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Datasets:
vedangfake
/
chess-slm-large-assets

Tasks:
Text Classification
Languages:
English
ArXiv:
Tags:
chess
mate
reasoning
gemma
lora
distillation
License:
Dataset card Files
xet
 Community
Dataset Viewer
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 249, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from 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 327, in _generate_tables
                  raise e
                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
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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Chess SLM Benchmark — Large Assets (mirror for GitHub-overflow files)

Mirror of files that exceed GitHub's 50 MB soft limit / 100 MB hard limit in the repo Vedang-P/chess-slm-benchmark (branch main). Every file here is a reproducible artifact: it can be regenerated from the repo's own scripts + the public MATE dataset. The GitHub repo documents how to regenerate each one (see repo_context.readme → "Oversized data assets").

Layout (mirrors repo paths):

path_in_repo size description
data/positions/mate-lora/train.jsonl 103 MB Gemma-4-E2B LoRA training set: 200,000 MATE noexplain rows as chat pairs (FEN + 2-candidate prompt → MoveX:<move>), test-set FENs excluded, seed 42
data/positions/mate-lora/eval.jsonl 2.9 MB 5,000 row eval split, position-disjoint from train (below GitHub limit; mirrored for completeness)
data/raw/mate-train/strategy.zip 70 MB Raw MATE strategy train zips (as downloaded from the MATE dataset)
data/raw/mate-train/no_explain.zip 52 MB Raw MATE noexplain train zips (as downloaded from the MATE dataset)
results/clean-1000/deepseek-v4-flash_mate-selection-test_strategy.samples.jsonl 60 MB Raw per-sample teacher (deepseek-v4-flash, thinking) records on the 1000-position strategy subset: full prompt, thinking chain, answer, verdict, latency

Sources and citations

  • MATE dataset — Wang, X., Zhang, M., and Wang, C. (2025). MATE: A Large-Scale, Multilingual Dataset for LLM-Based Chess Reasoning. NAACL 2025. arXiv:2411.06655. Dataset repo: OutFlankShu/MATE_DATASET (redirect target of OutFlankShu/MATE_NAACL2025_...), downloadable via https://huggingface.co/datasets/OutFlankShu/MATE_DATASET/resolve/main/. The zips are unmodified downloads of that dataset's train splits (explain_dataset*_strategy.jsonl, explain_dataset*_noexplain.jsonl). Download/build code: scripts/build_mate_c1_data.py (in the GitHub repo).
  • Teacher model — deepseek-v4-flash (opencode-go gateway API). The samples file records its own generated reasoning traces; run ids and prompts are embedded per line. Evaluation harness: scripts/run_mate_eval.py.

How these files were produced

  1. scripts/build_mate_c1_data.py download → data/raw/mate-train/*.zip (stored here as-is).
  2. scripts/build_mate_lora_data.py --n-train 200000 --n-eval 5000 --seed 42 → data/positions/mate-lora/{train,eval}.jsonl. Train keeps near-duplicate FENs (fine for selection SFT); eval is position-disjoint from train and from the MATE test set (contamination hygiene, verified at build time).
  3. scripts/run_mate_eval.py --subset strategy --model deepseek-v4-flash --thinking over data/positions/mate-selection-test.json → results/clean-1000/deepseek-v4-flash_mate-selection-test_strategy.samples.jsonl.

Regeneration 

git clone https://github.com/Vedang-P/chess-slm-benchmark.git
python3 scripts/build_mate_c1_data.py download        # needs data/raw/ layout
python3 scripts/build_mate_lora_data.py               # default 200k/5k, seed 42

Checksum files (.sha256) for each mirrored file are available in the checksums/ folder of this dataset repo.
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Total file size:
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Paper for vedangfake/chess-slm-large-assets