From_Shloka_to_Shobda_Corpus

From Shloka to Shobda Corpus is a manually curated Sanskrit–Bengali parallel corpus for low-resource neural machine translation (NMT), containing 38,605 sentence-aligned Sanskrit–Bengali pairs compiled from five sources: Bhagavad Gita (5,885 pairs), Bible (7,838 pairs), Spoken Tutorial (23,835 pairs, derived from the Saamayik Sanskrit–English repository via an English-intermediate Bengali translation pipeline), Buddhacharitam (709 pairs), and Ramacharitam (338 pairs), the latter two digitized from printed editions via OCR followed by manual expert correction. This release (v1.1) provides, for each source, both the raw collected text files and cleaned text files (with embedded URLs and formatting artifacts removed), a reproducible processing script (code.py) that regenerates the cleaned files and a per-source parallel_corpus.xlsx, and a corpus-wide metadata.xlsx containing a unique sentence_id, domain label (Religious / Literary / Educational), collection method, Bengali translation method, license/reuse status, word and character counts, and a train/validation/test split assignment for every sentence pair. A fixed-seed (42), source-stratified 80:10:10 train/validation/test split is provided as both a metadata column and standalone ID list files (split_train_ids.txt, split_validation_ids.txt, split_test_ids.txt). A cleaning_report.csv documents, per source, how many pairs contained URLs or triple-quotation-mark formatting artifacts prior to cleaning. All Sanskrit and Bengali sentence pairs are aligned at the sentence level (line n in the Sanskrit file corresponds to line n in the Bengali file, and to the matching sentence_id in metadata.xlsx). Sentence-level quality was validated on a stratified sample (first 100 pairs of every 1,000) by a domain expert in Sanskrit and Bengali linguistics. The dataset supports research in low-resource NMT, multilingual transfer learning, OCR-assisted NLP for Indic languages, and Sanskrit–Bengali cross-lingual studies. License: CC BY 4.0 for the original compilation; the Spoken Tutorial subset's underlying Saamayik source should be checked independently for its own license terms.