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Datasets:
jiviteshjn
/
hi-proverbs-cpt

Tasks:
Text Generation
Languages:
Hindi
Size:
100K<n<1M
Tags:
hindi
proverbs
idioms
lokokti
cultural-knowledge
continued-pretraining
License:
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
text: string
url: string
timestamp: string
source: string
source_file: string
shard: int64
line: int64
matched_idioms: list<item: string>
  child 0, item: string
original_text_chars: int64
to
{'text': Value('string'), 'url': Value('string'), 'timestamp': Value('timestamp[s]'), 'source': Value('string'), 'shard': Value('int64'), 'line': Value('int64'), 'matched_idioms': List(Value('string')), 'original_text_chars': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              text: string
              url: string
              timestamp: string
              source: string
              source_file: string
              shard: int64
              line: int64
              matched_idioms: list<item: string>
                child 0, item: string
              original_text_chars: int64
              to
              {'text': Value('string'), 'url': Value('string'), 'timestamp': Value('timestamp[s]'), 'source': Value('string'), 'shard': Value('int64'), 'line': Value('int64'), 'matched_idioms': List(Value('string')), 'original_text_chars': Value('int64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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text
string
url
string
timestamp
timestamp[s]
source
string
shard
int64
line
int64
matched_idioms
list
original_text_chars
int64
उच्चारण: गीत "फटी घाघरा-चोली" (डॉ.रूपचन्द्र शास्त्री 'मयंक') Labels: गीत, फटी घाघरा-चोली Onkar 13 अगस्त 2017 को 8:40 am -अच्छा लगता है (1) -सन्देश- (2) :स्वर-अर्चना चावजी का (3) !!रावण या रक्तबीज!! (1) ''धान खेत में लहराते" (1) 'आप' का अन्दाज़ बिल्कुल 'आप' सा (1) 'सिफत' के लिए शुभाशीष (1) ‘‘चम्पू छन्द’’ (1) ‘‘बाल-गीत’’...
http://uchcharan.blogspot.com/2017/08/blog-post_12.html
2017-08-20T15:10:05
mc4-hi
0
114
[ "काला अक्षर भैंस बराबर", "कुदरत की मार, ख़बर न सार", "थाली के बैंगन" ]
53,841
Qayamat aur insaaf ~ सच्च का आईना इस्लाम क्या है ? Qayamat aur insaaf Qayamat aur insaaf कयामत ( इंसाफ का दिन ) * इस लेख हम जानने की कोशिश करेंगे कि कयामत या फिर क़ियामत काहा जाता है । और महशर का ख़ौफ़ नाक मंजर ? * कयामत का मतलब होता है ।उठने का दिन , आख़िरत , जजमेंट डे इत्यादि । और । NOTE : - लेख काफी बड़ा हो सकता है , परं...
https://www.sachkaaina.com/2019/08/judgement-day.html
2020-04-06T09:07:51
mc4-hi
0
136
[ "आत्मा परमात्मा" ]
14,872
न्याय की गुहार लगाते-लगाते मौत की नींद सो गयी किरण, नहीं जगा सिस्टम - MadhepuraTimes Home / Madhepura / न्याय की गुहार लगाते-लगाते मौत की नींद सो गयी किरण, नहीं जगा सिस्टम न्याय की गुहार लगाते-लगाते मौत की नींद सो गयी किरण, नहीं जगा सिस्टम एक बहुत पुरानी कहावत है नीम हकीम खतरा-ए-जान। इसका अर्थ यह है कि ऐसा व्यक्ति जिसक...
https://www.madhepuratimes.com/2017/11/kirankilled-by-doctors.html
2019-02-16T02:50:54
mc4-hi
0
245
[ "नीम हकीम खतरा-ए-जान" ]
2,624
"कहानी / नायं हन्ति न हन्यते ! / राहुल देव(...TRUNCATED)
https://www.rachanakar.org/2016/04/blog-post_890.html
2020-08-15T05:50:59
mc4-hi
0
512
[ "यथा नाम तथा गुण" ]
10,320
"Deendayal sharma: September 2010\nनन्ही कवितायेँ - 2 / दीनदया(...TRUNCATED)
http://deendayalsharma.blogspot.com/2010_09_01_archive.html
2018-02-18T08:50:32
mc4-hi
0
598
["आँधी के आम","चोली दामन का साथ है","छठी का (...TRUNCATED)
1,690
"Omkar....: January 2017\nश्रमिक वर्ग की, जनसमाज की से(...TRUNCATED)
http://goodmorninglife24.blogspot.com/2017/01/
2018-07-19T13:00:15
mc4-hi
0
616
[ "सत्य को सदैव विजय होती है" ]
16,593
"“अंधेरों की रानी” “Queen Of Dark” – Nyakim Gatwech – The Happ(...TRUNCATED)
http://www.justhappyminds.com/queen-of-dark-nyakim-gatwech/
2018-09-19T15:24:09
mc4-hi
0
618
[ "जैसे के तेसे" ]
6,310
"घीसा - महादेवी वर्मा - साहित्य विमर्श\n(...TRUNCATED)
https://www.hindi-literature.com/gheesa-mahadevi-varma.html/
2018-06-19T21:38:32
mc4-hi
0
796
["जैसे के तेसे","न रहेगा बाँस न बजेगी बाँ(...TRUNCATED)
20,087
"विविध: October 2015\nगाय रामजी की हम लालूजी के\n(...TRUNCATED)
http://ohmygod-rajeev.blogspot.com/2015_10_01_archive.html
2018-01-21T00:42:15
mc4-hi
0
817
[ "काला अक्षर भैंस बराबर" ]
7,363
"विकास दुबे के दो और साथियों का एनकाउं(...TRUNCATED)
https://firstindianews.com/news/Two-more-colleagues-of-Vikas-Dubey-encounter-Prabhat-in-Kanpur-and-Ranbir-killed-in-Etawah-103650722
2020-08-04T11:21:14
mc4-hi
0
968
[ "सबके दाता राम" ]
10,636
End of preview.

Hindi Proverbs — Idiom-Tagged Continued-Pretraining Corpus

A 338K-document Hindi corpus (~1.4B tokens) for continued pretraining on cultural knowledge in figurative language, plus a structured dataset of 16,617 Hindi proverbs (लोकोक्तियाँ) with meanings, recovered via OCR-repair from a classic proverb dictionary. Each corpus document is natural Hindi text containing at least one proverb (matched including common surface variants), with an appended knowledge block listing every matched proverb and its meaning.

Built 2026-07-17/18 as part of the CultureInFigurativeLanguage project (companion to the Chinese corpus built from chengyu + mC4 zh).

Key numbers

Corpus documents 338,035 (135,453 mC4 hi + 117,719 fineweb-2 hin_Deva + 84,863 IndicCorp v2)
Tokens (Qwen3.5 tokenizer, per-source measured) ~1.37B (mC4 0.82B + fineweb-2 0.44B + IndicCorp 0.11B)
Proverb annotations 365,311
Proverb dataset 16,617 entries (16,590 unique) with meanings + entities
Surface-variant patterns used in matching 14,432 (across 9,179 proverbs)
Cross-language parallels 9,698 entries with parallels in 40+ languages
Distinct proverbs occurring in the corpus 2,124
Per-proverb document cap 10,000 (rare-first selection)

Contents

  • data/tagged_*.json.gz — the corpus. One JSON per line: {"text": "<document>\n\nलोकोक्तियों के अर्थ:\n<proverb> — <meaning>...", "url", "timestamp", "source": "mc4-hi"|"indiccorp-hi"|"fineweb2-hi", "shard", "line", "matched_idioms": [<canonical proverbs>], "original_text_chars"}
  • idioms/idioms_hi_llm_formatted.jsonl — the proverb dataset: {"idiom", "index", "source_index", "output": {"idiom", "entities", "literal_meanings" (empty for Hindi), "figurative_meanings"}}
  • idioms/idiom_variants_hi.jsonl — common surface variants used for matching: {"canonical", "index", "variants": [...]} (see below).
  • idioms/cross_language_parallels_hi.jsonl — the dictionary's तुलनीय blocks, structured: parallels of each proverb in Punjabi, Bhojpuri, Sanskrit, Rajasthani, Marathi, and ~40 other languages (all in Devanagari as printed).
  • idioms/cross_references_hi.jsonl — 2,019 दे० variant-form pointers.
  • idioms/entries_raw.jsonl, idioms/repair_decisions_hi.jsonl, idioms/variant_decisions_hi.jsonl — full provenance: rule-segmented OCR entries and every LLM repair/variant decision.
  • stats/ — extraction/selection/tagging summaries and per-proverb kept counts.

How it was built

  1. Proverb dataset from an OCR'd dictionary. Source: A Comprehensive Dictionary of Hindi Proverbs (बृहत् हिंदी लोकोक्ति कोश, ed. Bholanath Tiwari & Nur Nabi Abbasi), Digital Library of India scan (archive.org: in.ernet.dli.2015.464150), whose metadata states "dc.rights: In Public Domain". The Tesseract OCR text was segmented by rules into 15,200 entries, then repaired and structured by an LLM (gpt-5.4-mini) instructed to fix OCR errors conservatively, split glued entries, and DROP entries it could not confidently reconstruct (1,298 dropped). Every decision is included.
  2. Surface-variant generation for recall. Dictionary headwords use archaic morphology that modern text does not (होय vs होता है); an LLM generated commonly-occurring variants (gender/number agreement, verb modernization, postpositions, spelling, attested short forms; the dictionary's own दे० cross-reference forms were OCR-repaired in the same pass). Mechanical anti-invention validation (word-overlap and length guards, ambiguity dedup) kept 14,432 of 26,001 proposed variants. Variants match in the corpus but always report the canonical proverb.
  3. Corpus extraction. Two sources scanned with Aho-Corasick matching over Devanagari-normalized text (indic-nlp-library core: nukta removal, nasal-conjunct→anusvara; plus chandrabindu folding and punctuation/ whitespace collapsing): the mC4 hi train split (18.5M docs), HuggingFaceFW/fineweb-2 hin_Deva (22.1M docs, Common Crawl 2013-2024, ODC-BY; ~47% of its matched docs were near-duplicates of mC4 content and were removed by cross-source dedup, the remainder being largely 2021-2024 content mC4 predates) and AI4Bharat's IndicCorp v2 hi (6.1B tokens of verified news crawls, CC-0). Quality gates: 150–100,000 chars, ≤20 distinct proverbs per doc, minimum pattern length.
  4. Cross-source dedup + selection. Exact (blake2b) + MinHash-LSH near-dedup globally across both sources (~13K duplicates removed, including mC4↔IndicCorp overlap), then rare-proverb-first capped selection.
  5. Tagging. Each document gets a लोकोक्तियों के अर्थ: block listing its proverbs with up to 2 deduplicated meanings from the dictionary.

Usage 

from datasets import load_dataset
ds = load_dataset("jiviteshjn/hi-proverbs-cpt", data_files="data/*.json.gz", split="train")

hf download jiviteshjn/hi-proverbs-cpt --repo-type dataset --local-dir hi_proverbs_cpt

Documents average ~6K tokens (mC4 pages are long); sequence packing recommended.

Provenance & licenses

  • Corpus text: allenai/c4 multilingual hi (ODC-BY; inherits Common Crawl content caveats) and ai4bharat/IndicCorpV2 hi (CC-0, human-verified news sources).
  • Proverb dictionary: Digital Library of India scan declared In Public Domain (see above). OCR errors were repaired conservatively; residual OCR noise may remain — the decisions files document every transformation.
  • Compilation released under ODC-BY (the most restrictive component). Intended for research on cultural alignment of language models.

Known limitation: even with variant matching, only 2,124 of the 16,590 proverbs occur in web text — the dictionary's archaic/rural tail is absent from modern corpora. The proverb dataset itself covers all 16,617 entries regardless.
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