attribute error when writing trainer.train()
### Describe the bug

AttributeError                            Traceback (most recent call last)
Cell In[39], line 2
      1 # Start the training process
----> 2 trainer.train()

File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1537         hf_hub_utils.enable_progress_bars()
   1538 else:
-> 1539     return inner_training_loop(
   1540         args=args,
   1541         resume_from_checkpoint=resume_from_checkpoint,
   1542         trial=trial,
   1543         ignore_keys_for_eval=ignore_keys_for_eval,
   1544     )

File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1833     rng_to_sync = True
   1835 step = -1
-> 1836 for step, inputs in enumerate(epoch_iterator):
   1837     total_batched_samples += 1
   1839     if self.args.include_num_input_tokens_seen:

File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self)
    449 # We iterate one batch ahead to check when we are at the end
    450 try:
--> 451     current_batch = next(dataloader_iter)
    452 except StopIteration:
    453     yield

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self)
    627 if self._sampler_iter is None:
    628     # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E)
    629     self._reset()  # type: ignore[call-arg]
--> 630 data = self._next_data()
    631 self._num_yielded += 1
    632 if self._dataset_kind == _DatasetKind.Iterable and \
    633         self._IterableDataset_len_called is not None and \
    634         self._num_yielded > self._IterableDataset_len_called:

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self)
    672 def _next_data(self):
    673     index = self._next_index()  # may raise StopIteration
--> 674     data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    675     if self._pin_memory:
    676         data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
     49         data = self.dataset.__getitems__(possibly_batched_index)
     50     else:
---> 51         data = [self.dataset[idx] for idx in possibly_batched_index]
     52 else:
     53     data = self.dataset[possibly_batched_index]

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0)
     49         data = self.dataset.__getitems__(possibly_batched_index)
     50     else:
---> 51         data = [self.dataset[idx] for idx in possibly_batched_index]
     52 else:
     53     data = self.dataset[possibly_batched_index]

File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
   1762 def __getitem__(self, key):  # noqa: F811
   1763     """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764     return self._getitem(
   1765         key,
   1766     )

File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
   1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
   1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
   1750     pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
   1751 )
   1752 return formatted_output

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns)
    538 else:
    539     pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns)
--> 540     formatted_output = formatter(pa_table_to_format, query_type=query_type)
    541     if output_all_columns:
    542         if isinstance(formatted_output, MutableMapping):

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
    279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
    280     if query_type == "row":
--> 281         return self.format_row(pa_table)
    282     elif query_type == "column":
    283         return self.format_column(pa_table)

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table)
     56 def format_row(self, pa_table: pa.Table) -> dict:
---> 57     row = self.numpy_arrow_extractor().extract_row(pa_table)
     58     return self.recursive_tensorize(row)

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table)
    153 def extract_row(self, pa_table: pa.Table) -> dict:
--> 154     return _unnest(self.extract_batch(pa_table))

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table)
    159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160     return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0)
    159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160     return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array)
    194         array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
    195 if len(array) > 0:
--> 196     if any(
    197         (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
    198         or (isinstance(x, float) and np.isnan(x))
    199         for x in array
    200     ):
    201         return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
    202 return np.array(array, copy=False, **self.np_array_kwargs)

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0)
    194         array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
    195 if len(array) > 0:
    196     if any(
--> 197         (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
    198         or (isinstance(x, float) and np.isnan(x))
    199         for x in array
    200     ):
    201         return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
    202 return np.array(array, copy=False, **self.np_array_kwargs)

File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr)
    319     warnings.warn(
    320         f"In the future `np.{attr}` will be defined as the "
    321         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    323 if attr in __former_attrs__:
--> 324     raise AttributeError(__former_attrs__[attr])
    326 if attr == 'testing':
    327     import numpy.testing as testing

AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. 
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationsAttributeError                            Traceback (most recent call last)
Cell In[39], line 2
      1 # Start the training process
----> 2 trainer.train()

File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1537         hf_hub_utils.enable_progress_bars()
   1538 else:
-> 1539     return inner_training_loop(
   1540         args=args,
   1541         resume_from_checkpoint=resume_from_checkpoint,
   1542         trial=trial,
   1543         ignore_keys_for_eval=ignore_keys_for_eval,
   1544     )

File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1833     rng_to_sync = True
   1835 step = -1
-> 1836 for step, inputs in enumerate(epoch_iterator):
   1837     total_batched_samples += 1
   1839     if self.args.include_num_input_tokens_seen:

File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self)
    449 # We iterate one batch ahead to check when we are at the end
    450 try:
--> 451     current_batch = next(dataloader_iter)
    452 except StopIteration:
    453     yield

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self)
    627 if self._sampler_iter is None:
    628     # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E)
    629     self._reset()  # type: ignore[call-arg]
--> 630 data = self._next_data()
    631 self._num_yielded += 1
    632 if self._dataset_kind == _DatasetKind.Iterable and \
    633         self._IterableDataset_len_called is not None and \
    634         self._num_yielded > self._IterableDataset_len_called:

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self)
    672 def _next_data(self):
    673     index = self._next_index()  # may raise StopIteration
--> 674     data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    675     if self._pin_memory:
    676         data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
     49         data = self.dataset.__getitems__(possibly_batched_index)
     50     else:
---> 51         data = [self.dataset[idx] for idx in possibly_batched_index]
     52 else:
     53     data = self.dataset[possibly_batched_index]

File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0)
     49         data = self.dataset.__getitems__(possibly_batched_index)
     50     else:
---> 51         data = [self.dataset[idx] for idx in possibly_batched_index]
     52 else:
     53     data = self.dataset[possibly_batched_index]

File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
   1762 def __getitem__(self, key):  # noqa: F811
   1763     """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764     return self._getitem(
   1765         key,
   1766     )

File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
   1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
   1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
   1750     pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
   1751 )
   1752 return formatted_output

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns)
    538 else:
    539     pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns)
--> 540     formatted_output = formatter(pa_table_to_format, query_type=query_type)
    541     if output_all_columns:
    542         if isinstance(formatted_output, MutableMapping):

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
    279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
    280     if query_type == "row":
--> 281         return self.format_row(pa_table)
    282     elif query_type == "column":
    283         return self.format_column(pa_table)

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table)
     56 def format_row(self, pa_table: pa.Table) -> dict:
---> 57     row = self.numpy_arrow_extractor().extract_row(pa_table)
     58     return self.recursive_tensorize(row)

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table)
    153 def extract_row(self, pa_table: pa.Table) -> dict:
--> 154     return _unnest(self.extract_batch(pa_table))

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table)
    159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160     return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0)
    159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160     return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array)
    194         array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
    195 if len(array) > 0:
--> 196     if any(
    197         (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
    198         or (isinstance(x, float) and np.isnan(x))
    199         for x in array
    200     ):
    201         return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
    202 return np.array(array, copy=False, **self.np_array_kwargs)

File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0)
    194         array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
    195 if len(array) > 0:
    196     if any(
--> 197         (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
    198         or (isinstance(x, float) and np.isnan(x))
    199         for x in array
    200     ):
    201         return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
    202 return np.array(array, copy=False, **self.np_array_kwargs)

File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr)
    319     warnings.warn(
    320         f"In the future `np.{attr}` will be defined as the "
    321         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    323 if attr in __former_attrs__:
--> 324     raise AttributeError(__former_attrs__[attr])
    326 if attr == 'testing':
    327     import numpy.testing as testing

AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. 
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

Please help me to resolve the above error

### Steps to reproduce the bug

Please resolve the issue of deprecated function np.object to object in the numpy

### Expected behavior

np.object should be written as object only

### Environment info

kaggle notebook