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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
key: string
session: string
block: int64
trial: int64
corpus: string
sentence: string
best: string
nbest: list<item: struct<sentence: string, ac: double, ngram: double, llm: double, total: double>>
child 0, item: struct<sentence: string, ac: double, ngram: double, llm: double, total: double>
child 0, sentence: string
child 1, ac: double
child 2, ngram: double
child 3, llm: double
child 4, total: double
rescore: bool
alpha: double
acoustic_scale: double
length_penalty: double
lm: string
llm: bool
score_penalty_percent: double
opt: string
top_candidates_to_augment: int64
blank_penalty: double
logits: string
to
{'acoustic_scale': Value('float64'), 'blank_penalty': Value('float64'), 'alpha': Value('float64'), 'nbest': Value('int64'), 'rescore': Value('bool'), 'llm': Value('bool'), 'length_penalty': Value('float64'), 'top_candidates_to_augment': Value('int64'), 'score_penalty_percent': Value('float64'), 'lm': Value('string'), 'opt': Value('string'), 'logits': Value('string')}
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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
key: string
session: string
block: int64
trial: int64
corpus: string
sentence: string
best: string
nbest: list<item: struct<sentence: string, ac: double, ngram: double, llm: double, total: double>>
child 0, item: struct<sentence: string, ac: double, ngram: double, llm: double, total: double>
child 0, sentence: string
child 1, ac: double
child 2, ngram: double
child 3, llm: double
child 4, total: double
rescore: bool
alpha: double
acoustic_scale: double
length_penalty: double
lm: string
llm: bool
score_penalty_percent: double
opt: string
top_candidates_to_augment: int64
blank_penalty: double
logits: string
to
{'acoustic_scale': Value('float64'), 'blank_penalty': Value('float64'), 'alpha': Value('float64'), 'nbest': Value('int64'), 'rescore': Value('bool'), 'llm': Value('bool'), 'length_penalty': Value('float64'), 'top_candidates_to_augment': Value('int64'), 'score_penalty_percent': Value('float64'), 'lm': Value('string'), 'opt': Value('string'), 'logits': 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.
CS584 brain-to-text: Experiment 1 baseline artifacts
Prebuilt artifacts for a course project on phoneme-to-text decoding for intracortical speech BCIs, built on
the T15 Brain-to-Text '25 data and pretrained RNN from
Card et al. 2024. Code, full documentation and
reproduce commands: the project's experiments/exp1_ngram_baselines/README.md. Download with
python scripts/hf_baseline.py fetch [--lm] from that repo.
| path | what |
|---|---|
logits/val.npz |
pretrained-RNN phoneme logits for the 1,426 val-split trials, with sentence labels |
decodes/val__*.jsonl |
scored N-best lists (acoustic, n-gram, OPT-6.7B int8 scores) for each LM, one JSON per trial |
ngram_lms/owt2_3gram_1pct_clean_p1e-8/ |
Kaldi TLG.fst decoding graph: 3-gram on a 1% OpenWebText2 sample with all val sentences removed (baseline) |
ngram_lms/owt2_3gram_1pct_asis_p1e-8/ |
same, with val-sentence occurrences kept at the paper-like rate |
Val WER (with OPT-6.7B int8 rescoring): 8.09% (clean), 7.69% (as-is); the paper's released predictions: 6.32%.
The 3-grams follow upstream's LM recipe (SRILM, CMUdict vocabulary, entropy pruning at 1e-8, silence probability 0.9) on text from OpenWebText2 (MIT). Logits and labels are derived from the T15 dataset on Dryad; see its license for terms.
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