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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
clean_up_tokenization_spaces: bool
eos_token: null
model_max_length: int64
tokenizer_class: string
cls_token: string
unk_token: string
mask_token: string
pad_token: string
to
{'cls_token': Value('string'), 'mask_token': Value('string'), 'pad_token': Value('string'), 'unk_token': 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
              clean_up_tokenization_spaces: bool
              eos_token: null
              model_max_length: int64
              tokenizer_class: string
              cls_token: string
              unk_token: string
              mask_token: string
              pad_token: string
              to
              {'cls_token': Value('string'), 'mask_token': Value('string'), 'pad_token': Value('string'), 'unk_token': Value('string')}
              because column names don't match

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OmniMind Nucleotide Transformer v2 50m Multi-Species Patch

Repositório de modelo compatível com transformers >= 4.50 e ambiente Kaggle.

Origem

Base: InstaDeepAI/nucleotide-transformer-v2-50m-multi-species
Arquivos de peso (model.safetensors) são idênticos ao original.
modeling_esm.py e esm_config.py são a versão do cache local que carrega corretamente com transformers 4.57.4.

Modificações

modeling_esm.py: adicionado fallback local para find_pruneable_heads_and_indices e prune_linear_layer (removidos em versões recentes do transformers.pytorch_utils).
Dimensões do FFN GLU ajustadas para bater com o checkpoint: EsmIntermediate projeta para intermediate_size e EsmOutput projeta de intermediate_size // 2 para hidden_size.

Uso

from transformers import AutoTokenizer, AutoModelForMaskedLM
tok = AutoTokenizer.from_pretrained('fabricioslv/omnimind-nucleotide-transformer-v2-patch', trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained('fabricioslv/omnimind-nucleotide-transformer-v2-patch', trust_remote_code=True)

Provenance

Modelo: InstaDeepAI (Apache 2.0)
Patch: OmniMind, 2026-08-19

Downloads last month: 16

Total file size:

224 MB