---
language:
- id
license: cc0-1.0
task_categories:
- text-generation
pretty_name: IndoCleanCorpus
---

# IndoCleanCorpus

A large-scale cleaned Indonesian text corpus, built from multiple open sources and processed through a multi-stage quality filtering and deduplication pipeline.

## Source Datasets

| Dataset | Description |
|---|---|
| [Lyon28/Corpus-Indonesia](https://huggingface.co/datasets/Lyon28/Corpus-Indonesia) | General Indonesian text corpus |
| [DamarJati/indocorpus-sastra](https://huggingface.co/datasets/DamarJati/indocorpus-sastra) | Indonesian literary corpus |
| [indonesian-nlp/wikipedia-id](https://huggingface.co/datasets/indonesian-nlp/wikipedia-id) | Indonesian Wikipedia dump |

## Processing Pipeline

The raw merged corpus of **19,814,163** documents was cleaned through the following sequential stages:

| Stage | Remaining Docs | Removed |
|---|---|---|
| Raw input | 19,814,163 | — |
| Boilerplate filter | 19,811,935 | 2,228 |
| Length filter | 14,575,984 | 5,235,951 |
| Character ratio filter | 14,377,694 | 198,290 |
| Repetition filter | 14,376,725 | 969 |
| Language filter (Indonesian) | 13,528,694 | 848,031 |
| Exact deduplication | 13,431,107 | 97,587 |
| Near-deduplication | 13,411,142 | 19,965 |
| **Final** | **13,411,132** | **6,403,031** |

**Overall retention rate: 67.68%** — roughly 1 in 3 documents were removed.  
**Pipeline runtime:** ~6.7 hours (23,995 seconds).

### Filter Descriptions

- **Boilerplate filter** — removes templated or auto-generated content (nav menus, footers, repeated headers)
- **Length filter** — removes documents that are too short or too long to be useful
- **Character ratio filter** — removes documents with abnormal ratios of punctuation, digits, or non-alphabetic characters
- **Repetition filter** — removes documents with excessive repeated n-grams or lines
- **Language filter** — retains only documents classified as Indonesian (`id`)
- **Exact deduplication** — removes byte-identical duplicate documents
- **Near-deduplication** — removes near-duplicate documents using MinHash LSH

## Usage

```python
from datasets import load_dataset

ds = load_dataset("AiRukua/IndoCleanCorpus")
```

## Dataset Statistics

- **Final document count:** 13,411,132
- **Format:** Parquet (16 part files, ~2.2 GB total)
- **Language:** Indonesian (id)

## License

Source datasets are openly licensed. This dataset is released under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).