Issue: JNLPBA Dataset

Comment:
Since I cannot create a branch here is the updated code:

```python

# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Introduction to the Bio-Entity Recognition Task at JNLPBA"""

import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{kim2004introduction,
               title={Introduction to the bio-entity recognition task at JNLPBA},
               author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
               booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},
               pages={70--75},
               year={2004},
               organization={Citeseer}
}
"""

_DESCRIPTION = """\
The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search
on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts
were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.
Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
"""

_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"
_TRAIN_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz"
_VAL_URL = 'http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Evaluation/Genia4ERtest.tar.gz'


_URLS = {
    "train": _TRAIN_URL,
    "val": _VAL_URL,
}

_TRAIN_DIRECTORY = "Genia4ERtraining"
_VAL_DIRECTORY = "Genia4ERtest"

_TRAIN_FILE = "Genia4ERtask1.iob2"
_VAL_FILE = "Genia4EReval1.iob2"


class JNLPBAConfig(datasets.BuilderConfig):
    """BuilderConfig for JNLPBA"""

    def __init__(self, **kwargs):
        """BuilderConfig for JNLPBA.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(JNLPBAConfig, self).__init__(**kwargs)


class JNLPBA(datasets.GeneratorBasedBuilder):
    """JNLPBA dataset."""

    BUILDER_CONFIGS = [
        JNLPBAConfig(name="jnlpba", version=datasets.Version("1.0.0"), description="JNLPBA dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                'O',
                                'B-DNA',
                                'I-DNA',                             
                                'B-RNA',
                                'I-RNA',
                                'B-cell_line',
                                'I-cell_line',
                                'B-cell_type',
                                'I-cell_type',
                                'B-protein',
                                'I-protein',
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)
                    
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, 
                                    gen_kwargs={"filepath": os.path.join(downloaded_files['train'], _TRAIN_FILE)}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, 
                                    gen_kwargs={"filepath": os.path.join(downloaded_files['val'], _VAL_FILE)})
        ]
    

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line.startswith('###'):
                    continue
                if line == '' or line == '\n':
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # tokens are tab separated
                    splits = line.split("\t")
                    #print(splits)
                    #print(len(splits))
                    if len(splits) < 2:
                        splits = splits[0].split()
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }
```