Datasets:
File size: 11,069 Bytes
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---
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
- config_name: synthetic
features:
- name: doc_id
dtype: string
- name: text
dtype: string
splits:
- name: asm_Beng
- name: asm_Latn
- name: ben_Beng
- name: ben_Latn
- name: guj_Gujr
- name: guj_Latn
- name: hin_Deva
- name: hin_Latn
- name: kan_Knda
- name: kan_Latn
- name: mal_Mlym
- name: mal_Latn
- name: mar_Deva
- name: mar_Latn
- name: npi_Deva
- name: npi_Latn
- name: ory_Orya
- name: ory_Latn
- name: pan_Guru
- name: pan_Latn
- name: san_Deva
- name: san_Latn
- name: tam_Taml
- name: tam_Latn
- name: tel_Telu
- name: tel_Latn
- name: urd_Arab
- name: urd_Latn
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
- config_name: synthetic
data_files:
- split: asm_Beng
path: synthetic/asm_Beng/*.parquet
- split: asm_Latn
path: synthetic/asm_Latn/*.parquet
- split: ben_Beng
path: synthetic/ben_Beng/*.parquet
- split: ben_Latn
path: synthetic/ben_Latn/*.parquet
- split: guj_Gujr
path: synthetic/guj_Gujr/*.parquet
- split: guj_Latn
path: synthetic/guj_Latn/*.parquet
- split: hin_Deva
path: synthetic/hin_Deva/*.parquet
- split: hin_Latn
path: synthetic/hin_Latn/*.parquet
- split: kan_Knda
path: synthetic/kan_Knda/*.parquet
- split: kan_Latn
path: synthetic/kan_Latn/*.parquet
- split: mal_Mlym
path: synthetic/mal_Mlym/*.parquet
- split: mal_Latn
path: synthetic/mal_Latn/*.parquet
- split: mar_Deva
path: synthetic/mar_Deva/*.parquet
- split: mar_Latn
path: synthetic/mar_Latn/*.parquet
- split: npi_Deva
path: synthetic/npi_Deva/*.parquet
- split: npi_Latn
path: synthetic/npi_Latn/*.parquet
- split: ory_Orya
path: synthetic/ory_Orya/*.parquet
- split: ory_Latn
path: synthetic/ory_Latn/*.parquet
- split: pan_Guru
path: synthetic/pan_Guru/*.parquet
- split: pan_Latn
path: synthetic/pan_Latn/*.parquet
- split: san_Deva
path: synthetic/san_Deva/*.parquet
- split: san_Latn
path: synthetic/san_Latn/*.parquet
- split: tam_Taml
path: synthetic/tam_Taml/*.parquet
- split: tam_Latn
path: synthetic/tam_Latn/*.parquet
- split: tel_Telu
path: synthetic/tel_Telu/*.parquet
- split: tel_Latn
path: synthetic/tel_Latn/*.parquet
- split: urd_Arab
path: synthetic/urd_Arab/*.parquet
- split: urd_Latn
path: synthetic/urd_Latn/*.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
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="<subset_name>")
# 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="<subset_name>/<lang_code>")
# 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:
```
@article{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},
journal = {arXiv preprint arXiv: 2403.06350}
}
```
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