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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    FileNotFoundError
Message:      [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7ff6c546c980>'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4179, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2511, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2704, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2208, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2230, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 483, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/xml/xml.py", line 67, in _generate_tables
                  with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f:
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 967, in xopen
                  return open(main_hop, mode, *args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              FileNotFoundError: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7ff6c546c980>'

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Dataset Directory

This directory contains the Carolina corpus converted to parquet shards for nanochat training.

Use this repository to run the nanochat training using this dataset: https://github.com/alfakini/nanochat-pt-br

Generate the dataset

# Step 1: Download XML files (~3GB)
uv run python dev/download_carolina.py --output dataset/raw/

# Step 2: Repackage to parquet shards
uv run python dev/repackage_carolina.py --input dataset/raw/ --output dataset/

# Step 3: Upload to huggingface
uv run python dev/repackage_carolina.py --upload --input dataset/

Original dataset source

  • Corpus: Carolina
  • Description: A General Corpus of Contemporary Brazilian Portuguese (~3GB, ~2M documents)
  • License: CC-BY-4.0
  • Maintainers: Carolina C4AI team at University of Sao Paulo

Format

Each shard is a parquet file with:

  • Single column: text (string)
  • Row group size: 1024
  • Compression: zstd
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