,067 | 763 |
| Hindi | hi |
5,334 | 2,651 | 434 |
| Marathi | mr |
2,675 | 1,024 | 262 |
| Bengali | bn |
2,570 | 484 | 415 |
| Punjabi | pa |
1,866 | 1,028 | 147 |
| Tamil | ta |
1,617 | 584 | 175 |
| Gujarati | gu |
1,546 | 687 | 87 |
| Malayalam | ml |
1,074 | 349 | 111 |
| Kannada | kn |
911 | 423 | 78 |
| Telugu | te |
825 | 367 | 60 |
| Assamese | as |
378 | 57 | 60 |
| All | 27,478 | 11,721 | 2,592 |
Regional
The offices that publish little in their own language give only tens of releases a month, so for them every month PIB has is pooled into one split per language, in the regional and regional_documents configs. Months already published as month splits are left out, so no pair is in both.
nepali = load_dataset("micahchoo/pib-parallel", "regional", split="ne")
| Language | Split | Sentence pairs | Similarity ≥ 0.85 | Document pairs | Months | Google copies |
|---|---|---|---|---|---|---|
| Konkani (low confidence) | gom |
2,909 | 909 | 140 | November 2023, November 2024 | 12% |
| Khasi (low confidence) | kha |
8,198 | 3,186 | 905 | May 2023 to September 2026 (37) | 3% |
| Manipuri (low confidence) | mni |
350,496 | 153,558 | 25,250 | January 2019 to September 2026 (84) | not measured |
| Nepali | ne |
33,364 | 22,642 | 1,075 | May 2023 to September 2026 (39) | 5% |
How it was made
- Fetched from PIB's own pages: every release each office listed for the month, and the translations each release page links. PIB states which releases are translations of which, so no document was matched by guesswork.
- Cleaned: embedded tweets and posts removed (third-party material, and on translated pages most were left in English; 7,908 in November 2025); email addresses and phone numbers replaced by
<email>and<phone>on every side; error pages, withdrawn releases and repeated festival boilerplate dropped. - Aligned: each release cut into sentences (not after a title such as "Dr." or "डॉ.", or an initial such as "एल."), embedded with LaBSE, and matched in order, one to one, one to two or two to one, by dynamic programming. Pairs below a similarity of 0.70 were dropped. A pair of documents found from both sides was aligned once.
- Filtered: a pair was kept only if each side is mostly in its own script. In November 2025 this dropped 22,673 pairs: links, hashtags, staff initials and English lines left on translated pages, which align because they match themselves; number-only table cells; and Hindi speeches quoted on English pages.
- Checked: every email address and phone number form is searched for again in each month, independently of the rules that removed them, in every script's digits (
audit.py). A month with any left is not published.
How much is machine translation
Google Translate translated the English of up to 100 pairs per office and language. A pair whose PIB text is within chrF 90 of Google's counts as a copy. Two independent translations of one sentence almost never come that close: the sarvam-30b model and Google did on 2 of 200 Assamese sentences, and Google and IN22's human translations on 0 of 89 across six languages.
| Office, language | November 2018 | July 2019 | May 2021 | August 2023 | November 2025 |
|---|---|---|---|---|---|
| Agartala, Bengali | 2% | ||||
| Ahmedabad, Gujarati | 7% | 7% | 7% | 30% | 36% |
| Bengaluru, Kannada | 3% | 7% | 3% | 12% | 15% |
| Bhubaneswar, Odia | 9% | 5% | 7% | ||
| Chandigarh, Punjabi | 3% | 8% | 3% | 17% | |
| Chennai, Tamil | 2% | 5% | 2% | 5% | 5% |
| Gangtok, Nepali | 0% (of 79) | 13% | |||
| Guwahati, Assamese | 0% (of 57) | 0% (of 1) | 48% (of 83) | 30% (of 64) | |
| Hyderabad, Telugu | 0% | 0% (of 31) | 0% (of 10) | 7% (of 68) | 0% (of 77) |
| Kolkata, Bengali | 2% | 0% (of 22) | 0% (of 45) | 0% (of 41) | 0% (of 88) |
| Mumbai, Marathi | 1% | 1% | 9% | 1% | 7% |
| Shillong, Khasi | 12% (of 24) | ||||
| Thiruvananthapuram, Malayalam | 1% | 0% (of 1) | 4% | 13% | |
| Delhi's Assamese translations | 0% (of 43) | 6% | 31% | 23% | |
| Delhi's Bengali translations | 1% | 3% | 4% | 2% | |
| Delhi's Konkani translations | 13% | ||||
| Delhi's Hindi translations | 5% | 22% | 17% | 29% | |
| Delhi's Khasi translations | 0% (of 19) | 5% | |||
| Delhi's Kannada translations | 0% (of 4) | ||||
| Delhi's Malayalam translations | 0% | 18% | 25% | 18% | |
| Delhi's Marathi translations | 1% | 6% | 4% | 2% | |
| Delhi's Nepali translations | 3% | 9% | |||
| Delhi's Punjabi translations | 0% (of 7) | ||||
| Delhi's Telugu translations | 2% | 2% | 0% | 4% | |
| Delhi's Urdu translations | 3% | 4% | 22% | 7% |
Samples are 100 pairs unless stated; a blank cell was not measured, or the office linked no translations that month. November 2018 is a sample of ten offices, not a published month. The rate is a lower bound on machine text: a heavily edited Google translation, or another engine, passes as "not a copy". PIB's pages are set up to load Bhashini's machine-translation plugin; text made with it would not show as a copy of Google's. Google today also writes differently from Google years ago, so an older month's rate is a lower bound twice over.
What this means for a test set. A score against a Google-made reference favours Google. Use the pairs where google_copy is false, or the offices with the lowest rates (Kolkata, Hyderabad, Chennai, Mumbai), or both. google_copy is known for 6,237 sampled pairs (563 copies), not for every pair: Google's output itself is not in this dataset, as its terms forbid passing it on.
Where PIB links its translations
This method needs PIB to link a release to its translation. Ten sampled releases per office, November of each year:
| Office | 2017 | 2018 | 2019 | 2020 to 2024 |
|---|---|---|---|---|
| Ahmedabad, Bengaluru, Chennai | 10/10 | 10/10 | 9-10/10 | 10/10 |
| Hyderabad | 7/10 | 10/10 | 10/10 | 10/10 |
| Guwahati, Kolkata, Mumbai, Thiruvananthapuram | 0/10 | 10/10 | 10/10 | 10/10 |
| Chandigarh | 0/10 | 0/10 | 9/10 | 10/10 |
| Bhubaneswar | 0/10 | 0/10 | 0/10 | 10/10 |
| Delhi (English linking any translation) | 0/10 | 5/10 | 8/10 | 10/10 |
| Agartala, Gangtok, Mumbai Konkani, Shillong | no releases until 2023 | linked from 2023 |
From 2020 every office links its translations, so the pipeline covers 2020 onward as built. Before that, the regional pages link their English more often than the English pages link their translations, so crawl from the regional side. Releases that link nothing need retrieval, which the pipeline does not do.
Limits
- A model can have seen it. PIB's text is in many training sets.
- Machine translation in the references, as measured above.
- LaBSE's blind spots. It does not know Manipuri or Khasi and knows Konkani only through Marathi; their pairs are marked
low, and Manipuri's are weak (30 of 330 at 0.85 or more in November 2025). For every language, a similarity of 0.70 to 0.85 holds more partial and wrong pairs than above 0.85. - What the cleaning missed. Tweets pasted into a release as plain paragraphs, rather than embedded, cannot be told from prose (8 of 10,265 pages in November 2025). A Latin-script translation (Khasi) cannot be told from English by its script, so an English line on a Khasi page can remain.
officeis the crawl pass that found the pair, not always the office that translated it: Delhi's English releases link translations that regional offices post.posted_bynames the publisher of the release the pass started from.
Terms
The text is PIB's. PIB's Copyright Policy, read on 4 October 2026:
Material featured on this website may be reproduced free of charge and there is no need for any prior approval for using the content. The permission to reproduce this material shall not extend to any third-party material. Authorisation to reproduce such material must be obtained from the departments/copyright holders concerned. The material must be reproduced accurately and not used in a derogatory manner or in a misleading context. Wherever the material is being published or issued to others, the source must be prominently acknowledged.
So: name PIB as the source, and do not describe these translations as human: some are near copies of Google Translate. Embedded third-party posts were removed for this reason.
The annotations (the sentence alignment, similarities, number and script checks, google_copy, occurrences) are licensed CC BY 4.0. The code is MIT, at github.com/micahchoo/pib-parallel.
Cite
@misc{pib_parallel_2026,
title = {{PIB Parallel}: English and Indian languages from the Press Information Bureau},
author = {Micah},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/micahchoo/pib-parallel}},
note = {Text: Press Information Bureau, Government of India. Pipeline: \url{https://github.com/micahchoo/pib-parallel}}
}