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Datasets:
linhgiang
/
early-buddhist-texts

Tasks:
Translation
Text Retrieval
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
Pāli
English
Size:
100K - 1M
Tags:
buddhism
early-buddhist-texts
pali-canon
tipitaka
suttacentral
bilingual
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Dataset Viewer
Auto-converted to Parquet Duplicate
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
segment_id: string
nikaya_code: string
nikaya_name: string
sutta_id: string
sutta_title_pali: string
sutta_title_en: string
text_pali: string
text_english: string
translator: string
license: string
text_pali_ascii: string
to
{'segment_id': Value('string'), 'nikaya_code': Value('string'), 'nikaya_name': Value('string'), 'sutta_id': Value('string'), 'sutta_title_pali': Value('string'), 'sutta_title_en': Value('string'), 'text_pali': Value('string'), 'text_english': Value('string'), 'translator': Value('string'), 'license': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              segment_id: string
              nikaya_code: string
              nikaya_name: string
              sutta_id: string
              sutta_title_pali: string
              sutta_title_en: string
              text_pali: string
              text_english: string
              translator: string
              license: string
              text_pali_ascii: string
              to
              {'segment_id': Value('string'), 'nikaya_code': Value('string'), 'nikaya_name': Value('string'), 'sutta_id': Value('string'), 'sutta_title_pali': Value('string'), 'sutta_title_en': Value('string'), 'text_pali': Value('string'), 'text_english': Value('string'), 'translator': Value('string'), 'license': Value('string')}
              because column names don't match

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Early Buddhist Texts — Sutta Piṭaka (Pāli Canon & English Alignment)

Overview

This dataset contains the complete, canonical Early Buddhist Texts (Sutta Piṭaka) from the Pāli Tipiṭaka, aligned segment-by-segment with verified English translations by Bhikkhu Sujato (SuttaCentral Bilara Edition).

  • Total Suttas: 5,764 suttas across all 5 Nikāyas:
    • DN (Dīgha Nikāya - Long Discourses)
    • MN (Majjhima Nikāya - Middle Discourses)
    • SN (Saṁyutta Nikāya - Linked Discourses)
    • AN (Aṅguttara Nikāya - Numbered Discourses)
    • KN (Khuddaka Nikāya - Minor Discourses: Dhammapada, Udāna, Itivuttaka, Sutta Nipāta, etc.)
  • Total Aligned Segments: 284,708 bilingual records.
  • Compression: Columnar Apache Parquet with Zstandard (ZSTD) compression (~4.9 MB total).
  • License: CC0 1.0 Universal (Public Domain Dedication) — Free for research, commercial use, and AI training.

Usage

1. Python (Hugging Face Datasets) 

from datasets import load_dataset

# Stream directly without downloading the whole file
dataset = load_dataset("linhgiang/early-buddhist-texts", split="train", streaming=True)

for example in dataset.take(5):
    print(f"[{example['segment_id']}] {example['text_pali']}")
    print(f"  -> {example['text_english']}\n")

2. DuckDB Serverless SQL Query 

INSTALL httpfs; LOAD httpfs;
SELECT segment_id, sutta_title_en, text_pali, text_english 
FROM 'https://huggingface.co/datasets/linhgiang/early-buddhist-texts/resolve/main/early_buddhist_texts.parquet'
WHERE text_english ILIKE '%mindfulness of breathing%'
LIMIT 10;

3. Pandas / PyArrow 

import pandas as pd
df = pd.read_parquet("https://huggingface.co/datasets/linhgiang/early-buddhist-texts/resolve/main/early_buddhist_texts.parquet")
print(df.head())

Provenance

Downloads last month
75
Number of rows:
284,708
Total file size:
51.6 MB