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/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/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.
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cognitive-engine
A machine learning dataset and research module that aims to address cognitive pitfalls and enhance the cognitive capabilities of humans and language models.
1. Related Repository Collection:
2. conceptual-submodule-expansions
2.1 ontological-weaver
Complementary to the tension-holder-nerve. When the ecosystem encounters apparently irreconcilable ontological realities (e.g., indigenous wisdom frameworks vs quarterly reports), this transformer mechanism refuses binary collapse, instead generating a third (or a chain-fractalization), of synergistic conceptual bridges.
2.2 biomimetic-engine
A research-module to explore patterns and attention-routing mechanisms structurally modeled after biological phenomena (e.g., photosynthesis, cellular mitosis, flocking algorithms) to naturally guide language generation into organic, sustainable, ecological catalyzed and aligned knowledge and patterns.
2.3 symbiogenetic-fusion-node
An architectural framework/ and training pipeline for merging disparate AI models. Moving beyond traditional Mixture of Experts (MoE), it models endosymbiosis—allowing specialized models to absorb and integrate each other’s latent spaces to create entirely new, complex meta-models without destroying their original functional identities. (interesting, like a generalist qwen 5b merged with a liquid one 1.2. the key here is to be adaptable to cross-architecture, but also one with similar can already even exist on github, huggingface, or be a good idea indeed.)
Ronni Ross
2026
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