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RSL-SHRUTI Sangraha — Cleaned Classical Indian Texts
SHRUTI (श्रुति — That Which Is Heard) Sangraha is a quality-verified multilingual corpus of classical Indian texts, cleaned and prepared for NLP and educational applications.
High-quality, verified classical text data across 6 Indian languages — ready for tokenizer training, language modeling, and IKS research.
Dataset Summary
| Property | Value |
|---|---|
| Languages | Sanskrit, Hindi, Tamil, Telugu, Kannada, Malayalam |
| Total Files | 6 (one per language, verified versions) |
| Total Size | ~718 MB |
| Total Lines | ~1.19 million |
| Format | Plain text (.txt), UTF-8, one passage per line |
| Source | Public-domain classical texts |
| License | CC BY-NC 4.0 |
| Creator | Prof. Santhosh Sivasubramani, INTRINSIC Lab, RSL Foundation, IIT Delhi |
Files
| File | Language | Lines | Size |
|---|---|---|---|
sangraha_sanskrit_verified.txt |
Sanskrit (sa) | 76,066 | 56 MB |
sangraha_hindi_verified.txt |
Hindi (hi) | 212,842 | 127 MB |
sangraha_tamil_verified.txt |
Tamil (ta) | 237,990 | 135 MB |
sangraha_telugu_verified.txt |
Telugu (te) | 209,850 | 133 MB |
sangraha_kannada_verified.txt |
Kannada (kn) | 240,901 | 132 MB |
sangraha_malayalam_verified.txt |
Malayalam (ml) | 217,144 | 136 MB |
Note: We ship the "verified" variants. Each file has been quality-checked beyond initial cleaning to ensure accurate script, remove OCR artifacts, and validate passage boundaries.
Processing Pipeline
- Source collection — Classical texts gathered from public-domain digital libraries
- Cleaning — Script normalization, encoding fixes, whitespace standardization
- Verification — Manual spot-check + automated validation for script correctness, duplicate removal, and passage boundary integrity
- Output — One line per passage, UTF-8 encoded
How to Use
from datasets import load_dataset
# Load the full corpus
ds = load_dataset("RSL-INTRINSICLab-IIT/RSL-SHRUTI-Sangraha")
# Or load individual language files directly
with open("sangraha_tamil_verified.txt", encoding="utf-8") as f:
passages = f.readlines()
print(f"Tamil passages: {len(passages)}")
Use Cases
- Tokenizer training — Used to train RSL-BHARATI-v3 (32K vocab, 7-language IKS tokenizer)
- Language modeling — Pre-training or fine-tuning on classical Indian language data
- IKS NLP research — Named entity recognition, topic modeling, or text classification on classical texts
- Educational — Reference corpus for Indian language pedagogy and curriculum development
Data Provenance
The six text files distributed in this dataset are sourced exclusively from the AI4Bharat Sangraha project's "verified" split (Apache 2.0 license).
| File | Language | Source | License |
|---|---|---|---|
sangraha_sanskrit_verified.txt |
Sanskrit (sa) | AI4Bharat Sangraha — verified | Apache 2.0 |
sangraha_hindi_verified.txt |
Hindi (hi) | AI4Bharat Sangraha — verified | Apache 2.0 |
sangraha_tamil_verified.txt |
Tamil (ta) | AI4Bharat Sangraha — verified | Apache 2.0 |
sangraha_telugu_verified.txt |
Telugu (te) | AI4Bharat Sangraha — verified | Apache 2.0 |
sangraha_kannada_verified.txt |
Kannada (kn) | AI4Bharat Sangraha — verified | Apache 2.0 |
sangraha_malayalam_verified.txt |
Malayalam (ml) | AI4Bharat Sangraha — verified | Apache 2.0 |
The Sangraha verified split contains web-crawled text from human-verified websites, OCR-extracted content from high-quality PDFs, and transcribed material from audio/video — all in classical Indian languages.
Note: Other text sources used elsewhere in the BODHAN AI project (Thirukkural, Bhagavad Gita, Sangam literature, Wikipedia, Gutenberg) are not part of this dataset. They are used in NanoGPT pre-training and are documented in their respective repositories.
Cleaning Pipeline
All raw Sangraha text passes through a standardised cleaning pipeline:
- HTML/XML tag removal
- Unicode normalisation (NFC) and encoding fixes
- Duplicate passage removal (exact-match deduplication)
- Short-line filtering (< 20 characters removed)
- Script validation — each language file verified to contain only the expected script
The corpus files distributed here contain the "verified" variant — after all cleaning stages.
Related Resources
- RSL-BHARATI-v3 — Multilingual tokenizer trained on this corpus
- RSL-SHRUTI-Thirukkural — Thirukkural-CBSE curriculum mapping dataset
- RSL-PRAJNA-v2 — IKS teaching quality evaluation benchmark
Citation
@dataset{rsl_shruti_sangraha,
title={RSL-SHRUTI Sangraha: Cleaned Classical Indian Text Corpus in 6 Languages},
author={Sivasubramani, Santhosh},
year={2026},
institution={INTRINSIC Lab, RSL Foundation, IIT Delhi},
url={https://huggingface.co/datasets/RSL-INTRINSICLab-IIT/RSL-SHRUTI-Sangraha}
}
License
CC BY-NC 4.0 — Free for research and educational use. Commercial use requires a license from IIT Delhi.
Acknowledgment
Demonstrated at the Bharat Bodhan AI Conclave, anchored and driven by the Ministry of Education and IIT Madras, New Delhi.
Contact
Prof. Santhosh Sivasubramani Director, INTRINSIC Laboratory RSL Foundation, Centre for SeNSE, IIT Delhi ssivasub@iitd.ac.in https://intrinsic.iitd.ac.in
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