--- annotations_creators: - no-annotation language_creators: - found language: - nl - en license: - odc-by multilinguality: - monolingual - en-nl size_categories: - n<1K - 1K_en_nl` that interleaves `nl` and `en` examples from the cleaned `en` variant of C4. You can load any config like this: ```python from datasets import load_dataset datasets = load_dataset('yhavinga/mc4_nl_cleaned', 'tiny', streaming=True) print(datasets) ``` This will print ``` DatasetDict({ train: Dataset({ features: ['text', 'timestamp', 'url'], num_rows: 6303893 }) validation: Dataset({ features: ['text', 'timestamp', 'url'], num_rows: 16189 }) }) ``` Since the configs 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_nl_full_stream = load_dataset('yhavinga/mc4_nl_cleaned', "full", split='train', streaming=True) print(next(iter(mc4_nl_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 ### Social Impact of Dataset With more than 151GB (58GB compressed) of cleaned Dutch text and more than 23B estimated words, this is by far the largest available cleaned corpus for the Dutch language. The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 39GB in size for its deduplicated variant, and contains vulgarity. Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language. This can in turn have important repercussions for the development of commercial language technology applications for the Dutch language. ### 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 ### 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 ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` ### Contributions Thanks to [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com), [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for providing the `cleaned_it_mc4` example that shows how upload a dataset to the Huggingface hub.