jhu-clsp/mmBERT-base
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Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
shards: list<item: struct<column_encodings: list<item: string>, column_names: list<item: string>, column_sizes: list<item: null>, compression: string, format: string, hashes: list<item: null>, raw_data: struct<basename: string, bytes: int64, hashes: struct<>>, samples: int64, size_limit: int64, version: int64, zip_data: struct<basename: string, bytes: int64, hashes: struct<>>>>
version: int64
vs
total_duplicated_tokens: int64
total_tokens_written: int64
total_tokens_skipped: int64
percentiles: struct<0th: int64, 10th: int64, 20th: int64, 30th: int64, 40th: int64, 50th: int64, 60th: int64, 70th: int64, 80th: int64, 90th: int64, 95th: int64, 99th: int64, 100th: int64>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
return next(iter(self.iter(batch_size=n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 527, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
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.ArrowInvalid: Schema at index 1 was different:
shards: list<item: struct<column_encodings: list<item: string>, column_names: list<item: string>, column_sizes: list<item: null>, compression: string, format: string, hashes: list<item: null>, raw_data: struct<basename: string, bytes: int64, hashes: struct<>>, samples: int64, size_limit: int64, version: int64, zip_data: struct<basename: string, bytes: int64, hashes: struct<>>>>
version: int64
vs
total_duplicated_tokens: int64
total_tokens_written: int64
total_tokens_skipped: int64
percentiles: struct<0th: int64, 10th: int64, 20th: int64, 30th: int64, 40th: int64, 50th: int64, 60th: int64, 70th: int64, 80th: int64, 90th: int64, 95th: int64, 99th: int64, 100th: int64>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
mmBERT Pre-training Data P1
Phase 1 of 3: Diverse multilingual pre-training data mixture (trained for 2.3T tokens) used to train the mmBERT model suite.
NOTE: this is only P1 of the pre-training data due to HF limits, you need to download and combine all three into one folder
This dataset contains the pre-training phase data used to train all mmBERT encoder models. The data is provided in MDS format ready for use with Composer and the ModernBERT training repository.
π Data Composition
| Data Source | Tokens (B) | Percentage | Description |
|---|---|---|---|
| FineWeb2 | 1,196.6 | 60.2% | High-quality multilingual web crawl data |
| DCLM | 600.0 | 30.2% | High-quality English web crawl data |
| Starcoder | 100.6 | 5.1% | Code repositories and files |
| Arxiv | 27.8 | 1.4% | Academic preprints |
| StackExchange | 18.6 | 0.9% | Q&A forums |
| Tulu Flan | 15.3 | 0.8% | Instruction-following data |
| Dolmino Math | 11.2 | 0.6% | Mathematical content |
| PeS2o | 8.4 | 0.4% | Scientific papers |
| Wikipedia (MegaWika) | 4.7 | 0.2% | Encyclopedia articles |
| Books | 4.3 | 0.2% | Literature and reference books |
| StackExchange (Dolmino) | 1.4 | 0.1% | Curated Q&A content |
| Total | 1,989.0 | 100.0% | Diverse mixture for foundation training |
π Language Coverage
This phase covers 60 languages plus code, with an inverse temperature sampling schedule starting at Ο=0.7. Languages include:
- High-resource: English (34.5%), Russian (5.8%), German (4.4%), Spanish (4.5%), French (4.0%), Chinese (5.2%)
- Mid-resource: Italian, Portuguese, Japanese, Dutch, Polish, and 45 others
- Scripts: Latin, Cyrillic, Arabic, Chinese, Japanese, Thai, and many more
π Usage
For pre-training, see the ModernBERT repo: https://github.com/AnswerDotAI/ModernBERT
Direct Access
from streaming import StreamingDataset
# Load the streaming dataset
dataset = StreamingDataset(
remote='https://huggingface.co/datasets/jhu-clsp/mmbert-pretrain-p1-fineweb2-langs',
local='/tmp/mmbert-pretraining-data',
shuffle=True
)
# Access samples
for sample in dataset:
text = sample['text']
# Process your data...
π Related Resources
- Models: mmBERT Model Suite
- Phase 2: Mid-training Data (600B tokens)
- Phase 3: Decay Phase Data (100B tokens)
- Checkpoints: Training Checkpoints
- Paper: Arxiv link
- Hugging Face Paper: mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
- Code: GitHub Repository
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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