Datasets:

---

LogicLark-QuantumQuill

/

chess-bot-datasets

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
schema_version: int64
kind: string
created_at_utc: string
dataset_name: string
version: string
source_dir: string
file_count: int64
total_bytes: int64
dataset_format: string
runtime_splice_cache: struct<schema_version: int64, kind: string, created_at_utc: string, dataset_dir: string, config: struct<min_context: int64, min_target: int64, max_samples_per_game: int64, seed: int64>, splits: struct<train: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, val: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, test: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>>>
required_files_present: struct<train.jsonl: bool, val.jsonl: bool>
stats_json: struct<dataset_format: string, input_path: string, output_dir: string, schema_move_field: string, source_validated_move_alias_supported: list<item: string>, keep_headers: bool, runtime_splice_defaults: struct<min_context: int64, min_target: int64>, split_seed: int64, train_ratio: double, val_ratio: double, decisive_only: bool, input_games_total: int64, input_games_after_filters: int64, spliceable_games: int64, split_games: struct<train: int64, val: int64, test: int64>, split_plies_total: struct<train: int64, val: int64, test: int64>, split_avg_plies: struct<train: double, val: double, test: double>, split_winner_counts: struct<train: struct<W: int64, B: int64>, val: struct<W: int64, B: int64>, test: struct<W: int64, B: int64>>>
files: list<item: struct<path: string, size_bytes: int64, sha256: string>>
distribution: struct<mode: string, archive_name: string>
vs
schema_version: int64
kind: string
created_at_utc: string
dataset_name: string
version: string
source_dir: string
file_count: int64
total_bytes: int64
dataset_format: string
runtime_splice_cache: struct<schema_version: int64, kind: string, created_at_utc: string, dataset_dir: string, config: struct<min_context: int64, min_target: int64, max_samples_per_game: int64, seed: int64>, splits: struct<train: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, val: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, test: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>>>
required_files_present: struct<train.jsonl: bool, val.jsonl: bool>
stats_json: struct<dataset_format: string, input_path: string, output_dir: string, schema_move_field: string, source_validated_move_alias_supported: list<item: string>, keep_headers: bool, runtime_splice_defaults: struct<min_context: int64, min_target: int64>, split_seed: int64, train_ratio: double, val_ratio: double, decisive_only: bool, input_games_total: int64, input_games_after_filters: int64, spliceable_games: int64, split_games: struct<train: int64, val: int64, test: int64>, split_plies_total: struct<train: int64, val: int64, test: int64>, split_avg_plies: struct<train: double, val: double, test: double>, split_winner_counts: struct<train: struct<W: int64, B: int64>, val: struct<B: int64, W: int64>, test: struct<B: int64, W: int64>>>
files: list<item: struct<path: string, size_bytes: int64, sha256: string>>
distribution: struct<mode: string, archive_name: string>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              schema_version: int64
              kind: string
              created_at_utc: string
              dataset_name: string
              version: string
              source_dir: string
              file_count: int64
              total_bytes: int64
              dataset_format: string
              runtime_splice_cache: struct<schema_version: int64, kind: string, created_at_utc: string, dataset_dir: string, config: struct<min_context: int64, min_target: int64, max_samples_per_game: int64, seed: int64>, splits: struct<train: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, val: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, test: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>>>
              required_files_present: struct<train.jsonl: bool, val.jsonl: bool>
              stats_json: struct<dataset_format: string, input_path: string, output_dir: string, schema_move_field: string, source_validated_move_alias_supported: list<item: string>, keep_headers: bool, runtime_splice_defaults: struct<min_context: int64, min_target: int64>, split_seed: int64, train_ratio: double, val_ratio: double, decisive_only: bool, input_games_total: int64, input_games_after_filters: int64, spliceable_games: int64, split_games: struct<train: int64, val: int64, test: int64>, split_plies_total: struct<train: int64, val: int64, test: int64>, split_avg_plies: struct<train: double, val: double, test: double>, split_winner_counts: struct<train: struct<W: int64, B: int64>, val: struct<W: int64, B: int64>, test: struct<W: int64, B: int64>>>
              files: list<item: struct<path: string, size_bytes: int64, sha256: string>>
              distribution: struct<mode: string, archive_name: string>
              vs
              schema_version: int64
              kind: string
              created_at_utc: string
              dataset_name: string
              version: string
              source_dir: string
              file_count: int64
              total_bytes: int64
              dataset_format: string
              runtime_splice_cache: struct<schema_version: int64, kind: string, created_at_utc: string, dataset_dir: string, config: struct<min_context: int64, min_target: int64, max_samples_per_game: int64, seed: int64>, splits: struct<train: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, val: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>, test: struct<game_rows_total: int64, sample_rows_total: int64, total_cache_bytes: int64, files: struct<path_ids.u32.bin: struct<rel_path: string, size_bytes: int64>, offsets.u64.bin: struct<rel_path: string, size_bytes: int64>, splice_indices.u32.bin: struct<rel_path: string, size_bytes: int64>, sample_phase_ids.u8.bin: struct<rel_path: string, size_bytes: int64>>>>>
              required_files_present: struct<train.jsonl: bool, val.jsonl: bool>
              stats_json: struct<dataset_format: string, input_path: string, output_dir: string, schema_move_field: string, source_validated_move_alias_supported: list<item: string>, keep_headers: bool, runtime_splice_defaults: struct<min_context: int64, min_target: int64>, split_seed: int64, train_ratio: double, val_ratio: double, decisive_only: bool, input_games_total: int64, input_games_after_filters: int64, spliceable_games: int64, split_games: struct<train: int64, val: int64, test: int64>, split_plies_total: struct<train: int64, val: int64, test: int64>, split_avg_plies: struct<train: double, val: double, test: double>, split_winner_counts: struct<train: struct<W: int64, B: int64>, val: struct<B: int64, W: int64>, test: struct<B: int64, W: int64>>>
              files: list<item: struct<path: string, size_bytes: int64, sha256: string>>
              distribution: struct<mode: string, archive_name: string>