--- license: cc-by-4.0 task_categories: - text-generation language: - as - bn - gu - en - hi - kn - ks - ml - mr - ne - or - pa - sa - sd - ta - te - ur tags: - language-modeling - casual-lm - llm pretty_name: sangraha dataset_info: - config_name: verified features: - name: doc_id dtype: string - name: type dtype: string - name: text dtype: string splits: - name: asm - name: ben - name: brx - name: doi - name: eng - name: gom - name: guj - name: hin - name: kan - name: kas - name: mai - name: mal - name: mar - name: mni - name: nep - name: ori - name: pan - name: san - name: sat - name: snd - name: tam - name: tel - name: urd - config_name: unverified features: - name: doc_id dtype: string - name: text dtype: string splits: - name: asm - name: ben - name: guj - name: hin - name: kan - name: mal - name: mar - name: nep - name: ori - name: pan - name: san - name: tam - name: tel - name: urd configs: - config_name: verified data_files: - split: asm path: verified/asm/*.parquet - split: ben path: verified/ben/*.parquet - split: brx path: verified/brx/*.parquet - split: doi path: verified/doi/*.parquet - split: eng path: verified/eng/*.parquet - split: gom path: verified/gom/*.parquet - split: guj path: verified/guj/*.parquet - split: hin path: verified/hin/*.parquet - split: kan path: verified/kan/*.parquet - split: kas path: verified/kas/*.parquet - split: mai path: verified/mai/*.parquet - split: mal path: verified/mal/*.parquet - split: mar path: verified/mar/*.parquet - split: mni path: verified/mni/*.parquet - split: nep path: verified/nep/*.parquet - split: ori path: verified/ori/*.parquet - split: pan path: verified/pan/*.parquet - split: san path: verified/san/*.parquet - split: sat path: verified/sat/*.parquet - split: snd path: verified/snd/*.parquet - split: tam path: verified/tam/*.parquet - split: tel path: verified/tel/*.parquet - split: urd path: verified/urd/*.parquet - config_name: unverified data_files: - split: asm path: unverified/asm/*.parquet - split: ben path: unverified/ben/*.parquet - split: guj path: unverified/guj/*.parquet - split: hin path: unverified/hin/*.parquet - split: kan path: unverified/kan/*.parquet - split: mal path: unverified/mal/*.parquet - split: mar path: unverified/mar/*.parquet - split: nep path: unverified/nep/*.parquet - split: ori path: unverified/ori/*.parquet - split: pan path: unverified/pan/*.parquet - split: san path: unverified/san/*.parquet - split: tam path: unverified/tam/*.parquet - split: tel path: unverified/tel/*.parquet - split: urd path: unverified/urd/*.parquet size_categories: - 100B

Sangraha is the largest high-quality, cleaned Indic language pretraining data containing 251B tokens summed up over 22 languages, extracted from curated sources, existing multilingual corpora and large scale translations. **Coming Soon**: - Sangraha Synthetic - Translated and Romanised English Wikimedia data. - Sangraha Verified - Hindi YouTube transcribed data. **More information**: - For detailed information on the curation and cleaning process of Sangraha, please checkout our paper [on Arxiv](https://arxiv.org/abs/2403.06350); - Check out the scraping and cleaning pipelines used to curate Sangraha [on GitHub](https://github.com/AI4Bharat/IndicLLMSuite); ## Getting Started For downloading the entire Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha") ``` For downloading a subset (Verified/Unverified) of Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha", data_dir="") # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified") ``` For downloading one language from a subset of Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha", data_dir="/") # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified/asm") ``` ## Background Sangraha contains three broad components: - **Sangraha Verified**: Containing scraped data from "human-verified" Websites, OCR-extracted data from high quality Indic language PDFs, transcribed data from various Indic language videos, podcasts, movies, courses, etc. - **Sangraha Unverfied**: High quality Indic language data extracted from existing multilingual corpora employing perplexity filtering using n-gram language models trained on Sangraha Verified. - **Sangraha Synthetic**: WikiMedia English translated to 14 Indic languages and further "romanised" from 14 languages by transliteration to English. ## Data Statistics | **Lang Code** | **Verified** | **Synthetic** | **Unverified** | **Total Tokens (in Millions)** | | ------------- | ------------ | ------------- | -------------- | ------------------------------ | | asm | 292.1 | 11,696.4 | 17.5 | 12,006.0 | | ben | 10,604.4 | 13,814.1 | 5,608.8 | 30,027.5 | | brx | 1.5 | - | - | 1.5 | | doi | 0.06 | - | - | 0.06 | | eng | 12,759.9 | - | - | 12,759.9 | | gom | 10.1 | - | - | 10.1 | | guj | 3,647.9 | 12,934.5 | 597.0 | 17,179.4 | | hin | 12,617.3 | 9,578.7 | 12,348.3 | 34,544.3 | | kan | 1,778.3 | 12,087.4 | 388.8 | 14,254.5 | | kas | 0.5 | - | - | 0.5 | | mai | 14.6 | - | - | 14.6 | | mal | 2,730.8 | 13,130.0 | 547.8 | 16,408.6 | | mar | 2,827.0 | 10,816.7 | 652.1 | 14,295.8 | | mni | 7.4 | - | - | 7.4 | | npi | 1,822.5 | 10,588.7 | 485.5 | 12,896.7 | | ori | 1,177.1 | 11,338.0 | 23.7 | 12,538.8 | | pan | 1,075.3 | 9,969.6 | 136.9 | 11,181.8 | | san | 1,329.0 | 13,553.5 | 9.8 | 14,892.3 | | sat | 0.3 | - | - | 0.3 | | snd | 258.2 | - | - | 258.2 | | tam | 3,985.1 | 11,859.3 | 1,515.9 | 17,360.3 | | urd | 3,658.1 | 9,415.8 | 1,328.2 | 14,402.1 | | tel | 3,706.8 | 11,924.5 | 647.4 | 16,278.7 | | **Total** | **64,306.1** | **162,707.9** | **24,307.7** | **251,321.0** | To cite Sangraha, please use: ``` @misc{khan2024indicllmsuite, title={IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages}, author={Mohammed Safi Ur Rahman Khan and Priyam Mehta and Ananth Sankar and Umashankar Kumaravelan and Sumanth Doddapaneni and Suriyaprasaad G and Varun Balan G and Sparsh Jain and Anoop Kunchukuttan and Pratyush Kumar and Raj Dabre and Mitesh M. Khapra}, year={2024}, eprint={2403.06350}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```