Hi,
I have been trying to load a dataset for a chemical named entity recognition.  Here is the script
import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{krallinger2015chemdner,
  title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
  author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others},
  journal={Journal of cheminformatics},
  volume={7},
  number={1},
  pages={1--17},
  year={2015},
  publisher={BioMed Central}
}
"""

_DESCRIPTION = """\
 The CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines
"""

_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr"
_URL = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD/"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "devel.tsv"
_TEST_FILE = "test.tsv"


class BC4CHEMDConfig(datasets.BuilderConfig):
    """BuilderConfig for  BC4CHEMD"""

    def __init__(self, **kwargs):
        """BuilderConfig for  BC4CHEMD.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(BC4CHEMDConfig, self).__init__(**kwargs)


class  BC4CHEMD(datasets.GeneratorBasedBuilder):
    """ BC4CHEMD dataset."""

    BUILDER_CONFIGS = [
        BC4CHEMDConfig(name="BC4CHEMD", version=datasets.Version("1.0.0"), description=" BC4CHEMD 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-Chemical",
                                "I-Chemical",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]
    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 == "" 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")
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }

When I try to load this dataset , I got this error as shown in the screenshot
`from datasets import load_dataset
dataset = load_dataset(“ghadeermobasher/3ok”, use_auth_token=True)`

Load_dataset_error1784×634 115 KB

Any help?