Rijgersberg/common_corpus_dutch_pd

Common Corpus v2 - Dutch Public Domain collection

This is a version of Common Corpus v2 filtered to keep only the rows where collection is "Dutch-PD". Looking for all Dutch-language documents in Common Corpus, regardless of the collection they are in? Then you might want to look at Rijgersberg/common_corpus_nl.

Common Corpus is a very large open and permissible licensed text dataset created by Pleias. Please be sure to acknowledge the creators of the original dataset when using this filtered version.

Filtering

Common Corpus is a collection of disparate datasets. There is no easy way to select subsets or languages of Common Corpus, so this dataset has extracted the Dutch-PD subset to be used separately.

Usage

from datasets import load_dataset

dataset = load_dataset('Rijgersberg/common_corpus_dutch_pd', split='train')

Contents

The dataset has the following content. Tokens are measured by the robbert-2023-dutch-base Dutch tokenizer on the text column only. word_count is taken directly from the word_count column of the dataset.

collection	open_type	row_count	word_count	token_count
Dutch-PD	Open Culture	219,209	1,461,934,337	2,798,438,458

Code

In principle it is very easy to create the dataset by filtering Common Corpus using Hugging Face datasets' dataset.filter() functionality. However, Common Corpus is larger than my available disk space.

A possible solution is to process Common Corpus streaming and relying on the fact that the Dutch subset will be much, much smaller than the full dataset. The code for that solution is below. However, I was having trouble streaming the entire dataset without any connection errors along the way.

from datasets import load_dataset, Dataset


common_corpus = load_dataset('PleIAs/common_corpus', split='train', streaming=True)

def nl():
    for row in common_corpus:
        if row['collection'] == 'Dutch-PD':
            yield row

common_corpus_nl = Dataset.from_generator(nl)

common_corpus_nl.push_to_hub('Rijgersberg/common_corpus_nl')

Therefore I took the approach for every one of ten subfolders of Common Corpus:

• downloading the subfolder in a fault-tolerant way
• doing the filtering to Dutch-PD rows only
• uploading that by itself to the Hugging Face hub
• deleting all the downloaded files and datasets cache files (around 1.5 TB for every subfolder)

Then finally I concatenated the ten Dutch-PD datasets into a single one, which is the one you are looking at.

import shutil

from datasets import concatenate_datasets, load_dataset
from huggingface_hub import snapshot_download
from huggingface_hub.errors import LocalEntryNotFoundError
from requests import ReadTimeout


local_dir = "/path/to/downloadfolder/commoncorpus"

for i in range(1, 10+1):
    success = False
    while not success:
        try:
            # download one common corpus folder at a time to a local directory
            snapshot_download(  # will skip files that have already been downloaded
                repo_id='PleIAs/common_corpus',
                repo_type='dataset',
                allow_patterns=f'common_corpus_{i}/*',
                local_dir=local_dir
            )
            success = True
        except (LocalEntryNotFoundError, ReadTimeout) as e:
            print(e)

    subdataset = load_dataset(local_dir, split='train')

    dutchpd = subdataset.filter(lambda collection: collection == 'Dutch-PD', input_columns=['collection'])
    dutchpd.push_to_hub(f'Rijgersberg/common_corpus_dutch_pd_{i}')

    # remove local copies of the data to free up disk space
    shutil.rmtree(local_dir)
    shutil.rmtree('/path/to/cache/huggingface/datasets/commoncorpus')

# concatenate all (much smaller) Dutch-PD datasets into a single dataset
common_corpus_nl = concatenate_datasets([load_dataset(f'Rijgersberg/common_corpus_dutch_pd_{i}', split='train')
                                         for i in range(1, 10+1)])
common_corpus_nl.push_to_hub('Rijgersberg/common_corpus_dutch_pd')