Datasets:

dukeofmtnview
/

sanskrit-metrical-poetry

Languages:

Sanskrit

Tags:

sanskrit

poetry

meter

License:

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xet

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The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
data: list<item: struct<text: string, meter: string, topic: string, pattern: string, source: string, data_type: string, difficulty: string, description: string>>
  child 0, item: struct<text: string, meter: string, topic: string, pattern: string, source: string, data_type: string, difficulty: string, description: string>
      child 0, text: string
      child 1, meter: string
      child 2, topic: string
      child 3, pattern: string
      child 4, source: string
      child 5, data_type: string
      child 6, difficulty: string
      child 7, description: string
text: null
meter: null
topic: null
pattern: null
source: null
data_type: null
to
{'text': Value(dtype='string', id=None), 'meter': Value(dtype='string', id=None), 'topic': Value(dtype='string', id=None), 'pattern': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'data_type': Value(dtype='string', id=None)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2270, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1888, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2215, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              data: list<item: struct<text: string, meter: string, topic: string, pattern: string, source: string, data_type: string, difficulty: string, description: string>>
                child 0, item: struct<text: string, meter: string, topic: string, pattern: string, source: string, data_type: string, difficulty: string, description: string>
                    child 0, text: string
                    child 1, meter: string
                    child 2, topic: string
                    child 3, pattern: string
                    child 4, source: string
                    child 5, data_type: string
                    child 6, difficulty: string
                    child 7, description: string
              text: null
              meter: null
              topic: null
              pattern: null
              source: null
              data_type: null
              to
              {'text': Value(dtype='string', id=None), 'meter': Value(dtype='string', id=None), 'topic': Value(dtype='string', id=None), 'pattern': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'data_type': Value(dtype='string', id=None)}
              because column names don't match

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Sanskrit Metrical Poetry Dataset

This dataset contains examples and tasks for Sanskrit metrical poetry composition, designed to train models to generate metrically correct Sanskrit poetry.

Dataset Description

Dataset Summary

The Sanskrit Metrical Poetry dataset is designed for training models to compose Sanskrit poetry that adheres to specific metrical patterns (chandas). It contains examples of Sanskrit poems with their metrical analysis and tasks for poetry composition.

Languages

The dataset is in Sanskrit (sa).

Dataset Structure

The dataset contains two main components:

Examples

Examples of Sanskrit poems with their metrical analysis:

{
  "text": "धर्मो रक्षति रक्षितः\nसत्यं वदति सर्वदा।\nज्ञानं ददाति विनयं\nविद्या ददाति पात्रताम्॥",
  "meter": "अनुष्टुभ्",
  "topic": "ज्ञानम्",
  "source": "Traditional",
  "pattern": "LGGLGGLG\nLGGLGGLG\nLGGLGGLG\nLGGLGGLG",
  "identified_meters": "{'exact': {'अनुष्टुभ्'}, 'partial': set()}",
  "syllable_info": "{'syllables': ['ध', 'र्मो', 'र', 'क्ष', 'ति', 'र', 'क्षि', 'तः', '\n', 'स', 'त्यं', 'व', 'द', 'ति', 'स', 'र्व', 'दा', '।', '\n', 'ज्ञा', 'नं', 'द', 'दा', 'ति', 'वि', 'न', 'यं', '\n', 'वि', 'द्या', 'द', 'दा', 'ति', 'पा', 'त्र', 'ताम्', '॥'], 'weights': ['L', 'G', 'G', 'L', 'G', 'G', 'L', 'G', '\n', 'L', 'G', 'G', 'L', 'G', 'G', 'L', 'G', '।', '\n', 'L', 'G', 'G', 'L', 'G', 'G', 'L', 'G', '\n', 'L', 'G', 'G', 'L', 'G', 'G', 'L', 'G', '॥']}"
}

Tasks

Tasks for Sanskrit metrical poetry composition:

{
  "meter": "अनुष्टुभ्",
  "topic": "ज्ञानम्",
  "meter_info": {
    "description": "अनुष्टुभ् (Anuṣṭubh) is a common meter with 8 syllables per quarter (pāda).",
    "pattern": "LGGLGGLG"
  },
  "difficulty": "medium"
}

Dataset Creation

Source Data

The examples are sourced from traditional Sanskrit literature and poetry.

Annotations

The dataset includes metrical analysis of each poem, including:

Meter identification
Syllable weights (laghu/guru)
Pattern representation

Additional Information

Licensing Information

This dataset is released under the CC BY-SA 4.0 license.

Citation Information

@misc{sanskrit-metrical-poetry,
  author = {Sanskrit Coders},
  title = {Sanskrit Metrical Poetry Dataset},
  year = {2025},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/sanskrit-coders/chandas}}
}

Contributions

Thanks to the Sanskrit Coders community for their contributions to this dataset.

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