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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:    TypeError
Message:      Couldn't cast array of type
struct<currency_f1_delta: double, macro_f1_delta: double, seed: int64, time_range_f1_delta: double>
to
{'exact_vector_delta_main_minus_sampling': Value('float64'), 'macro_f1_delta_main_minus_sampling': Value('float64'), 'seed': Value('int64')}
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 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<currency_f1_delta: double, macro_f1_delta: double, seed: int64, time_range_f1_delta: double>
              to
              {'exact_vector_delta_main_minus_sampling': Value('float64'), 'macro_f1_delta_main_minus_sampling': Value('float64'), 'seed': Value('int64')}

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.

TimeMemory — private academic research artifacts

This repository is a private migration archive, not a public dataset release or a single uniform load_dataset table.

Companion code, detailed recovery manual and sanitized research/conversation history: GitHub liofoil/timememory.

Start with GitHub RESTORE.md, CODEX_HANDOFF.md, and docs/RESEARCH_HISTORY.md. Download only the explicit files in migration/artifacts_manifest.json, using the frozen HF commit recorded in the companion GitHub migration/artifact_release.json.

Contents: eight consecutive open research stages, base examples, lifetime fitting, calibration, generated controlled edits, split manifests, predictions, training checkpoints, all failed attempts/configuration logs, API usage estimates, tables and reports; plus authorized STALE/LoCoMo development partitions. The latest result folder is nonmonotonic_memory/results/step6-clean-confirmation-v1/.

The current strongest result is specialized factor checking on a controlled edit benchmark. Natural support transfer remains poor; system-level rejudging/reanswering results are provisional because the designated third-model arbitration file was unavailable. Do not promote them to confirmed claims by downloading this snapshot.

Redlines: no sealed/reserved partitions, human/blind review packs, raw Codex chats, clipboard, credentials, SSH keys or foundation-model caches. Exclusions are documented; nothing was deleted from the source machine.

Mixed upstream terms apply, with no blanket license grant from this repository:

  • HaluMem: official card currently lists CC-BY-NC-ND-4.0; only existing authorized development research caches are migrated privately, not the mixed-boundary full raw corpus.
  • STALE: local source manifest records CC-BY-4.0.
  • LoCoMo: CC-BY-NC-4.0.
  • Foundation weights are not redistributed. Obtain exact upstream revisions and required gated access; custom checkpoints must be loaded with the matching original backbone and research code.

Keep this repository private. Academic purpose and privacy do not supersede source licenses. Review per-source terms and attribution before any future public release.

For interrupted transfer, rerun the manifest-based uploader/restorer. Use low concurrency and respect rate-limit reset headers; do not make one commit per file or repeatedly poll the API.

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