The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
df = pandas_read_json(f)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
obj = self._get_object_parser(self.data)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
self._parse()
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1403, 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 228, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3357, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2111, in _head
return next(iter(self.iter(batch_size=n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2315, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 476, in _iter_arrow
for key, pa_table in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 323, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables
raise e
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
"Tic Tac Toe Tricks There are several distinct strategies that can be employed to ensure victory when playing tic tac toe, but there are also a few simple tricks that new players can use to help their chances. Remember, this game is known as a 'solved game', which means that there is a definite strategy that can be employed to win every single time. However, if both players employ that same unbeatable strategy, the game will result in a draw every time." https://www.siammandalay.com/2021/05/18/how-to-win-tic-tac-toe-tricks-to-always-win-noughts-crosses/
model initialized on cpu. ReplayBuffer initialized with capacity: 50000 ReplayBuffer initialized with capacity: 50000 Loaded 12 seed examples for player X (after augmentation if any) into ReplayBuffer. Loaded 12 seed examples for player O (after augmentation if any) into ReplayBuffer. Loaded 8 seed examples for player X (after augmentation if any) into ReplayBuffer. Loaded 8 seed examples for player O (after augmentation if any) into ReplayBuffer. PygameDisplay initialized. GameLogger initialized. Logging to: ttt_runs_output_optimized\run_optimized_v1.0_20250605_091852\game_logs, Images to: ttt_runs_output_optimized\run_optimized_v1.0_20250605_091852\image_frames
--- Evaluating models after game 100 --- Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Evaluating models after game 200 --- Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Evaluating models after game 300 --- Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Evaluating models after game 400 --- Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
--- Starting Game 500/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=1655, O=1751 Training after game 500: Avg Loss X: 0.6662, Avg Loss O: 0.9334
--- Evaluating models after game 500 --- Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 600 --- Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 700 --- Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 800 --- Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
--- Evaluating models after game 900 --- Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Starting Game 1000/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=3333, O=3516 Training after game 1000: Avg Loss X: 0.5366, Avg Loss O: 0.7208
--- Evaluating models after game 1000 --- Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Evaluating models after game 1100 --- Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Evaluating models after game 1200 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1300 --- Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
--- Evaluating models after game 1400 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 1500/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=5099, O=5332 Training after game 1500: Avg Loss X: 0.4487, Avg Loss O: 0.6971
--- Evaluating models after game 1500 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1600 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1700 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1800 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 1900 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 2000/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=6989, O=7211 Training after game 2000: Avg Loss X: 0.4598, Avg Loss O: 0.5945
--- Evaluating models after game 2000 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2100 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2200 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2300 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2400 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 2500/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=8869, O=9108 Training after game 2500: Avg Loss X: 0.4708, Avg Loss O: 0.5109
--- Evaluating models after game 2500 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2600 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2700 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2800 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 2900 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 3000/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=10761, O=10998 Training after game 3000: Avg Loss X: 0.4827, Avg Loss O: 0.5137
--- Evaluating models after game 3000 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3100 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3200 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3300 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3400 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 3500/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=12651, O=12881 Training after game 3500: Avg Loss X: 0.3450, Avg Loss O: 0.4849
--- Evaluating models after game 3500 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3600 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3700 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3800 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 3900 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Starting Game 4000/10000 --- LRs: X=1.0e-04, O=1.0e-04. Buffers: X=14521, O=14750 Training after game 4000: Avg Loss X: 0.4175, Avg Loss O: 0.5450
--- Evaluating models after game 4000 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
--- Evaluating models after game 4100 --- Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
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