Datasets:

juiceb0xc0de
/

ling-3.0-tiny-atlas-outputs

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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'correlation', 'neuron_b', 'neuron_a', 'joint_active_n'}) and 9 missing columns ({'feature', 'bucket_entropy', 'bucket_quality', 'eta_squared', 'runner_up_bucket', 'dominant_bucket_mean', 'runner_up_bucket_mean', 'dominance_margin', 'bucket_means'}).

This happened while the json dataset builder was generating data using

hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs/analysis/l0_attn_coactivation_pairs.json (at revision 7e71315f993d7d12f96bf151b2d5c850415175ea), ['hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_component_comparison.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_code_analysis.json', 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'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_router_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_router_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_router_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_v_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_v_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_v_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_v_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l8_v_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_attn_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_attn_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_attn_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_attn_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_component_comparison.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_heads_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_heads_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_heads_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_heads_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_heads_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_k_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_k_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_k_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_k_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_k_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/authentic_vs_corporate_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/code_vs_natural_language_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/logit_lens_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/moe_layout.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/ov_circuit_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/sub_zero_brain_atlas.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/subzero_report.json']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              neuron_a: int64
              neuron_b: int64
              correlation: double
              dominant_bucket: null
              joint_active_n: int64
              to
              {'feature': Value('int64'), 'dominant_bucket': Value('string'), 'dominant_bucket_mean': Value('float64'), 'runner_up_bucket': Value('string'), 'runner_up_bucket_mean': Value('float64'), 'dominance_margin': Value('float64'), 'bucket_entropy': Value('float64'), 'eta_squared': Value('float64'), 'bucket_quality': Value('float64'), 'bucket_means': {'brainstorming': Value('float64'), 'business': Value('float64'), 'community': Value('float64'), 'core_technical': Value('float64'), 'creative_writing': Value('float64'), 'data': Value('float64'), 'design': Value('float64'), 'humor': Value('float64'), 'introspection': Value('float64'), 'learning': Value('float64'), 'ml_ai': Value('float64'), 'niche': Value('float64'), 'planning': Value('float64'), 'research': Value('float64'), 'roleplay': Value('float64'), 'tool_use': Value('float64'), 'writing': Value('float64')}}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              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 1694, 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 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'correlation', 'neuron_b', 'neuron_a', 'joint_active_n'}) and 9 missing columns ({'feature', 'bucket_entropy', 'bucket_quality', 'eta_squared', 'runner_up_bucket', 'dominant_bucket_mean', 'runner_up_bucket_mean', 'dominance_margin', 'bucket_means'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs/analysis/l0_attn_coactivation_pairs.json (at revision 7e71315f993d7d12f96bf151b2d5c850415175ea), ['hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_attn_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_component_comparison.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_heads_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_k_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_mlp_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_mlp_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_mlp_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l0_mlp_neuron_taxonomy.json', 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'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_k_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_k_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_mlp_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_q_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_router_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_bucket_metrics.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_coactivation_pairs.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_code_analysis.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_neuron_taxonomy.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/analysis/l9_v_per_head.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/authentic_vs_corporate_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/code_vs_natural_language_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/logit_lens_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/moe_layout.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/ov_circuit_scores.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/sub_zero_brain_atlas.json', 'hf://datasets/juiceb0xc0de/ling-3.0-tiny-atlas-outputs@7e71315f993d7d12f96bf151b2d5c850415175ea/subzero_report.json']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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feature int64	dominant_bucket string	dominant_bucket_mean float64	runner_up_bucket string	runner_up_bucket_mean float64	dominance_margin float64	bucket_entropy float64	eta_squared float64	bucket_quality float64	bucket_means dict
0	creative_writing	-0.002518	writing	-0.002534	0.000016	0.99807	0.022987	0.000044	{ "brainstorming": -0.0027850487, "business": -0.0029895036, "community": -0.0026005742, "core_technical": -0.0026103912, "creative_writing": -0.00251794, "data": -0.0031342292, "design": -0.0025564008, "humor": -0.0025839882, "introspection": -0.0036821277, "learning": -0.003114671, "ml_ai": -0.0...
1	learning	-0.000155	introspection	-0.000254	0.000099	0.960062	0.044824	0.00179	{ "brainstorming": -0.0008398296, "business": -0.0006528083, "community": -0.0005442501, "core_technical": -0.0011706841, "creative_writing": -0.0011396649, "data": -0.0005177866, "design": -0.0008575815, "humor": -0.0013963209, "introspection": -0.0002539829, "learning": -0.0001552879, "ml_ai": -...
2	ml_ai	-0.002997	learning	-0.003346	0.000349	0.998481	0.044503	0.000068	{ "brainstorming": -0.0038929703, "business": -0.0038786936, "community": -0.0043149511, "core_technical": -0.0035201595, "creative_writing": -0.0039618979, "data": -0.0035257319, "design": -0.0039515751, "humor": -0.0037346901, "introspection": -0.0033691635, "learning": -0.0033458204, "ml_ai": -...
3	niche	0.008807	tool_use	0.008672	0.000136	0.999867	0.018119	0.000002	{ "brainstorming": 0.0081473831, "business": 0.0082153706, "community": 0.0082176756, "core_technical": 0.0079459278, "creative_writing": 0.0080601107, "data": 0.0082878182, "design": 0.0086699724, "humor": 0.0080836704, "introspection": 0.0085035395, "learning": 0.0082631037, "ml_ai": 0.008140823...
4	roleplay	0.016134	learning	0.015977	0.000157	0.999935	0.027308	0.000002	{ "brainstorming": 0.0153033575, "business": 0.0157721303, "community": 0.0157961156, "core_technical": 0.0154648516, "creative_writing": 0.0156099489, "data": 0.0152549827, "design": 0.0157564711, "humor": 0.015524447, "introspection": 0.0154893287, "learning": 0.015976673, "ml_ai": 0.0154350419,...
5	research	-0.004169	data	-0.00461	0.00044	0.999349	0.026797	0.000017	{ "brainstorming": -0.0049596638, "business": -0.0048457077, "community": -0.0047483803, "core_technical": -0.0052844882, "creative_writing": -0.0050012143, "data": -0.0046095885, "design": -0.0051431451, "humor": -0.005034226, "introspection": -0.0053384579, "learning": -0.0051985448, "ml_ai": -0...
6	core_technical	-0.013175	ml_ai	-0.013438	0.000263	0.999594	0.115485	0.000047	{ "brainstorming": -0.0147725567, "business": -0.0148729999, "community": -0.015246151, "core_technical": -0.0131753189, "creative_writing": -0.0148279285, "data": -0.0141967023, "design": -0.0148617113, "humor": -0.0152894752, "introspection": -0.0158284549, "learning": -0.0149531942, "ml_ai": -0...
7	brainstorming	-0.000549	data	-0.000782	0.000234	0.99099	0.022149	0.0002	{ "brainstorming": -0.0005485689, "business": -0.0008748287, "community": -0.0011419124, "core_technical": -0.0013785464, "creative_writing": -0.0008729853, "data": -0.0007823979, "design": -0.0012490128, "humor": -0.0011186547, "introspection": -0.0011747463, "learning": -0.0010830689, "ml_ai": -...
8	learning	0.000565	community	0.000493	0.000073	0.73514	0.043637	0.011558	{ "brainstorming": -0.000052695, "business": -0.0001983706, "community": 0.0004925863, "core_technical": 0.000040299, "creative_writing": 0.0001053856, "data": -0.0002594723, "design": 0.0002169913, "humor": -0.0000682752, "introspection": 0.0001170886, "learning": 0.0005653664, "ml_ai": -0.000644...
9	brainstorming	-0.011181	community	-0.011419	0.000238	0.999912	0.034555	0.000003	{ "brainstorming": -0.0111813825, "business": -0.0116247796, "community": -0.0114189526, "core_technical": -0.0115023386, "creative_writing": -0.0120724943, "data": -0.0114415763, "design": -0.0114600426, "humor": -0.0122044384, "introspection": -0.0115537662, "learning": -0.0116811562, "ml_ai": -...
10	business	-0.01206	ml_ai	-0.012107	0.000047	0.999951	0.017868	0.000001	{ "brainstorming": -0.0126283914, "business": -0.0120597258, "community": -0.0124858022, "core_technical": -0.0125633031, "creative_writing": -0.0126375733, "data": -0.0121112801, "design": -0.0126329325, "humor": -0.0123415412, "introspection": -0.0123595931, "learning": -0.0126026869, "ml_ai": -...
11	planning	0.004799	community	0.004772	0.000027	0.99983	0.010233	0.000002	{ "brainstorming": 0.0046722703, "business": 0.00469905, "community": 0.0047715651, "core_technical": 0.0043396419, "creative_writing": 0.0046356199, "data": 0.0045217313, "design": 0.0045803096, "humor": 0.0046466254, "introspection": 0.0046084095, "learning": 0.0046204841, "ml_ai": 0.004440045, ...
12	introspection	0.012631	business	0.012244	0.000387	0.999921	0.024429	0.000002	{ "brainstorming": 0.011947968, "business": 0.0122444481, "community": 0.0118354876, "core_technical": 0.0119370827, "creative_writing": 0.0116735809, "data": 0.0119113885, "design": 0.0122214751, "humor": 0.0117297284, "introspection": 0.0126314014, "learning": 0.0120737264, "ml_ai": 0.0118461605...
13	tool_use	0.010463	data	0.010108	0.000356	0.999765	0.052499	0.000012	{ "brainstorming": 0.0097011728, "business": 0.0097376481, "community": 0.0097643342, "core_technical": 0.009761394, "creative_writing": 0.0090453438, "data": 0.0101078534, "design": 0.0099619012, "humor": 0.009514831, "introspection": 0.0093379579, "learning": 0.0098235831, "ml_ai": 0.0100129843,...
14	brainstorming	-0.018304	research	-0.01863	0.000326	0.999871	0.051731	0.000007	{ "brainstorming": -0.0183040649, "business": -0.0190707706, "community": -0.0196271241, "core_technical": -0.0193117727, "creative_writing": -0.0190210883, "data": -0.0191500094, "design": -0.0194691028, "humor": -0.0190580208, "introspection": -0.0191876553, "learning": -0.0196904782, "ml_ai": -...
15	tool_use	-0.003444	core_technical	-0.003515	0.000071	0.998494	0.038256	0.000058	{ "brainstorming": -0.0040686526, "business": -0.0040163114, "community": -0.0043490557, "core_technical": -0.003515213, "creative_writing": -0.0038808789, "data": -0.0036758212, "design": -0.0037359868, "humor": -0.0039079413, "introspection": -0.0048477184, "learning": -0.004200961, "ml_ai": -0....
16	roleplay	0.010784	introspection	0.010613	0.000171	0.9998	0.045527	0.000009	{ "brainstorming": 0.010046728, "business": 0.0103721097, "community": 0.0102333408, "core_technical": 0.0093166763, "creative_writing": 0.0099870255, "data": 0.0100659812, "design": 0.0097409915, "humor": 0.0103704613, "introspection": 0.0106125204, "learning": 0.0102307498, "ml_ai": 0.0096858144...
17	ml_ai	-0.012216	planning	-0.012442	0.000226	0.999938	0.023192	0.000001	{ "brainstorming": -0.012679182, "business": -0.0128187668, "community": -0.0129490029, "core_technical": -0.0124788461, "creative_writing": -0.0125292586, "data": -0.0125238225, "design": -0.0127903372, "humor": -0.0126955844, "introspection": -0.0131471045, "learning": -0.0130301844, "ml_ai": -0...
18	tool_use	0.001948	ml_ai	0.001929	0.00002	0.997263	0.02233	0.000061	{ "brainstorming": 0.0013889398, "business": 0.0014152844, "community": 0.0016786504, "core_technical": 0.0017614636, "creative_writing": 0.0017412254, "data": 0.0018421946, "design": 0.0018105505, "humor": 0.0015417009, "introspection": 0.0015822306, "learning": 0.001638325, "ml_ai": 0.0019286992...
19	introspection	0.007007	learning	0.006919	0.000088	0.999629	0.032307	0.000012	{ "brainstorming": 0.0062723905, "business": 0.0065988991, "community": 0.0066353092, "core_technical": 0.0059505687, "creative_writing": 0.0060219588, "data": 0.0065414542, "design": 0.0063039991, "humor": 0.0059509971, "introspection": 0.0070065302, "learning": 0.0069189449, "ml_ai": 0.006372066...
20	roleplay	0.005564	humor	0.005368	0.000196	0.998818	0.040565	0.000048	{ "brainstorming": 0.0047780322, "business": 0.0047720983, "community": 0.0043323576, "core_technical": 0.0046606408, "creative_writing": 0.0050262772, "data": 0.0047251573, "design": 0.0048932261, "humor": 0.0053678052, "introspection": 0.0053486233, "learning": 0.0049937409, "ml_ai": 0.005148865...
21	ml_ai	0.006025	data	0.005756	0.000269	0.998842	0.058693	0.000068	{ "brainstorming": 0.0051055225, "business": 0.005501979, "community": 0.0054040002, "core_technical": 0.0055367518, "creative_writing": 0.0043692677, "data": 0.00575613, "design": 0.00562202, "humor": 0.0044010575, "introspection": 0.0051603727, "learning": 0.0051816311, "ml_ai": 0.006025211, "...
22	writing	-0.001202	learning	-0.00138	0.000178	0.994535	0.029529	0.000161	{ "brainstorming": -0.0020550985, "business": -0.0023308774, "community": -0.0017055492, "core_technical": -0.0017091314, "creative_writing": -0.0018178072, "data": -0.0017221635, "design": -0.0017423565, "humor": -0.0016632869, "introspection": -0.0021985229, "learning": -0.0013795608, "ml_ai": -...
23	tool_use	0.008984	niche	0.008456	0.000528	0.999637	0.034673	0.000013	{ "brainstorming": 0.0074752127, "business": 0.0079193581, "community": 0.0081959711, "core_technical": 0.007645437, "creative_writing": 0.0077324896, "data": 0.0079193283, "design": 0.008184772, "humor": 0.0081768008, "introspection": 0.0074574645, "learning": 0.0079317195, "ml_ai": 0.0081093162,...
24	creative_writing	0.0018	humor	0.001642	0.000157	0.964108	0.03964	0.001423	{ "brainstorming": 0.0011874846, "business": 0.0005624632, "community": 0.0008485498, "core_technical": 0.0011014475, "creative_writing": 0.0017996744, "data": 0.000233263, "design": 0.0009283364, "humor": 0.0016423793, "introspection": 0.0013028093, "learning": 0.0006809232, "ml_ai": 0.0003019569...
25	research	0.005429	ml_ai	0.005222	0.000206	0.99773	0.076174	0.000173	{ "brainstorming": 0.0042145713, "business": 0.004642556, "community": 0.00380095, "core_technical": 0.0047571044, "creative_writing": 0.0042238808, "data": 0.0049089896, "design": 0.0036637085, "humor": 0.0044413419, "introspection": 0.0044614146, "learning": 0.0043888064, "ml_ai": 0.0052224044, ...
26	core_technical	-0.010132	ml_ai	-0.010436	0.000304	0.999914	0.026451	0.000002	{ "brainstorming": -0.010971169, "business": -0.0106511973, "community": -0.0107943416, "core_technical": -0.0101321554, "creative_writing": -0.0109998388, "data": -0.0105855884, "design": -0.0108281737, "humor": -0.0109752975, "introspection": -0.0110363336, "learning": -0.010530686, "ml_ai": -0....
27	ml_ai	-0.009707	core_technical	-0.010102	0.000396	0.99972	0.057301	0.000016	{ "brainstorming": -0.0106526604, "business": -0.010787731, "community": -0.0113593964, "core_technical": -0.0101022441, "creative_writing": -0.011052059, "data": -0.0104183955, "design": -0.0111690201, "humor": -0.0108982865, "introspection": -0.0111136837, "learning": -0.0108315349, "ml_ai": -0....
28	introspection	0.006124	roleplay	0.006068	0.000056	0.999753	0.026655	0.000007	{ "brainstorming": 0.0057149976, "business": 0.0058042672, "community": 0.005868542, "core_technical": 0.0051929248, "creative_writing": 0.0056995354, "data": 0.0058135702, "design": 0.0058337534, "humor": 0.0058277468, "introspection": 0.0061238632, "learning": 0.0060273432, "ml_ai": 0.0055265203...
29	humor	0.003918	tool_use	0.00389	0.000027	0.998265	0.03317	0.000058	{ "brainstorming": 0.0031481658, "business": 0.0036546311, "community": 0.0037055134, "core_technical": 0.003709787, "creative_writing": 0.0035651897, "data": 0.0034774744, "design": 0.0034733021, "humor": 0.0039176107, "introspection": 0.0031105322, "learning": 0.0028766426, "ml_ai": 0.0037892237...
30	introspection	0.009155	ml_ai	0.009057	0.000099	0.999766	0.044836	0.00001	{ "brainstorming": 0.0085442644, "business": 0.0086132307, "community": 0.0083899526, "core_technical": 0.0086440137, "creative_writing": 0.0083458535, "data": 0.0085633341, "design": 0.0081544779, "humor": 0.0078878887, "introspection": 0.0091553638, "learning": 0.009038602, "ml_ai": 0.0090568336...
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46	introspection	0.002156	community	0.002113	0.000042	0.985341	0.056379	0.000826	{ "brainstorming": 0.0015879888, "business": 0.0015555791, "community": 0.0021134336, "core_technical": 0.0006702726, "creative_writing": 0.0018603941, "data": 0.0015294316, "design": 0.001717836, "humor": 0.0019909155, "introspection": 0.0021558101, "learning": 0.0014276747, "ml_ai": 0.0005931602...
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50	learning	0.018141	writing	0.018003	0.000139	0.999938	0.029094	0.000002	{ "brainstorming": 0.0170016699, "business": 0.0175223015, "community": 0.0179997664, "core_technical": 0.0174695104, "creative_writing": 0.0172854234, "data": 0.0173455179, "design": 0.0179029312, "humor": 0.0176609755, "introspection": 0.0176822431, "learning": 0.0181413423, "ml_ai": 0.017410596...
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52	tool_use	0.00588	community	0.005514	0.000366	0.999497	0.025246	0.000013	{ "brainstorming": 0.0052085021, "business": 0.0053956695, "community": 0.0055139307, "core_technical": 0.0052795568, "creative_writing": 0.0047978549, "data": 0.0050561046, "design": 0.0053999037, "humor": 0.0049857995, "introspection": 0.0052014547, "learning": 0.0054646498, "ml_ai": 0.005244810...
53	creative_writing	-0.003805	brainstorming	-0.004023	0.000217	0.99909	0.044443	0.00004	{ "brainstorming": -0.0040227897, "business": -0.0046606162, "community": -0.0043730158, "core_technical": -0.0046102465, "creative_writing": -0.0038053237, "data": -0.0045630769, "design": -0.0043830727, "humor": -0.004053127, "introspection": -0.004431502, "learning": -0.0040455139, "ml_ai": -0....
54	introspection	0.002387	roleplay	0.002351	0.000036	0.997426	0.026501	0.000068	{ "brainstorming": 0.0019841962, "business": 0.0018988587, "community": 0.0018345535, "core_technical": 0.0017841381, "creative_writing": 0.0020659645, "data": 0.001977934, "design": 0.0015059382, "humor": 0.0020401715, "introspection": 0.00238716, "learning": 0.0020721389, "ml_ai": 0.0016686417, ...
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56	introspection	0.006967	community	0.00649	0.000477	0.999198	0.052037	0.000042	{ "brainstorming": 0.0061528478, "business": 0.0062256386, "community": 0.0064895917, "core_technical": 0.005225806, "creative_writing": 0.0056602512, "data": 0.0056441207, "design": 0.0060715564, "humor": 0.0060355673, "introspection": 0.006966732, "learning": 0.0060021873, "ml_ai": 0.0053197364,...
57	research	0.002869	ml_ai	0.002866	0.000003	0.995901	0.027374	0.000112	{ "brainstorming": 0.0021230152, "business": 0.0023602711, "community": 0.0020447811, "core_technical": 0.0026073428, "creative_writing": 0.0018744828, "data": 0.0024518489, "design": 0.0019943197, "humor": 0.0023646413, "introspection": 0.0024195267, "learning": 0.0019421148, "ml_ai": 0.002865515...
58	brainstorming	-0.008782	introspection	-0.008912	0.00013	0.999814	0.031152	0.000006	{ "brainstorming": -0.0087819565, "business": -0.0090030609, "community": -0.0094478242, "core_technical": -0.0099186292, "creative_writing": -0.0093914215, "data": -0.0090695936, "design": -0.0093171783, "humor": -0.0094422419, "introspection": -0.0089123603, "learning": -0.0095827878, "ml_ai": -...
59	tool_use	0.003405	community	0.003052	0.000353	0.997961	0.028085	0.000057	{ "brainstorming": 0.0023860601, "business": 0.0026139177, "community": 0.0030520854, "core_technical": 0.0023434248, "creative_writing": 0.0025081164, "data": 0.0027402823, "design": 0.0029385707, "humor": 0.0023884547, "introspection": 0.0022478576, "learning": 0.0025836478, "ml_ai": 0.002411426...
60	introspection	0.015952	brainstorming	0.015776	0.000176	0.999929	0.029195	0.000002	{ "brainstorming": 0.015775647, "business": 0.0152399745, "community": 0.015415879, "core_technical": 0.0150140934, "creative_writing": 0.015393896, "data": 0.0151774799, "design": 0.0150110666, "humor": 0.015462623, "introspection": 0.0159515347, "learning": 0.0154609261, "ml_ai": 0.0147820627, ...
61	roleplay	0.002943	business	0.002564	0.000379	0.996235	0.047945	0.000181	{ "brainstorming": 0.0019704618, "business": 0.0025637976, "community": 0.0024674935, "core_technical": 0.0021582344, "creative_writing": 0.0020599498, "data": 0.0019637139, "design": 0.0024309626, "humor": 0.0023609558, "introspection": 0.0018567666, "learning": 0.0018696014, "ml_ai": 0.002071948...
62	ml_ai	-0.002761	writing	-0.002762	0.000001	0.997823	0.065409	0.000142	{ "brainstorming": -0.0036522024, "business": -0.0030120627, "community": -0.0035999473, "core_technical": -0.0028359292, "creative_writing": -0.0040213396, "data": -0.0031481115, "design": -0.0033528118, "humor": -0.0038919481, "introspection": -0.0037683444, "learning": -0.0031819977, "ml_ai": -...
63	creative_writing	-0.007912	community	-0.007924	0.000012	0.99971	0.0217	0.000006	{ "brainstorming": -0.0080251424, "business": -0.0087164799, "community": -0.007923644, "core_technical": -0.008502502, "creative_writing": -0.0079120267, "data": -0.0083181653, "design": -0.008549734, "humor": -0.0083794277, "introspection": -0.0087812319, "learning": -0.0082114805, "ml_ai": -0.0...
64	research	-0.005168	core_technical	-0.005308	0.00014	0.99966	0.023875	0.000008	{ "brainstorming": -0.0056740833, "business": -0.0055053388, "community": -0.0059606866, "core_technical": -0.0053081186, "creative_writing": -0.0053481008, "data": -0.0055007515, "design": -0.0058316006, "humor": -0.0054340502, "introspection": -0.0057730875, "learning": -0.00543012, "ml_ai": -0....
65	creative_writing	-0.006629	humor	-0.006908	0.000279	0.999734	0.02929	0.000008	{ "brainstorming": -0.0070980662, "business": -0.0075670793, "community": -0.0075602015, "core_technical": -0.0072784214, "creative_writing": -0.0066289995, "data": -0.0077056107, "design": -0.0075098909, "humor": -0.0069083134, "introspection": -0.0071373261, "learning": -0.0072411336, "ml_ai": -...
66	tool_use	0.01145	niche	0.011376	0.000073	0.999941	0.012446	0.000001	{ "brainstorming": 0.0107968086, "business": 0.0107583515, "community": 0.0109140174, "core_technical": 0.0107986936, "creative_writing": 0.0110640964, "data": 0.0106393211, "design": 0.0110303136, "humor": 0.0109960297, "introspection": 0.0110286269, "learning": 0.010971413, "ml_ai": 0.0109657841...
67	core_technical	0.00075	ml_ai	0.000674	0.000076	0.933714	0.02041	0.001353	{ "brainstorming": 0.0001231964, "business": 0.0003766619, "community": 0.0002600416, "core_technical": 0.0007501341, "creative_writing": 0.000535631, "data": 0.0005011003, "design": 0.0006137816, "humor": 0.0006089606, "introspection": -0.0000981963, "learning": 0.0004890625, "ml_ai": 0.000674029...
68	research	-0.006622	data	-0.006756	0.000134	0.999795	0.027528	0.000006	{ "brainstorming": -0.0071036695, "business": -0.0070988284, "community": -0.0075446377, "core_technical": -0.00701235, "creative_writing": -0.0072931899, "data": -0.0067559271, "design": -0.0071701049, "humor": -0.0070927199, "introspection": -0.0070107626, "learning": -0.0071098665, "ml_ai": -0....
69	research	-0.007919	introspection	-0.008037	0.000119	0.999898	0.019342	0.000002	{ "brainstorming": -0.0083204359, "business": -0.0083943121, "community": -0.008114432, "core_technical": -0.0085426541, "creative_writing": -0.0085863573, "data": -0.0080941953, "design": -0.0082275327, "humor": -0.0084591238, "introspection": -0.0080373082, "learning": -0.0083227642, "ml_ai": -0...
70	research	-0.011062	creative_writing	-0.011237	0.000175	0.999842	0.044673	0.000007	{ "brainstorming": -0.0112606119, "business": -0.0114372903, "community": -0.0116346842, "core_technical": -0.0121552898, "creative_writing": -0.0112373354, "data": -0.0117054107, "design": -0.0120469499, "humor": -0.011592309, "introspection": -0.0114084641, "learning": -0.0117094358, "ml_ai": -0...
71	introspection	0.017653	business	0.017496	0.000157	0.999953	0.025316	0.000001	{ "brainstorming": 0.0173910893, "business": 0.0174959209, "community": 0.0173680708, "core_technical": 0.0167383645, "creative_writing": 0.0173757765, "data": 0.0168909561, "design": 0.0172441378, "humor": 0.017196849, "introspection": 0.0176529028, "learning": 0.0169543754, "ml_ai": 0.0166639704...
72	tool_use	0.006427	niche	0.006193	0.000234	0.99964	0.034623	0.000012	{ "brainstorming": 0.0058889943, "business": 0.0060475366, "community": 0.0061506592, "core_technical": 0.00594849, "creative_writing": 0.0053046392, "data": 0.0059002298, "design": 0.0060030855, "humor": 0.0058427374, "introspection": 0.0057692421, "learning": 0.0056651565, "ml_ai": 0.0056732511,...
73	creative_writing	-0.003005	introspection	-0.003032	0.000027	0.999019	0.026827	0.000026	{ "brainstorming": -0.0030625826, "business": -0.0035868466, "community": -0.0037499082, "core_technical": -0.0038094309, "creative_writing": -0.0030054122, "data": -0.0034981812, "design": -0.0034988844, "humor": -0.0032639103, "introspection": -0.0030323362, "learning": -0.0032142559, "ml_ai": -...
74	creative_writing	-0.001038	humor	-0.001186	0.000148	0.99191	0.040107	0.000324	{ "brainstorming": -0.0016179564, "business": -0.001800166, "community": -0.0016116474, "core_technical": -0.0016166078, "creative_writing": -0.0010378389, "data": -0.0016481157, "design": -0.0016908626, "humor": -0.0011860608, "introspection": -0.0024592271, "learning": -0.0018961703, "ml_ai": -0...
75	creative_writing	-0.006603	core_technical	-0.006629	0.000026	0.999857	0.020238	0.000003	{ "brainstorming": -0.0069371606, "business": -0.0069143069, "community": -0.0069149691, "core_technical": -0.0066290144, "creative_writing": -0.0066031078, "data": -0.0071895854, "design": -0.0072182249, "humor": -0.0066755842, "introspection": -0.0073780841, "learning": -0.0070187706, "ml_ai": -...
76	tool_use	-0.009247	core_technical	-0.00936	0.000113	0.99975	0.049557	0.000012	{ "brainstorming": -0.0103994384, "business": -0.0099345902, "community": -0.0098737273, "core_technical": -0.0093604904, "creative_writing": -0.0106521454, "data": -0.0097434232, "design": -0.0098442081, "humor": -0.0105760284, "introspection": -0.0099502979, "learning": -0.0098618641, "ml_ai": -...
77	design	-0.001972	tool_use	-0.002072	0.000099	0.997754	0.040816	0.000092	{ "brainstorming": -0.0025085101, "business": -0.0024224634, "community": -0.0021717174, "core_technical": -0.002352884, "creative_writing": -0.0030618303, "data": -0.0023475916, "design": -0.0019723603, "humor": -0.0030135389, "introspection": -0.0022888996, "learning": -0.0026280205, "ml_ai": -0...
78	planning	0.007606	business	0.007399	0.000207	0.999733	0.02909	0.000008	{ "brainstorming": 0.0073803347, "business": 0.0073988605, "community": 0.0071581462, "core_technical": 0.0068480927, "creative_writing": 0.0067193187, "data": 0.0072136945, "design": 0.0069666072, "humor": 0.0065678144, "introspection": 0.007356077, "learning": 0.0068670334, "ml_ai": 0.0069000036...
79	tool_use	0.018514	design	0.018003	0.000511	0.9999	0.043117	0.000004	{ "brainstorming": 0.0173863508, "business": 0.0178217087, "community": 0.0177572463, "core_technical": 0.0168637633, "creative_writing": 0.0169154797, "data": 0.0173572805, "design": 0.0180026032, "humor": 0.0179910306, "introspection": 0.0175364297, "learning": 0.0172352716, "ml_ai": 0.017583770...
80	research	-0.007922	business	-0.008222	0.000299	0.9997	0.046803	0.000014	{ "brainstorming": -0.0085844453, "business": -0.0082217781, "community": -0.008605428, "core_technical": -0.0087980926, "creative_writing": -0.0090940828, "data": -0.0083685145, "design": -0.0086455131, "humor": -0.0093260426, "introspection": -0.0086534461, "learning": -0.0084826881, "ml_ai": -0...
81	core_technical	-0.000876	learning	-0.00119	0.000314	0.994199	0.03147	0.000183	{ "brainstorming": -0.0013739964, "business": -0.00148904, "community": -0.0015314344, "core_technical": -0.0008759645, "creative_writing": -0.0016671218, "data": -0.0015518916, "design": -0.0013304281, "humor": -0.0014434089, "introspection": -0.0020201628, "learning": -0.0011901328, "ml_ai": -0....
82	tool_use	0.004545	planning	0.00436	0.000185	0.998458	0.039405	0.000061	{ "brainstorming": 0.0040803677, "business": 0.0040020142, "community": 0.0041506304, "core_technical": 0.0040769121, "creative_writing": 0.0034817117, "data": 0.004197693, "design": 0.0042071496, "humor": 0.003165208, "introspection": 0.0034294305, "learning": 0.0041779531, "ml_ai": 0.0042690467,...
83	introspection	-0.01302	tool_use	-0.013423	0.000403	0.999911	0.032118	0.000003	{ "brainstorming": -0.0138424356, "business": -0.0137730222, "community": -0.0136964889, "core_technical": -0.0144778304, "creative_writing": -0.0137969973, "data": -0.0136842914, "design": -0.0138548641, "humor": -0.0141156726, "introspection": -0.0130197499, "learning": -0.0137739079, "ml_ai": -...
84	humor	-0.003259	community	-0.003387	0.000128	0.998543	0.04398	0.000064	{ "brainstorming": -0.0041869138, "business": -0.0039581382, "community": -0.0033873494, "core_technical": -0.0040593641, "creative_writing": -0.0035791174, "data": -0.0042442707, "design": -0.0038245646, "humor": -0.0032591359, "introspection": -0.0041840402, "learning": -0.0042260811, "ml_ai": -...
85	introspection	-0.005406	tool_use	-0.005562	0.000156	0.999593	0.02629	0.000011	{ "brainstorming": -0.0061214655, "business": -0.005791917, "community": -0.0058007333, "core_technical": -0.0058685336, "creative_writing": -0.0061218208, "data": -0.0060310191, "design": -0.0057918737, "humor": -0.0059293909, "introspection": -0.0054064039, "learning": -0.0063045961, "ml_ai": -0...
86	writing	0.001836	community	0.001572	0.000264	0.995405	0.023409	0.000108	{ "brainstorming": 0.0012266251, "business": 0.0013496329, "community": 0.0015720367, "core_technical": 0.0013406078, "creative_writing": 0.0014076055, "data": 0.0013244121, "design": 0.0013206598, "humor": 0.0014640104, "introspection": 0.0008695686, "learning": 0.0010299284, "ml_ai": 0.001130243...
87	ml_ai	0.007856	core_technical	0.00763	0.000226	0.999543	0.063615	0.000029	{ "brainstorming": 0.0072241402, "business": 0.0074405153, "community": 0.0065695476, "core_technical": 0.0076300777, "creative_writing": 0.0067348406, "data": 0.0072910343, "design": 0.0068271668, "humor": 0.007047459, "introspection": 0.0068277121, "learning": 0.0072349212, "ml_ai": 0.0078559322...
88	business	0.001048	data	0.001047	0.000001	0.96647	0.023025	0.000772	{ "brainstorming": 0.0006547187, "business": 0.0010481358, "community": 0.0006093399, "core_technical": 0.0003527253, "creative_writing": 0.0002877411, "data": 0.0010467378, "design": 0.0006940313, "humor": 0.0006645102, "introspection": 0.0007249442, "learning": 0.0005818103, "ml_ai": 0.000560274...
89	roleplay	0.016722	humor	0.016444	0.000278	0.999873	0.071617	0.000009	{ "brainstorming": 0.0158127528, "business": 0.0159136876, "community": 0.0161977634, "core_technical": 0.0156765189, "creative_writing": 0.0164042804, "data": 0.0155975539, "design": 0.0160032362, "humor": 0.0164438672, "introspection": 0.0164390393, "learning": 0.0158513505, "ml_ai": 0.015029199...
90	tool_use	0.003538	community	0.00343	0.000108	0.995188	0.043903	0.000211	{ "brainstorming": 0.0021350572, "business": 0.0028491227, "community": 0.0034295069, "core_technical": 0.0023064003, "creative_writing": 0.0019325156, "data": 0.0027626923, "design": 0.0028295142, "humor": 0.0024096186, "introspection": 0.0020272301, "learning": 0.0030857364, "ml_ai": 0.002838654...
91	research	-0.006493	ml_ai	-0.006685	0.000192	0.999689	0.035013	0.000011	{ "brainstorming": -0.0070546479, "business": -0.0067512835, "community": -0.0073724086, "core_technical": -0.0070199622, "creative_writing": -0.0071371817, "data": -0.0068611698, "design": -0.0074273092, "humor": -0.0068493341, "introspection": -0.0072181812, "learning": -0.007600537, "ml_ai": -0...
92	creative_writing	0.004587	humor	0.004503	0.000084	0.996506	0.090563	0.000316	{ "brainstorming": 0.0037101633, "business": 0.0040297331, "community": 0.0042018145, "core_technical": 0.0033521447, "creative_writing": 0.0045867236, "data": 0.0036108186, "design": 0.0039242306, "humor": 0.0045029358, "introspection": 0.0043868036, "learning": 0.0040621879, "ml_ai": 0.003170487...
93	brainstorming	-0.003312	core_technical	-0.003417	0.000105	0.999305	0.028779	0.00002	{ "brainstorming": -0.0033118711, "business": -0.0037264884, "community": -0.004021551, "core_technical": -0.0034172097, "creative_writing": -0.0039046274, "data": -0.0036893014, "design": -0.0041625593, "humor": -0.003990856, "introspection": -0.0040485659, "learning": -0.0039125662, "ml_ai": -0....
94	introspection	0.004035	learning	0.003765	0.00027	0.998492	0.052521	0.000079	{ "brainstorming": 0.0034169375, "business": 0.0035903046, "community": 0.0036321918, "core_technical": 0.002737138, "creative_writing": 0.0032756138, "data": 0.0034887949, "design": 0.0032058412, "humor": 0.0031766705, "introspection": 0.0040346724, "learning": 0.0037650813, "ml_ai": 0.0029723414...
95	research	0.000979	data	0.000535	0.000444	0.710293	0.043884	0.012713	{ "brainstorming": 0.0000810264, "business": 0.000197131, "community": 0.0001033297, "core_technical": -0.0000418775, "creative_writing": -0.0001937128, "data": 0.0005352798, "design": 0.0000102349, "humor": -0.0003250364, "introspection": -0.0002727333, "learning": 0.0003506629, "ml_ai": 0.000307...
96	core_technical	-0.013585	planning	-0.013619	0.000034	0.999872	0.040677	0.000005	{ "brainstorming": -0.0139006041, "business": -0.0139287785, "community": -0.0139982263, "core_technical": -0.0135845114, "creative_writing": -0.0147599084, "data": -0.0137629053, "design": -0.014180555, "humor": -0.0149972429, "introspection": -0.0139290979, "learning": -0.013930453, "ml_ai": -0....
97	ml_ai	-0.001477	business	-0.001782	0.000305	0.996946	0.027491	0.000084	{ "brainstorming": -0.0023222007, "business": -0.0017824192, "community": -0.0022478311, "core_technical": -0.0020632155, "creative_writing": -0.0025426126, "data": -0.0018798951, "design": -0.0019168376, "humor": -0.0024598003, "introspection": -0.0022952151, "learning": -0.002026347, "ml_ai": -0...
98	learning	-0.00737	introspection	-0.007485	0.000115	0.999672	0.037467	0.000012	{ "brainstorming": -0.0076016882, "business": -0.0079064062, "community": -0.0077074254, "core_technical": -0.0088332938, "creative_writing": -0.0080910213, "data": -0.0076028672, "design": -0.0077935299, "humor": -0.0081524607, "introspection": -0.0074849138, "learning": -0.0073703513, "ml_ai": -...
99	tool_use	0.002329	introspection	0.001894	0.000435	0.97504	0.042059	0.00105	{ "brainstorming": 0.0007705402, "business": 0.0008261022, "community": 0.0013826755, "core_technical": 0.0005702121, "creative_writing": 0.0007967644, "data": 0.0013380167, "design": 0.0014828166, "humor": 0.0005717526, "introspection": 0.0018936673, "learning": 0.0016637106, "ml_ai": 0.001105503...
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