nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema
It should be good now. I was able to run

```python
>>> from nlp import concatenate_datasets, load_dataset
>>>
>>> bookcorpus = load_dataset("bookcorpus", split="train")
>>> wiki = load_dataset("wikipedia", "20200501.en", split="train")
>>> wiki.remove_columns_("title")  # only keep the text
>>>
>>> assert bookcorpus.features.type == wiki.features.type
>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])
```
Here's the code I'm trying to run:

```python
dset_wikipedia = nlp.load_dataset("wikipedia", "20200501.en", split="train", cache_dir=args.cache_dir)
dset_wikipedia.drop(columns=["title"])
dset_wikipedia.features.pop("title")
dset_books = nlp.load_dataset("bookcorpus", split="train", cache_dir=args.cache_dir)
dset = nlp.concatenate_datasets([dset_wikipedia, dset_books])
```

This fails because they have different schemas, despite having identical features.

```python
assert dset_wikipedia.features == dset_books.features # True
assert dset_wikipedia._data.schema == dset_books._data.schema # False
```

The Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.

```python
dset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)
```

nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema 
 Here's the code I'm trying to run:

```python
dset_wikipedia = nlp.load_dataset("wikipedia", "20200501.en", split="train", cache_dir=args.cache_dir)
dset_wikipedia.drop(columns=["title"])
dset_wikipedia.features.pop("title")
dset_books = nlp.load_dataset("bookcorpus", split="train", cache_dir=args.cache_dir)
dset = nlp.concatenate_datasets([dset_wikipedia, dset_books])
```

This fails because they have different schemas, despite having identical features.

```python
assert dset_wikipedia.features == dset_books.features # True
assert dset_wikipedia._data.schema == dset_books._data.schema # False
```

The Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.

```python
dset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)
```
 
 It should be good now. I was able to run

```python
>>> from nlp import concatenate_datasets, load_dataset
>>>
>>> bookcorpus = load_dataset("bookcorpus", split="train")
>>> wiki = load_dataset("wikipedia", "20200501.en", split="train")
>>> wiki.remove_columns_("title")  # only keep the text
>>>
>>> assert bookcorpus.features.type == wiki.features.type
>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])
```