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 3422, 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 2187, 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 2391, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, 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 1904, 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 499, in _iter_arrow
for key, pa_table in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, 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.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
"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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