README.md

---
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<n<1T
---

# Sangraha

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/63ef3cd11e695b35aa48bebc/nDnyidcqIOLAP9dTw9GrK.png" />
</p>

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

You can download the dataset using Hugging Face datasets:

```python
from datasets import load_dataset

dataset = load_dataset("ai4bharat/sangraha")
```

## 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}
}
```