Segmentation fault when loading local JSON dataset as of #372 
 The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. 

```
dataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data')
```
causes
```
Using custom data configuration default
Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/XXX/.cache/huggingface/datasets/json/default/0.0.0...
0 tables [00:00, ? tables/s]Segmentation fault (core dumped)
```
where `./datasets/train-v2.0.json` is downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/.
This is consistent with other SQuAD-formatted JSON files.

When attempting to load the dataset again, I get the following:
```
Using custom data configuration default
Traceback (most recent call last):
  File "dataloader.py", line 6, in <module>
    'json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data')
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset
    save_infos=save_infos,
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 382, in download_and_prepare
    with incomplete_dir(self._cache_dir) as tmp_data_dir:
  File "/home/XXX/.conda/envs/torch/lib/python3.7/contextlib.py", line 112, in __enter__
    return next(self.gen)
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 368, in incomplete_dir
    os.makedirs(tmp_dir)
  File "/home/XXX/.conda/envs/torch/lib/python3.7/os.py", line 223, in makedirs
    mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/XXX/.cache/huggingface/datasets/json/default/0.0.0.incomplete'
```

(Not sure if you wanted this in the previous issue #369 or not as it was closed.) 
 I've seen this sort of thing before -- it might help to delete the directory -- I've also noticed that there is an error with the json Dataloader for any data I've tried to load. I've replaced it with this, which skips over the data feature population step:


```python
import os

import pyarrow.json as paj

import nlp as hf_nlp

from nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils
from nlp.arrow_writer import ArrowWriter


class JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):
    BUILDER_CONFIG_CLASS = BuilderConfig

    def _info(self):
        return DatasetInfo()

    def _split_generators(self, dl_manager):
        """ We handle string, list and dicts in datafiles
        """
        if isinstance(self.config.data_files, (str, list, tuple)):
            files = self.config.data_files
            if isinstance(files, str):
                files = [files]
            return [SplitGenerator(name=Split.TRAIN, gen_kwargs={"files": files})]
        splits = []
        for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:
            if split_name in self.config.data_files:
                files = self.config.data_files[split_name]
                if isinstance(files, str):
                    files = [files]
                splits.append(SplitGenerator(name=split_name, gen_kwargs={"files": files}))
        return splits

    def _prepare_split(self, split_generator):
        fname = "{}-{}.arrow".format(self.name, split_generator.name)
        fpath = os.path.join(self._cache_dir, fname)

        writer = ArrowWriter(path=fpath)

        generator = self._generate_tables(**split_generator.gen_kwargs)
        for key, table in utils.tqdm(generator, unit=" tables", leave=False):
            writer.write_table(table)
        num_examples, num_bytes = writer.finalize()

        split_generator.split_info.num_examples = num_examples
        split_generator.split_info.num_bytes = num_bytes

    def _generate_tables(self, files):
        for i, file in enumerate(files):
            pa_table = paj.read_json(
                file
            )
            yield i, pa_table

```