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Datasets:
orionweller
/
mmBERT-pretraining-data-chunk2

Tasks:
Fill-Mask
ArXiv:
Tags:
pretraining
encoder
multilingual
License:
Dataset card Data Studio Files
xet
Community
1
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'hashes' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 642, in write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 457, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'hashes' with no child field to Parquet. Consider adding a dummy child field.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1847, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 661, in finalize
                  self._build_writer(self.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 457, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'hashes' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1451, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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shards
dict
version
int64
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39060.mds", "bytes": 104856752, "hashes": {} }, "samples": 41130, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39060.mds.zstd", "bytes": 50951132, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39061.mds", "bytes": 104856979, "hashes": {} }, "samples": 41355, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39061.mds.zstd", "bytes": 51016044, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39062.mds", "bytes": 104855520, "hashes": {} }, "samples": 41363, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39062.mds.zstd", "bytes": 50980853, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39063.mds", "bytes": 104856204, "hashes": {} }, "samples": 41179, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39063.mds.zstd", "bytes": 51087377, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39064.mds", "bytes": 104856068, "hashes": {} }, "samples": 41245, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39064.mds.zstd", "bytes": 51025493, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39065.mds", "bytes": 104856908, "hashes": {} }, "samples": 41275, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39065.mds.zstd", "bytes": 51033619, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39066.mds", "bytes": 104857023, "hashes": {} }, "samples": 41283, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39066.mds.zstd", "bytes": 51103776, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39067.mds", "bytes": 104853904, "hashes": {} }, "samples": 41497, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39067.mds.zstd", "bytes": 51000147, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39068.mds", "bytes": 104856167, "hashes": {} }, "samples": 41363, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39068.mds.zstd", "bytes": 51014549, "hashes": {} } }
2
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2
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2
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2
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2
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2
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2
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2
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2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39086.mds", "bytes": 104854324, "hashes": {} }, "samples": 41387, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39086.mds.zstd", "bytes": 50998017, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39087.mds", "bytes": 104856198, "hashes": {} }, "samples": 41276, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39087.mds.zstd", "bytes": 51083750, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39088.mds", "bytes": 104855295, "hashes": {} }, "samples": 41381, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39088.mds.zstd", "bytes": 51004382, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39089.mds", "bytes": 104857342, "hashes": {} }, "samples": 41234, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39089.mds.zstd", "bytes": 51055789, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39090.mds", "bytes": 104856819, "hashes": {} }, "samples": 41266, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39090.mds.zstd", "bytes": 51067921, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39091.mds", "bytes": 104856474, "hashes": {} }, "samples": 41432, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39091.mds.zstd", "bytes": 50969200, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39092.mds", "bytes": 104854208, "hashes": {} }, "samples": 41167, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39092.mds.zstd", "bytes": 51037630, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39093.mds", "bytes": 104855188, "hashes": {} }, "samples": 41262, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39093.mds.zstd", "bytes": 51027057, "hashes": {} } }
2
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2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39095.mds", "bytes": 104857211, "hashes": {} }, "samples": 41448, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39095.mds.zstd", "bytes": 51094529, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39096.mds", "bytes": 104855977, "hashes": {} }, "samples": 41321, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39096.mds.zstd", "bytes": 50983056, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39097.mds", "bytes": 104857550, "hashes": {} }, "samples": 41427, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39097.mds.zstd", "bytes": 51150091, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39098.mds", "bytes": 104857238, "hashes": {} }, "samples": 41542, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39098.mds.zstd", "bytes": 51063826, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39099.mds", "bytes": 104855336, "hashes": {} }, "samples": 41226, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39099.mds.zstd", "bytes": 51008029, "hashes": {} } }
2
{ "column_encodings": [ "str", "ndarray:uint32" ], "column_names": [ "id", "input_ids" ], "column_sizes": [ null, null ], "compression": "zstd", "format": "mds", "hashes": [], "raw_data": { "basename": "shard.39100.mds", "bytes": 104857205, "hashes": {} }, "samples": 41356, "size_limit": 104857600, "version": 2, "zip_data": { "basename": "shard.39100.mds.zstd", "bytes": 51057558, "hashes": {} } }
2
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2
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2
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2
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2
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mmBERT Training Data (Ready-to-Use)

License: MIT Paper Models GitHub

Complete Training Dataset: Pre-randomized and ready-to-use multilingual training data (3T tokens) for encoder model pre-training.

This dataset is part of the complete, pre-shuffled training data used to train the mmBERT encoder models. Unlike the individual phase datasets, this version is ready for immediate use but the mixture cannot be modified easily. The data is provided in decompressed MDS format ready for use with ModernBERT's Composer and the ModernBERT training repository.

Licensing & Attribution

This dataset aggregates multiple open-source datasets under permissive licenses. See individual source datasets for specific attribution requirements.

Related Resources

Citation 

@misc{marone2025mmbertmodernmultilingualencoder,
      title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning}, 
      author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme},
      year={2025},
      eprint={2509.06888},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.06888}, 
}
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