dtype of tensors should be preserved
Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..

And then for your information, when reading from arrow format we have to cast from arrow to numpy (which is fast since pyarrow has a numpy integration), and then to torch.

However there's one thing that can help you: we make sure that the dtypes correspond to what is defined in `features`.
Therefore what you can do is provide `features` in `.map(preprocess, feature=...)` to specify the output types.

For example in your case:
```python
from datasets import Features, Value, Sequence

features = Features({
    "input_ids": Sequence(Value("int32")),
    "sembedding": Sequence(Value("float32"))
})
preprocessed_dataset = dataset.map(preprocess, features=features)

preprocessed_dataset.set_format("torch", columns=["input_ids", "sembedding"])
print(preprocessed_dataset[0]["sembedding"].dtype)
# "torch.float32"
```

Let me know if it helps
After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https://discuss.pytorch.org/t/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32/96221)). 

As a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:

```python
def preprocess(sentences: List[str]):
    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]

    sembeddings = stransformer.encode(sentences)
    print(sembeddings.dtype)
    return {"input_ids": token_ids, "sembedding": sembeddings}
```

Given a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column "sembedding" to a tensor that I as a user expect to be a float32.

It appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. 

My model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.

```python
dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
```

This happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. 

```python
import torch
import numpy as np

l = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]
torch_tensor = torch.tensor(l)
np_array = np.array(l)
np_to_torch = torch.from_numpy(np_array)

print(torch_tensor.dtype)
# torch.float32
print(np_array.dtype)
# float64
print(np_to_torch.dtype)
# torch.float64
```

This might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.

The alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.
dtype of tensors should be preserved 
 After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https://discuss.pytorch.org/t/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32/96221)). 

As a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:

```python
def preprocess(sentences: List[str]):
    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]

    sembeddings = stransformer.encode(sentences)
    print(sembeddings.dtype)
    return {"input_ids": token_ids, "sembedding": sembeddings}
```

Given a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column "sembedding" to a tensor that I as a user expect to be a float32.

It appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. 

My model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.

```python
dataset.set_format(type="torch", columns=["input_ids", "sembedding"])
```

This happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. 

```python
import torch
import numpy as np

l = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]
torch_tensor = torch.tensor(l)
np_array = np.array(l)
np_to_torch = torch.from_numpy(np_array)

print(torch_tensor.dtype)
# torch.float32
print(np_array.dtype)
# float64
print(np_to_torch.dtype)
# torch.float64
```

This might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.

The alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. 
 Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..

And then for your information, when reading from arrow format we have to cast from arrow to numpy (which is fast since pyarrow has a numpy integration), and then to torch.

However there's one thing that can help you: we make sure that the dtypes correspond to what is defined in `features`.
Therefore what you can do is provide `features` in `.map(preprocess, feature=...)` to specify the output types.

For example in your case:
```python
from datasets import Features, Value, Sequence

features = Features({
    "input_ids": Sequence(Value("int32")),
    "sembedding": Sequence(Value("float32"))
})
preprocessed_dataset = dataset.map(preprocess, features=features)

preprocessed_dataset.set_format("torch", columns=["input_ids", "sembedding"])
print(preprocessed_dataset[0]["sembedding"].dtype)
# "torch.float32"
```

Let me know if it helps