Custom feature types in `load_dataset` from CSV 
 I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. 

I am working with the local files from the emotion dataset. To get the data you can use the following code:

```Python
from pathlib import Path
import wget

EMOTION_PATH = Path("./data/emotion")
DOWNLOAD_URLS = [
    "https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1",
    "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1",
    "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1",
]

if not Path.is_dir(EMOTION_PATH):
     Path.mkdir(EMOTION_PATH)
for url in DOWNLOAD_URLS:
     wget.download(url, str(EMOTION_PATH))
```

The first five lines of the train set are:
```
i didnt feel humiliated;sadness
i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness
im grabbing a minute to post i feel greedy wrong;anger
i am ever feeling nostalgic about the fireplace i will know that it is still on the property;love
i am feeling grouchy;anger
```

Here the code to reproduce the issue:
```Python
from datasets import Features, Value, ClassLabel, load_dataset

class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
emotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})
file_dict = {'train': EMOTION_PATH/'train.txt'}

dataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)
```

**Observed behaviour:**
```Python
dataset['train'].features
```
```Python
{'text': Value(dtype='string', id=None),
 'label': Value(dtype='string', id=None)}
```
**Expected behaviour:**
```Python
dataset['train'].features
```
```Python
{'text': Value(dtype='string', id=None),
 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}
```

**Things I've tried:**
- deleting the cache
- trying other types such as `int64`

Am I missing anything? Thanks for any pointer in the right direction. 
 Hi @lhoestq we've tried out your suggestion but are now running into the following error:

```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-163-81ffd5ac18c9> in <module>
----> 1 dataset.cast_(emotion_features)

/usr/local/lib/python3.6/dist-packages/datasets/dataset_dict.py in cast_(self, features)
    125         self._check_values_type()
    126         for dataset in self.values():
--> 127             dataset.cast_(features=features)
    128 
    129     def remove_columns_(self, column_names: Union[str, List[str]]):

/usr/local/lib/python3.6/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
    161             # Call actual function
    162 
--> 163             out = func(self, *args, **kwargs)
    164 
    165             # Update fingerprint of in-place transforms + update in-place history of transforms

/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in cast_(self, features)
    602         self._info.features = features
    603         schema = pa.schema(features.type)
--> 604         self._data = self._data.cast(schema)
    605 
    606     @fingerprint(inplace=True)

/usr/local/lib/python3.6/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()

ValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']
```

Looking at the types in `emotion_features` we see that `label` and `text` appear to be swapped in the Arrow table:

```
emotion_features.type
StructType(struct<label: int64, text: string>)
```

Did we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https://huggingface.co/docs/datasets/features.html?highlight=features#dataset-features), but perhaps we misunderstood something 😬