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Datasets:
kaushikreddyxyz
/
corpus-scores-dom-layer8

Size:
1B<n<10B
Tags:
interpretability
probing
gemma-2
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
n_tokens: int64
mean: list<item: double>
  child 0, item: double
std: list<item: double>
  child 0, item: double
layer: int64
axes: struct<0: string, 1: string>
  child 0, 0: string
  child 1, 1: string
concepts: list<item: string>
  child 0, item: string
method: string
scores_shape: string
families: list<item: string>
  child 0, item: string
to
{'concepts': List(Value('string')), 'families': List(Value('string')), 'layer': Value('int64'), 'method': Value('string'), 'scores_shape': Value('string'), 'axes': {'0': Value('string'), '1': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              n_tokens: int64
              mean: list<item: double>
                child 0, item: double
              std: list<item: double>
                child 0, item: double
              layer: int64
              axes: struct<0: string, 1: string>
                child 0, 0: string
                child 1, 1: string
              concepts: list<item: string>
                child 0, item: string
              method: string
              scores_shape: string
              families: list<item: string>
                child 0, item: string
              to
              {'concepts': List(Value('string')), 'families': List(Value('string')), 'layer': Value('int64'), 'method': Value('string'), 'scores_shape': Value('string'), 'axes': {'0': Value('string'), '1': Value('string')}}
              because column names don't match

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corpus-scores-dom-layer8

Difference-of-means (DoM) STEERING scores for ClimbMix, gemma-2-2b layer 8. Kept separate from the detection score store corpus-scores (overflow).

Files (per shard <sid>, ClimbMix shards 320-362, 43 total)

  • scores_<sid>.npy — int8 [n_tokens, 54]; axis 0 token, axis 1 concept (index into columns.json concepts[])
  • tokens_<sid>.npy — int32 [n_tokens] gemma tokenizer ids
  • docs_<sid>.jsonl — per-document metadata (token spans)

Provenance

All-DoM steering probes from concept-probes-gemma2-2b gold_probes/layer08 applied to gemma-2-2b residual activations (~46.6M tokens/shard, ~2.0B total). Scores standardized per probe over ClimbMix, then int8-quantized (clipped at ±4σ). Dequantize with dom_quant.json: score = int8 * scale[c] + zero[c].
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Total file size:
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