BioReason-Pro
Collection
10 items • Updated • 5
Datasets:
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: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1405, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 982, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
safetensors unknown | __key__ string | __url__ string |
|---|---|---|
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... | go_embeddings/GO_0000001 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000002 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000003 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000006 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000007 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000009 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000010 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000011 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000012 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000014 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000015 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000016 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000017 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000018 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000019 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000022 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000023 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000024 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000025 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000026 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000027 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000028 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000030 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000031 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000032 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000033 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
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... | go_embeddings/GO_0000034 | hf://datasets/wanglab/bioreason-pro-go-embeddings@c7319f0230214f82b607b92c480c9bcee69aef90/go_embeddings.tar.gz |
End of preview.
🧬 BioReason-Pro
Advancing Protein Function Prediction with
Multimodal Biological Reasoning
BioReason-Pro GO Term Embeddings
Precomputed text embeddings for 43,248 Gene Ontology terms, one per term, produced
with Qwen/Qwen3-Embedding-4B over the
GO term name and definition.
BioReason-Pro's GO graph encoder (a 3-layer GAT over go-basic.obo) is initialised from
these vectors rather than learning term representations from scratch. They are required
for training, evaluation, and checkpoint conversion — passed as --precomputed_embeddings_path.
Usage
git clone https://github.com/bowang-lab/BioReason-Pro.git && cd BioReason-Pro
pip install -e .
python scripts/download_assets.py --dest /data/bioreason --skip-structures
# -> /data/bioreason/go_embeddings (pass as GO_EMBEDDINGS_PATH)
Layout
A single go_embeddings.tar.gz that extracts to a flat directory:
go_embeddings/
GO_0000001.safetensors
GO_0000002.safetensors
... # 43,248 files, 338 MB extracted
Each file holds one tensor of dimension 2560 under the key embedding.
Regenerating
These vectors can be rebuilt from scratch, though downloading is strongly preferred — regenerating on a different model revision produces different vectors and will not match the released checkpoints:
python -m bioreason2.utils.go_embed \
--model_name Qwen/Qwen3-Embedding-4B \
--output_dir /data/bioreason/go_embeddings \
--batch_size 32 --device cuda
Citation
If you find this work useful, please cite our papers:
@article {Fallahpour2026.03.19.712954,
author = {Fallahpour, Adibvafa and Seyed-Ahmadi, Arman and Idehpour, Parsa and Ibrahim, Omar and Gupta, Purav and Naimer, Jack and Zhu, Kevin and Shah, Arnav and Ma, Shihao and Adduri, Abhinav and G{\"u}loglu, Talu and Liu, Nuo and Cui, Haotian and Jain, Arihant and de Castro, Max and Fallahpour, Amirfaham and Cembellin-Prieto, Antonio and Stiles, John S. and Nem{\v c}ko, Filip and Nevue, Alexander A. and Moon, Hyungseok C. and Sosnick, Lucas and Markham, Olivia and Duan, Haonan and Lee, Michelle Y. Y. and Salvador, Andrea F. M. and Maddison, Chris J. and Thaiss, Christoph A. and Ricci-Tam, Chiara and Plosky, Brian S. and Burke, Dave P. and Hsu, Patrick D. and Goodarzi, Hani and Wang, Bo},
title = {BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning},
elocation-id = {2026.03.19.712954},
year = {2026},
doi = {10.64898/2026.03.19.712954},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954},
eprint = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954.full.pdf},
journal = {bioRxiv}
}
@misc{fallahpour2025bioreasonincentivizingmultimodalbiological,
title={BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model},
author={Adibvafa Fallahpour and Andrew Magnuson and Purav Gupta and Shihao Ma and Jack Naimer and Arnav Shah and Haonan Duan and Omar Ibrahim and Hani Goodarzi and Chris J. Maddison and Bo Wang},
year={2025},
eprint={2505.23579},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.23579},
}
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