The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ranges/[]/layout) changed from object to array in row 0
During handling of the above exception, another exception occurred:
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 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Flychess Stockfish 500k
A reproducible, sharded dataset of 500,000 legal chess positions with Stockfish depth-3 move and bounded value labels for training and evaluating Flychess FlyNet.
This repository is the canonical 500k dataset release. It contains binary training shards, integrity sidecars, a machine-readable manifest, and the schema files needed to interpret the arrays without guessing.
Release identity
- Dataset: Flychess Stockfish 500k
- Canonical release prefix: campaign/depth3/segment-000-500k-fresh-20260921
- Samples: 500,000
- Shards: 5 x 100,000 positions
- Feature width: 851
- Action vocabulary: 4,544 deterministic UCI move shapes
- Teacher: Stockfish, depth 3
- Position sampler: uniform_legal_playout_per_global_index
- Plies per sampled position: 4-60
- Generation seed: 20260921
- Source commit: 1efceab534244096b9af309262913abdf0be2982
- Schema version: flynet.sharded_dataset/v1
The manifest is authoritative for exact counts, shard boundaries, checksums, and provenance. The three schema files in the release prefix are authoritative for array interpretation.
Repository layout
campaign/depth3/segment-000-500k-fresh-20260921/
|- manifest.json
|- feature-schema.json
|- label-schema.json
|- action-vocabulary.json
|- shard-000000.npz
|- shard-000000.json
|- ...
|- shard-000004.npz
|- shard-000004.json
Each NPZ shard contains four arrays:
| Array | Shape | Stored dtype | Meaning |
|---|---|---|---|
| features | [N, 851] | float16 | FlyNet board encoding |
| action_indices | [N] | uint16 | Index into action-vocabulary.json |
| values | [N] | float16 | Bounded side-to-move teacher value in [-1, 1] |
| fens | [N] | Unicode string | Exact FEN for the sampled position |
The per-shard JSON sidecar records the sample range, byte count, storage contract, and SHA-256 digest for the corresponding NPZ file.
Loading a shard
Use allow_pickle=False when reading the files:
from pathlib import Path
import numpy as np
from huggingface_hub import snapshot_download
repo_id = "YOUR_HF_NAMESPACE/flychess-stockfish-500k"
prefix = "campaign/depth3/segment-000-500k-fresh-20260921"
root = Path(snapshot_download(
repo_id=repo_id,
repo_type="dataset",
allow_patterns=[f"{prefix}/*"],
))
with np.load(root / prefix / "shard-000000.npz", allow_pickle=False) as shard:
features = shard["features"]
action_indices = shard["action_indices"]
values = shard["values"]
fens = shard["fens"]
print(features.shape, action_indices.shape, values.shape, fens.shape)
The source project provides the compatible FlyNet encoder and training loader. The schema files make the storage contract independently inspectable.
Feature contract
The 851 features are ordered as follows:
- 768 piece-square indicators: white then black, six piece types, 64 squares.
- One side-to-move scalar: +1 for White and -1 for Black.
- Four castling-right indicators.
- A 64-element en-passant-square one-hot region.
- Halfmove and fullmove counters with the documented clipping and scaling.
- Twelve normalized piece-count features.
See feature-schema.json for exact half-open index ranges and semantics.
Provenance and limitations
- Positions are generated from legal chess playouts; they are not human game records.
- Stockfish supplies the move and bounded value labels. Depth 3 is a low-cost teacher setting, not ground truth or a claim of playing strength.
- This release is a versioned publication of a verified 500,000-position snapshot. The manifest records the source prefix and source commit; the upload itself does not claim that all positions were newly generated during publication.
- The data encodes the FlyNet feature contract. It is an engineering dataset, not a biological connectome or evidence of biological cognition.
- The dataset is stored as NumPy NPZ rather than Apache Arrow/Parquet, so the full feature arrays are intended for programmatic loading rather than automatic row preview in the Hub Dataset Viewer.
Licensing and attribution
The dataset contains generated labels produced with Stockfish; it does not redistribute Stockfish source code or a Stockfish binary. Stockfish is distributed under GPLv3. Review the upstream Stockfish license and the Flychess source provenance before redistributing or incorporating this dataset into another release.
- Stockfish: https://github.com/official-stockfish/Stockfish
- Flychess source project: https://github.com/EF-Code/flychess
No broad permissive license is asserted for this generated artifact beyond the applicable third-party terms and the provenance recorded in manifest.json.
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