Hugging Face's logo

Datasets:
abotresol
/
neutral-vectors-gemma-4-31b-it-postfix

Tasks:
Feature Extraction
Tags:
interpretability
emotion
activation-vectors
gemma
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
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
story_id: string
emotion: string
story_index: int64
sha1: string
n_tokens: int64
error: null
neutral: struct<0: list<item: double>, 3: list<item: double>, 6: list<item: double>, 9: list<item: double>, 1 (... 382 chars omitted)
  child 0, 0: list<item: double>
      child 0, item: double
  child 1, 3: list<item: double>
      child 0, item: double
  child 2, 6: list<item: double>
      child 0, item: double
  child 3, 9: list<item: double>
      child 0, item: double
  child 4, 12: list<item: double>
      child 0, item: double
  child 5, 15: list<item: double>
      child 0, item: double
  child 6, 18: list<item: double>
      child 0, item: double
  child 7, 21: list<item: double>
      child 0, item: double
  child 8, 24: list<item: double>
      child 0, item: double
  child 9, 27: list<item: double>
      child 0, item: double
  child 10, 30: list<item: double>
      child 0, item: double
  child 11, 33: list<item: double>
      child 0, item: double
  child 12, 36: list<item: double>
      child 0, item: double
  child 13, 39: list<item: double>
      child 0, item: double
  child 14, 42: list<item: double>
      child 0, item: double
  child 15, 45: list<item: double>
      child 0, item: double
  child 16, 48: list<item: double>
      child 0, item: double
  child 17, 51: list<item: double>
      child 0, item: double
  child 18, 54: list<item: double>
      child 0, item: double
  child 19, 57: list<item: double>
      child 0, item: double
to
{'neutral': {'0': List(Value('float64')), '3': List(Value('float64')), '6': List(Value('float64')), '9': List(Value('float64')), '12': List(Value('float64')), '15': List(Value('float64')), '18': List(Value('float64')), '21': List(Value('float64')), '24': List(Value('float64')), '27': List(Value('float64')), '30': List(Value('float64')), '33': List(Value('float64')), '36': List(Value('float64')), '39': List(Value('float64')), '42': List(Value('float64')), '45': List(Value('float64')), '48': List(Value('float64')), '51': List(Value('float64')), '54': List(Value('float64')), '57': List(Value('float64'))}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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
              story_id: string
              emotion: string
              story_index: int64
              sha1: string
              n_tokens: int64
              error: null
              neutral: struct<0: list<item: double>, 3: list<item: double>, 6: list<item: double>, 9: list<item: double>, 1 (... 382 chars omitted)
                child 0, 0: list<item: double>
                    child 0, item: double
                child 1, 3: list<item: double>
                    child 0, item: double
                child 2, 6: list<item: double>
                    child 0, item: double
                child 3, 9: list<item: double>
                    child 0, item: double
                child 4, 12: list<item: double>
                    child 0, item: double
                child 5, 15: list<item: double>
                    child 0, item: double
                child 6, 18: list<item: double>
                    child 0, item: double
                child 7, 21: list<item: double>
                    child 0, item: double
                child 8, 24: list<item: double>
                    child 0, item: double
                child 9, 27: list<item: double>
                    child 0, item: double
                child 10, 30: list<item: double>
                    child 0, item: double
                child 11, 33: list<item: double>
                    child 0, item: double
                child 12, 36: list<item: double>
                    child 0, item: double
                child 13, 39: list<item: double>
                    child 0, item: double
                child 14, 42: list<item: double>
                    child 0, item: double
                child 15, 45: list<item: double>
                    child 0, item: double
                child 16, 48: list<item: double>
                    child 0, item: double
                child 17, 51: list<item: double>
                    child 0, item: double
                child 18, 54: list<item: double>
                    child 0, item: double
                child 19, 57: list<item: double>
                    child 0, item: double
              to
              {'neutral': {'0': List(Value('float64')), '3': List(Value('float64')), '6': List(Value('float64')), '9': List(Value('float64')), '12': List(Value('float64')), '15': List(Value('float64')), '18': List(Value('float64')), '21': List(Value('float64')), '24': List(Value('float64')), '27': List(Value('float64')), '30': List(Value('float64')), '33': List(Value('float64')), '36': List(Value('float64')), '39': List(Value('float64')), '42': List(Value('float64')), '45': List(Value('float64')), '48': List(Value('float64')), '51': List(Value('float64')), '54': List(Value('float64')), '57': List(Value('float64'))}}
              because column names don't match

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.

Emotion vectors, google/gemma-4-31b-it (corrected extraction)

Residual-stream activations for google/gemma-4-31b-it, pooled per story and averaged per emotion. Each emotion ends up as one direction in the model's activation space.

Read LINEAGE.md before using this. This set supersedes abotresol/neutral-vectors-gemma-4-31b-it. The earlier extraction ran while the tokenizer padded on the left, so the step that skips a story's first 50 tokens skipped padding instead. This set re-extracts the same corpus with padding forced to the right. LINEAGE.md gives the measured before-and-after impact. The predecessor stays up, unmodified, as the "before" side of that comparison.

What is in it

Path Contents
<emotion>/layer_<N>_resid.npy the mean vector for one emotion at one layer
emotion_vectors.json the same per-emotion means, in the reference's format
shards/<emotion>__<idx>.npy one [layers, d_model] array per story, before averaging
manifest.jsonl per-story metadata: emotion, index, text SHA-1, token count
run_config.json the full extraction configuration
LINEAGE.md what this set corrects, and by how much

Shards are published so the per-emotion means can be recomputed, resampled or subsetted without running the model again.

How it was made

  • Corpus: abotresol/neutral-transcripts-gemma-4-31b-it, transcripts written to carry no emotion
  • Layers: every third, 0 to 57
  • Pooling: mean over non-padding tokens after position 50, stories truncated at 512 tokens. The first 50 tokens are dropped as narrative framing, which is the convention the source paper used.
  • Precision: bf16 weights, fp32 activations. Seed 20260720.
  • Across stories: a token-weighted mean, so a long story counts for more than a short one.

Extracted by scripts/extract_emotion_vectors.py in gemma4-emotion-vectors, adapted from sinievanderben/emotion_experiment.

Reproducing

Re-extracting needs the model weights and a GPU with enough memory for a 31B model in bf16. Analysis does not: the per-emotion means in this repository are enough to redo the geometry and detection work on a laptop.

uv run python scripts/extract_emotion_vectors.py

Caveats

A 2-3 day research sprint, not a reviewed publication. The write-up (https://github.com/Antonio-Tresol/gemma4-emotion-vectors) records which findings survived a falsification pass and which did not. These vectors describe a model reading emotions in text; that is a different claim from the model having them.

Licence

MIT, matching the project repository. The model weights and the story corpora carry their own licences.
Downloads last month
245
Total file size:
58.3 MB