Datasets does not load HuggingFace Repository properly

### Describe the bug

Dear Datasets team,

We just have published a dataset on Huggingface:
https://huggingface.co/ai4privacy

However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier:

```python
from datasets import load_dataset
dataset = load_dataset("ai4privacy/pii-masking-200k")
```

```
Downloading readme: 100%
11.8k/11.8k [00:00<00:00, 512kB/s]
Downloading data files: 100%
1/1 [00:11<00:00, 11.16s/it]
Downloading data: 100%
64.3M/64.3M [00:02<00:00, 32.9MB/s]
Downloading data: 100%
113M/113M [00:03<00:00, 35.0MB/s]
Downloading data: 100%
97.7M/97.7M [00:02<00:00, 46.1MB/s]
Downloading data: 100%
90.8M/90.8M [00:02<00:00, 44.9MB/s]
Downloading data: 100%
7.63k/7.63k [00:00<00:00, 41.0kB/s]
Downloading data: 100%
1.03k/1.03k [00:00<00:00, 9.44kB/s]
Extracting data files: 100%
1/1 [00:00<00:00, 29.26it/s]
Generating train split:
209261/0 [00:05<00:00, 41201.25 examples/s]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1939 )
-> 1940 writer.write_table(table)
1941 num_examples_progress_update += len(table)

8 frames
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size)
571 pa_table = pa_table.combine_chunks()
--> 572 pa_table = table_cast(pa_table, self._schema)
573 if self.embed_local_files:

[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema)
2327 if table.schema != schema:
-> 2328 return cast_table_to_schema(table, schema)
2329 elif table.schema.metadata != schema.metadata:

[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema)
2285 if sorted(table.column_names) != sorted(features):
-> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]

ValueError: Couldn't cast
JOBTYPE: int64
PHONEIMEI: int64
ACCOUNTNAME: int64
VEHICLEVIN: int64
GENDER: int64
CURRENCYCODE: int64
CREDITCARDISSUER: int64
JOBTITLE: int64
SEX: int64
CURRENCYSYMBOL: int64
IP: int64
EYECOLOR: int64
MASKEDNUMBER: int64
SECONDARYADDRESS: int64
JOBAREA: int64
ACCOUNTNUMBER: int64
language: string
BITCOINADDRESS: int64
MAC: int64
SSN: int64
EMAIL: int64
ETHEREUMADDRESS: int64
DOB: int64
VEHICLEVRM: int64
IPV6: int64
AMOUNT: int64
URL: int64
PHONENUMBER: int64
PIN: int64
TIME: int64
CREDITCARDNUMBER: int64
FIRSTNAME: int64
IBAN: int64
BIC: int64
COUNTY: int64
STATE: int64
LASTNAME: int64
ZIPCODE: int64
HEIGHT: int64
ORDINALDIRECTION: int64
MIDDLENAME: int64
STREET: int64
USERNAME: int64
CURRENCY: int64
PREFIX: int64
USERAGENT: int64
CURRENCYNAME: int64
LITECOINADDRESS: int64
CREDITCARDCVV: int64
AGE: int64
CITY: int64
PASSWORD: int64
BUILDINGNUMBER: int64
IPV4: int64
NEARBYGPSCOORDINATE: int64
DATE: int64
COMPANYNAME: int64
to
{'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
because column names don't match

The above exception was the direct cause of the following exception:

DatasetGenerationError Traceback (most recent call last)
[](https://localhost:8080/#) in ()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k")

[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2151
2152 # Download and prepare data
-> 2153 builder_instance.download_and_prepare(
2154 download_config=download_config,
2155 download_mode=download_mode,

[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
952 if num_proc is not None:
953 prepare_split_kwargs["num_proc"] = num_proc
--> 954 self._download_and_prepare(
955 dl_manager=dl_manager,
956 verification_mode=verification_mode,

[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
1047 try:
1048 # Prepare split will record examples associated to the split
-> 1049 self._prepare_split(split_generator, **prepare_split_kwargs)
1050 except OSError as e:
1051 raise OSError(

[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1811 job_id = 0
1812 with pbar:
-> 1813 for job_id, done, content in self._prepare_split_single(
1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1815 ):

[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1957 e = e.__context__
-> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1959
1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)

DatasetGenerationError: An error occurred while generating the dataset
```

Thank you and have a great day ahead

### Steps to reproduce the bug

Open Google Colab Notebook:

Run command:
!pip3 install datasets

Run code:
from datasets import load_dataset
dataset = load_dataset("ai4privacy/pii-masking-200k")

### Expected behavior

Download the dataset successfully from HuggingFace to the notebook so that we can start working with it

### Environment info

- `datasets` version: 2.14.6
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.19.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3