UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors
Well because I don't know the internal of numpy as well as you I guess hahahah, do you want to try to open a PR proposing a solution?
I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:

> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.
> C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\datasets\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\torch\csrc\utils\tensor_numpy.cpp:141.)
>   return torch.tensor(x, **format_kwargs)

The first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.

Snippet:
```
    dataset = Dataset.from_dict(torch.load("data/dummy.pt.pt"))
    print(dataset)
    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
    keys_to_retain = {"input_ids", "sembedding"}
    dataset = dataset.map(lambda example: tokenizer(example["text"], padding='max_length'), batched=True)
    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)

    dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)

    print(next(iter(dataloader)))
```

PS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.
UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors 
 I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:

> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.
> C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\datasets\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\torch\csrc\utils\tensor_numpy.cpp:141.)
>   return torch.tensor(x, **format_kwargs)

The first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.

Snippet:
```
    dataset = Dataset.from_dict(torch.load("data/dummy.pt.pt"))
    print(dataset)
    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
    keys_to_retain = {"input_ids", "sembedding"}
    dataset = dataset.map(lambda example: tokenizer(example["text"], padding='max_length'), batched=True)
    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)

    dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)

    print(next(iter(dataloader)))
```

PS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. 
 Well because I don't know the internal of numpy as well as you I guess hahahah, do you want to try to open a PR proposing a solution?