The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
ScholarSearchAgent Auxiliary Models
This dataset repository hosts the trained auxiliary model artifacts used by ScholarSearchAgent, an intelligent academic paper search and recommendation system for complex research queries.
Source code: https://github.com/Kaedeser/ScholarSearchAgent
Download And Restore
The complete model bundle is stored as 200 MiB archive parts named
ScholarSearchAgentAuxiliaryModel_20260723.tar.part0001,
ScholarSearchAgentAuxiliaryModel_20260723.tar.part0002, and so on. Download
all parts in numerical order, concatenate them, verify the SHA-256 value, then
extract the resulting TAR archive.
PowerShell:
Get-ChildItem "ScholarSearchAgentAuxiliaryModel_20260723.tar.part*" |
Sort-Object Name |
Get-Content -AsByteStream |
Set-Content -AsByteStream "ScholarSearchAgentAuxiliaryModel_20260723.tar"
Get-FileHash "ScholarSearchAgentAuxiliaryModel_20260723.tar" -Algorithm SHA256
tar -xf "ScholarSearchAgentAuxiliaryModel_20260723.tar"
Expected SHA-256:
c15830db9b3c126d584b1ffbdef17cd70a4101d20470ad78661eb883b4fe4a46
Included Models
| Directory after extraction | Purpose | Model form | Reported result |
| --- | --- | --- |
| models/query_gate_biobert | Detect whether a query should enter the academic-search pipeline | BioBERT sequence classifier | Test accuracy: 0.997143 |
| models/intent_biobert | Classify academic search intent | BioBERT sequence classifier | See packaged training metadata |
| models/selector_reranker | Rerank recalled paper candidates | Sentence Transformers CrossEncoder | F1: 88.81% |
| models/crawler_strategy_lora | Select section-expansion strategies | Qwen2.5-3B-Instruct LoRA adapter | Section F1: 0.3007 |
The crawler strategy artifact is a LoRA adapter. The public
Qwen2.5-3B-Instruct base model is not redistributed in this repository and
must be obtained separately under its original license.
Data And Privacy
The archive contains model weights, tokenizer/configuration files, and training metadata required for inference. It does not contain API keys, passwords, private endpoints, raw training datasets, source-code history, or participant identity information.
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