Downloading a portion of parquet files

🤗Datasets
1

Hi all,

So I want to download a portion of the refined-web dataset but apart from streaming I couldn’t make it run. So the question to be asked here is how can I download a portion of the refinedweb dataset?

Here are some of the stuff I tried and fail:

subset = load_dataset("tiiuae/falcon-refinedweb", num_proc=10, split="train[%10]")
subset = load_dataset("tiiuae/falcon-refinedweb", num_proc=10, split="train[10:20]")

following these already asked question Subset of split via fraction

these two examples just tried to download the whole dataset.

Then I tried to query the file-list and download it and it resulted in an error:


dataset_name = "tiiuae/falcon-refinedweb"
API_TOKEN = ""
data_files = ""

headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = (
    "https://datasets-server.huggingface.co/parquet?dataset=tiiuae/falcon-refinedweb"
)


def query():
    response = requests.get(API_URL, headers=headers)
    return response.json()


data = query()
no_samples = len(data["parquet_files"])
portion = 0.1
iteration_limit = int(no_samples * portion)

data_files = [data["parquet_files"][ii]["url"] for ii in range(iteration_limit)]

subset = load_dataset(dataset_name, num_proc=10, data_files=data_files)

This resulted in the following error:

raceback (most recent call last):██████████████████████████████████████████████████████████████████████████████████████████████████████▊    | 54/56 [26:20<00:47, 23.95s/files]
  File "load_dataset.py", line 28, in <module>███████████████████████████████████████████| 56/56 [26:50<00:00, 18.33s/files]
    subset = load_dataset("tiiuae/falcon-refinedweb", num_proc=10, data_files=data_files)
  File " python3.10/site-packages/datasets/load.py", line 2609, in load_dataset 53/56 [25:47<01:07, 22.35s/files]
    builder_instance.download_and_prepare(██████████████████████████████████████████████████████████████████████████████████████████████▊    | 54/56 [26:02<00:40, 20.15s/files]
  File " python3.10/site-packages/datasets/builder.py", line 1027, in download_and_prepare32<00:00, 17.41s/files]
    self._download_and_prepare(
  File " python3.10/site-packages/datasets/builder.py", line 1140, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
  File " python3.10/site-packages/datasets/utils/info_utils.py", line 101, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=2766953721769, num_examples=968000015, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=278244261454, num_examples=96730207, shard_lengths=[169000, 177919, 178838, 175919, 172000, 173919, 176838, 180919, 181919, 177919, 178919, 170919, 170919, 180919, 173919, 172919, 174919, 161919, 165000, 168919, 177919, 175919, 174919, 170919, 176919, 173919, 179919, 176919, 184838, 180919, 175919, 169919, 171919, 185919, 169919, 175919, 167919, 172919, 165000, 174919, 174919, 176919, 174919, 170919, 172919, 176919, 178919, 180919, 177838, 177919, 173919, 173919, 181919, 161000, 173919, 172919, 6919, 166000, 164919, 163919, 178919, 177919, 177919, 171919, 176919, 178919, 176919, 177919, 184919, 179838, 175919, 169000, 172919, 175919, 169919, 176919, 174919, 172919, 157919, 175919, 171919, 178919, 173919, 170919, 178919, 179919, 181919, 176919, 181919, 178838, 174919, 170000, 180838, 174919, 170000, 173919, 169919, 168919, 169919, 181919, 176919, 172919, 175919, 178919, 178919, 177919, 177838, 178919, 178919, 175919, 170919, 181919, 160919, 174919, 174000, 173919, 170919, 172919, 175919, 175919, 176919, 170919, 174919, 164919, 176919, 176919, 180919, 178919, 177919, 174919, 171919, 177919, 177838, 175919, 173919, 168000, 166919, 178919, 175919, 175919, 176919, 177919, 162919, 174919, 181919, 177919, 180838, 177919, 178919, 166919, 180919, 172919, 172919, 174919, 172919, 165000, 174919, 179919, 175919, 172919, 167919, 178919, 165919, 175919, 177919, 180919, 177919, 173919, 171919, 7919, 175919, 175919, 176919, 173919, 171919, 169000, 170919, 175919, 177919, 172919, 162919, 176919, 160919, 174919, 179919, 176919, 182919, 175919, 159919, 165919, 181919, 172919, 172919, 174919, 175919, 170919, 176919, 172919, 174919, 174919, 162919, 174919, 175919, 175919, 180919, 179919, 177919, 171919, 172919, 179919, 177919, 173919, 174919, 178919, 163919, 162919, 176919, 173919, 174919, 166919, 180919, 168919, 173919, 176919, 181919, 51919, 177919, 165000, 164919, 175919, 176919, 173919, 174919, 169919, 164919, 156919, 178919, 173919, 171919, 165919, 181919, 167919, 176919, 174919, 176919, 180919, 178919, 168919, 169919, 179919, 172919, 174919, 172919, 168919, 153919, 176919, 174919, 174919, 171919, 164919, 180919, 176919, 175919, 176919, 180919, 177919, 170919, 155919, 169919, 175919, 175919, 176919, 171919, 156919, 173919, 175919, 175919, 172919, 165919, 181919, 177919, 115919, 176919, 176919, 178919, 179919, 160000, 176838, 174919, 175919, 173000, 176838, 174919, 168000, 171919, 177919, 173919, 170919, 178919, 179919, 171919, 178919, 178838, 179919, 176919, 167919, 165000, 178919, 174919, 173919, 173919, 176919, 168919, 160919, 176919, 173919, 170919, 177919, 180919, 178919, 172919, 173919, 181919, 180919, 175919, 173919, 165919, 178919, 178919, 176919, 172919, 172919, 169919, 179919, 170919, 170919, 177919, 10919, 181919, 176919, 171919, 176919, 179919, 178919, 173919, 173919, 164919, 178919, 172919, 173919, 175919, 172919, 167000, 173919, 175919, 175919, 171919, 175919, 181838, 173919, 173919, 179919, 175919, 180919, 172919, 170919, 175919, 171919, 171919, 176919, 173919, 161000, 173919, 175919, 177919, 170919, 176919, 177919, 175919, 176919, 170919, 183838, 179919, 173919, 169000, 167919, 178919, 170919, 177919, 170919, 163919, 162919, 176919, 29919, 178919, 169000, 175919, 179838, 180919, 175919, 172919, 181919, 179919, 173919, 171919, 162919, 177919, 172919, 175919, 173919, 170919, 175919, 174919, 175919, 173919, 176919, 177919, 186919, 179919, 175919, 174919, 176919, 178919, 185919, 165919, 177919, 172919, 174919, 174919, 165919, 170919, 178919, 177919, 171919, 170919, 175919, 180919, 180919, 184919, 176919, 178919, 175919, 176919, 171919, 173919, 172919, 168919, 175919, 131919, 169000, 174919, 172919, 176919, 175919, 172919, 179919, 179838, 165000, 175919, 178919, 179838, 173919, 183919, 168919, 179919, 171919, 175919, 163000, 174919, 170919, 175919, 174919, 163919, 173919, 175919, 179919, 165919, 182919, 173919, 178919, 175919, 187838, 168000, 178838, 172000, 171919, 173919, 172919, 171919, 176919, 173919, 173919, 165919, 178919, 178919, 169919, 177919, 173919, 177919, 173919, 180919, 171919, 178919, 171919, 9919, 169000, 173919, 155919, 161919, 163919, 172919, 174919, 162919, 175919, 179919, 179919, 161919, 174919, 177919, 178919, 176919, 180919, 165919, 171919, 171919, 172919, 168919, 159919, 166919, 167919, 175919, 174919, 167919, 176919, 181919, 167919, 173919, 174919, 179919, 174919, 178919, 168919, 167919, 173919, 172919, 173919, 173919, 166919, 168919, 174919, 173919, 157919, 174919, 178919, 183919, 169919, 170919, 179919, 178919, 170919, 140919], dataset_name='falcon-refinedweb')}]
2

Using streaming mode, you can download only what you requested, e.g.

fineweb = load_dataset("tiiuae/falcon-refinedweb", split="train", streaming=True)

# skip the first 10 samples and then take only the first 10, resulting in [10:20]
subset = list(fineweb.skip(10).take(10))
1 Like
3

Thanks for you reply, is there any other way then streaming that you can think of?

4

You can also specify a subset of files to download as you did.

We fixed the error you got in the latest version of datasets:

pip install -U datasets

Btw the files from the datasets-server endpoint you are using seems to be URLs, instead I’d recommend passing the paths of the files inside the repository. One way to get the list of files is using list_repo_files()