JNLPBA Dataset
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[]
["# Steps to reproduce\r\n\r\nTo reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('jnlpba')\r\n\r\ndataset['train'].features['ner_tags']\r\n```\r\nOutput:\r\n```python\r\nSequence(feature=ClassLabel(num_classes=3, names=['O', 'B', 'I'], names_file=None, id=None), length=-1, id=None)\r\n```\r\n\r\n"
 'Since I cannot create a branch here is the updated code:\r\n\r\n```python\r\n\r\n# coding=utf-8\r\n# Copyright 2020 HuggingFace Datasets Authors.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the "License");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n#     http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an "AS IS" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n# Lint as: python3\r\n"""Introduction to the Bio-Entity Recognition Task at JNLPBA"""\r\n\r\nimport os\r\n\r\nimport datasets\r\n\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\n_CITATION = """\\\r\n@inproceedings{kim2004introduction,\r\n               title={Introduction to the bio-entity recognition task at JNLPBA},\r\n               author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},\r\n               booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},\r\n               pages={70--75},\r\n               year={2004},\r\n               organization={Citeseer}\r\n}\r\n"""\r\n\r\n_DESCRIPTION = """\\\r\nThe data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search\r\non MEDLINE using the MeSH terms \x18human\x19, \x18blood cells\x19 and \x18transcription factors\x19. From this search 2,000 abstracts\r\nwere selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.\r\nAmong the classes, 36 terminal classes were used to annotate the GENIA corpus.\r\n"""\r\n\r\n_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"\r\n_TRAIN_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz"\r\n_VAL_URL = \'http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Evaluation/Genia4ERtest.tar.gz\'\r\n\r\n\r\n_URLS = {\r\n    "train": _TRAIN_URL,\r\n    "val": _VAL_URL,\r\n}\r\n\r\n_TRAIN_DIRECTORY = "Genia4ERtraining"\r\n_VAL_DIRECTORY = "Genia4ERtest"\r\n\r\n_TRAIN_FILE = "Genia4ERtask1.iob2"\r\n_VAL_FILE = "Genia4EReval1.iob2"\r\n\r\n\r\nclass JNLPBAConfig(datasets.BuilderConfig):\r\n    """BuilderConfig for JNLPBA"""\r\n\r\n    def __init__(self, **kwargs):\r\n        """BuilderConfig for JNLPBA.\r\n        Args:\r\n          **kwargs: keyword arguments forwarded to super.\r\n        """\r\n        super(JNLPBAConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass JNLPBA(datasets.GeneratorBasedBuilder):\r\n    """JNLPBA dataset."""\r\n\r\n    BUILDER_CONFIGS = [\r\n        JNLPBAConfig(name="jnlpba", version=datasets.Version("1.0.0"), description="JNLPBA dataset"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=datasets.Features(\r\n                {\r\n                    "id": datasets.Value("string"),\r\n                    "tokens": datasets.Sequence(datasets.Value("string")),\r\n                    "ner_tags": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                \'O\',\r\n                                \'B-DNA\',\r\n                                \'I-DNA\',                             \r\n                                \'B-RNA\',\r\n                                \'I-RNA\',\r\n                                \'B-cell_line\',\r\n                                \'I-cell_line\',\r\n                                \'B-cell_type\',\r\n                                \'I-cell_type\',\r\n                                \'B-protein\',\r\n                                \'I-protein\',\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        downloaded_files = dl_manager.download_and_extract(_URLS)\r\n                    \r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, \r\n                                    gen_kwargs={"filepath": os.path.join(downloaded_files[\'train\'], _TRAIN_FILE)}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, \r\n                                    gen_kwargs={"filepath": os.path.join(downloaded_files[\'val\'], _VAL_FILE)})\r\n        ]\r\n    \r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info("⏳ Generating examples from = %s", filepath)\r\n        with open(filepath, encoding="utf-8") as f:\r\n            guid = 0\r\n            tokens = []\r\n            ner_tags = []\r\n            for line in f:\r\n                if line.startswith(\'###\'):\r\n                    continue\r\n                if line == \'\' or line == \'\\n\':\r\n                    if tokens:\r\n                        yield guid, {\r\n                            "id": str(guid),\r\n                            "tokens": tokens,\r\n                            "ner_tags": ner_tags,\r\n                        }\r\n                        guid += 1\r\n                        tokens = []\r\n                        ner_tags = []\r\n                else:\r\n                    # tokens are tab separated\r\n                    splits = line.split("\\t")\r\n                    #print(splits)\r\n                    #print(len(splits))\r\n                    if len(splits) < 2:\r\n                        splits = splits[0].split()\r\n                    tokens.append(splits[0])\r\n                    ner_tags.append(splits[1].rstrip())\r\n            # last example\r\n            yield guid, {\r\n                "id": str(guid),\r\n                "tokens": tokens,\r\n                "ner_tags": ner_tags,\r\n            }\r\n```']
## Describe the bug
A clear and concise description of what the bug is.

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```

## Expected results
The dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L81-L83) are: O, B, and I.  The correct entities from the original data file are: 

['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']

## Actual results
The dataset loader script needs to include the following NER 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']

And the [data](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:

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