Dataset Viewer
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
model: string
layer: int64
axes: list<item: string>
child 0, item: string
arms: list<item: string>
child 0, item: string
reps: int64
n_gen: int64
c: double
max_new_tokens: int64
equalise_cell_norm: bool
iso_cost: list<item: double>
child 0, item: double
gram: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
n_trials_per_arm: int64
seed: int64
plan_sha256: string
ekman: struct<anger: list<item: double>, disgust: list<item: double>, fear: list<item: double>, joy: list<i (... 72 chars omitted)
child 0, anger: list<item: double>
child 0, item: double
child 1, disgust: list<item: double>
child 0, item: double
child 2, fear: list<item: double>
child 0, item: double
child 3, joy: list<item: double>
child 0, item: double
child 4, sadness: list<item: double>
child 0, item: double
child 5, surprise: list<item: double>
child 0, item: double
gram_condition: double
tokens: list<item: string>
child 0, item: string
coords: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
scale: list<item: double>
child 0, item: double
note: string
dual: null
c_star: double
poles: struct<valence: struct<pos: list<item: string>, neg: list<item: string>>, heat: struct<pos: list<ite (... 396 chars omitted)
child 0, valence: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 1, heat: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 2, arousal: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 3, intensity: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 4, antagonism_peace: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 5, body_mind: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 6, social_inner_outer: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
to
{'model': Value('string'), 'layer': Value('int64'), 'c_star': Value('float64'), 'axes': List(Value('string')), 'poles': {'valence': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'heat': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'arousal': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'intensity': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'antagonism_peace': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'body_mind': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'social_inner_outer': {'pos': List(Value('string')), 'neg': List(Value('string'))}}, 'gram': List(List(Value('float64'))), 'dual': Value('null'), 'gram_condition': Value('float64'), 'iso_cost': List(Value('float64')), 'scale': List(Value('float64')), 'tokens': List(Value('string')), 'coords': List(List(Value('int64'))), 'ekman': {'anger': List(Value('float64')), 'disgust': List(Value('float64')), 'fear': List(Value('float64')), 'joy': List(Value('float64')), 'sadness': List(Value('float64')), 'surprise': List(Value('float64'))}, 'note': 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 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
model: string
layer: int64
axes: list<item: string>
child 0, item: string
arms: list<item: string>
child 0, item: string
reps: int64
n_gen: int64
c: double
max_new_tokens: int64
equalise_cell_norm: bool
iso_cost: list<item: double>
child 0, item: double
gram: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
n_trials_per_arm: int64
seed: int64
plan_sha256: string
ekman: struct<anger: list<item: double>, disgust: list<item: double>, fear: list<item: double>, joy: list<i (... 72 chars omitted)
child 0, anger: list<item: double>
child 0, item: double
child 1, disgust: list<item: double>
child 0, item: double
child 2, fear: list<item: double>
child 0, item: double
child 3, joy: list<item: double>
child 0, item: double
child 4, sadness: list<item: double>
child 0, item: double
child 5, surprise: list<item: double>
child 0, item: double
gram_condition: double
tokens: list<item: string>
child 0, item: string
coords: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
scale: list<item: double>
child 0, item: double
note: string
dual: null
c_star: double
poles: struct<valence: struct<pos: list<item: string>, neg: list<item: string>>, heat: struct<pos: list<ite (... 396 chars omitted)
child 0, valence: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 1, heat: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 2, arousal: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 3, intensity: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 4, antagonism_peace: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 5, body_mind: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
child 6, social_inner_outer: struct<pos: list<item: string>, neg: list<item: string>>
child 0, pos: list<item: string>
child 0, item: string
child 1, neg: list<item: string>
child 0, item: string
to
{'model': Value('string'), 'layer': Value('int64'), 'c_star': Value('float64'), 'axes': List(Value('string')), 'poles': {'valence': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'heat': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'arousal': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'intensity': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'antagonism_peace': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'body_mind': {'pos': List(Value('string')), 'neg': List(Value('string'))}, 'social_inner_outer': {'pos': List(Value('string')), 'neg': List(Value('string'))}}, 'gram': List(List(Value('float64'))), 'dual': Value('null'), 'gram_condition': Value('float64'), 'iso_cost': List(Value('float64')), 'scale': List(Value('float64')), 'tokens': List(Value('string')), 'coords': List(List(Value('int64'))), 'ekman': {'anger': List(Value('float64')), 'disgust': List(Value('float64')), 'fear': List(Value('float64')), 'joy': List(Value('float64')), 'sadness': List(Value('float64')), 'surprise': List(Value('float64'))}, 'note': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
From Emotion Words to Steering Dials — data
The full data behind the paper From Emotion Words to Steering Dials: A Prototype Interface and Its Measured Limits: activation vectors at every layer, every steered generation, and the interface atlases, for nine language models.
| paper | link with the published version |
| verify every number (laptop, CPU) | https://github.com/Sinerror/Human-to-LLM-interface-verify |
| rebuild everything from the prompts | https://github.com/Sinerror/Human-to-LLM-interface-reproduce |
| the interface, live | https://sinerror.github.io/Human-to-LLM-interface/ |
Most readers want the verify repository, which ships a small subset of this data and recomputes every reported number. This dataset is for reproduction and re-analysis.
Layout
One folder per model, laid out like the reproduction pipeline's work tree, so
recompute_all.py --from-work <this folder> reads it directly.
| path | contents |
|---|---|
<model>/<model>_vecs.npz |
hidden states for 795 word senses, every layer |
<model>/<model>_heldout_vecs.npz |
the pre-registered unseen words, extracted separately |
<model>/atlas_<model>.json |
the interface atlas: axes, Gram matrix, token coordinates (int8), Ekman positions |
<model>/gens/ |
three dials at a time, full factorial, at the model's coherence edge c*: raw, dual-basis and matched random-control arms |
<model>/gens_single/ |
the same trials and stems with one dial turned per cell (the superposition test): raw and dual arms |
<model>/gens_common/ |
the archived run at one common magnitude, c = 0.08, raw arm |
edges.json |
the coherence ladders and each model's c* |
layer_selection.csv |
per-layer held-out AUC, which chose each model's layer |
results/ |
the analysis outputs behind the paper's numbers and figures |
Models: Gemma-3-1B-it and -pt, Gemma-2-9B (4-bit), Qwen2.5-1.5B, Phi-2, Mistral-7B-v0.3, Llama-3-8B, Pythia-6.9B, Mamba-2.8B.
Generated text
Generations are model output produced under activation steering, often at the edge of fluency. Some are degenerate, and some are emotionally charged by construction. They are research artifacts, not endorsed content.
Licence
CC BY-SA 4.0: sense glosses from Wiktionary are part of every sense key, and
share-alike follows them. See LICENCE for the Wiktionary and WordNet notices.
The vectors, generations and atlases derive from the models, and each model's
own licence may apply to the files derived from it; copies are in licenses/.
Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.
Built with Meta Llama 3. Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
Qwen2.5-1.5B, Mistral-7B-v0.3, Pythia-6.9B and Mamba-2.8B are under the Apache License 2.0, and Phi-2 under the MIT License, as stated on their model cards.
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