Datasets:

indonesian-nlp
/

mc4-id

	---
	annotations_creators:
	- no-annotation
	language_creators:
	- found
	language:
	- id
	license:
	- odc-by
	multilinguality:
	- monolingual
	size_categories:
	tiny:
	- 1M<n<10M
	small:
	- 10M<n<100M
	medium:
	- 10M<n<100M
	large:
	- 10M<n<100M
	full:
	- 100M<n<1B
	source_datasets:
	- extended
	task_categories:
	- text-generation
	task_ids:
	- language-modeling
	paperswithcode_id: mc4
	pretty_name: mC4-id
	---

	# Dataset Card for Clean(maybe) Indonesia mC4

	## Dataset Description

	- Original Homepage: [HF Hub](https://huggingface.co/datasets/allenai/c4)
	- Paper: [ArXiv](https://arxiv.org/abs/1910.10683)

	### Dataset Summary

	A thoroughly cleaned version of the Indonesia split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the [Common Crawl dataset](https://commoncrawl.org). The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4).


	### Data Fields

	The data contains the following fields:

	- `url`: url of the source as a string
	- `text`: text content as a string
	- `timestamp`: timestamp of extraction as a string

	### Data Splits


	You can load any subset like this:

	```python
	from datasets import load_dataset

	mc4_id_tiny = load_dataset("munggok/mc4-id", "tiny")
	```

	Since splits are quite large, you may want to traverse them using the streaming mode available starting from 🤗 Datasets v1.9.0:

	```python
	from datasets import load_dataset

	mc4_id_full_stream = load_dataset("munggok/mc4-id", "full", split='train', streaming=True)
	print(next(iter(mc4_id_full_stream))) # Prints the example presented above
	```

	## Dataset Creation

	Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`.

	## Considerations for Using the Data

	### Discussion of Biases

	Despite the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.

	## Additional Information

	### Dataset Curators

	Authors at AllenAI are the original curators for the `mc4` corpus.

	### Licensing Information

	AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.

	### Citation Information

	If you use this dataset in your work, please cite us and the original mC4 authors as:

	```

	@inproceedings{xue-etal-2021-mt5,
	title = "m{T}5: A Massively Multilingual Pre-trained Text-to-Text Transformer",
	author = "Xue, Linting and
	Constant, Noah and
	Roberts, Adam and
	Kale, Mihir and
	Al-Rfou, Rami and
	Siddhant, Aditya and
	Barua, Aditya and
	Raffel, Colin",
	booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
	month = jun,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.naacl-main.41",
	doi = "10.18653/v1/2021.naacl-main.41",
	pages = "483--498",
	}
	```

	### Contributions

	Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.