Delete inspec_ke_tagged.py

inspec_ke_tagged.py

@@ -1,121 +0,0 @@
-# Lint as: python3
-"""Keyphrase extraction as sequence labeling using contextualized embeddings"""
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@article{sahrawat2020keyphrase,
-  title={Keyphrase extraction as sequence labeling using contextualized embeddings},
-  author={Sahrawat, Dhruva and Mahata, Debanjan and Zhang, Haimin and Kulkarni, Mayank and Sharma, Agniv and Gosangi, Rakesh and Stent, Amanda and Kumar, Yaman and Shah, Rajiv Ratn and Zimmermann, Roger},
-  journal={Advances in Information Retrieval},
-  volume={12036},
-  pages={328},
-  year={2020},
-  publisher={Nature Publishing Group}
-}
-"""
-
-_DESCRIPTION = """\
-This dataset is one of the datasets used in the paper entitled, Keyphrase Extraction from Scholarly Articles as Sequence 
-Labeling using Contextualized Embeddings https://arxiv.org/abs/1910.08840. The dataset consists of the Inspec corpus 
-which has been tagged using the BIO tagging scheme. The dataset should be used for training and evaluating keyphrase 
-extraction models when modeled as a sequence tagging task
-"""
-
-_URL = "https://huggingface.co/datasets/midas/inspec_ke_tagged/blob/main/"
-_TRAINING_FILE = "train.txt"
-_DEV_FILE = "valid.txt"
-_TEST_FILE = "test.txt"
-
-
-class InspecKETaggedConfig(datasets.BuilderConfig):
-    """BuilderConfig for InspecKETagged"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for InspecKETagged.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(InspecKETaggedConfig, self).__init__(**kwargs)
-
-
-class InspecKETagged(datasets.GeneratorBasedBuilder):
-    """InspecKETagged dataset."""
-
-    BUILDER_CONFIGS = [
-        InspecKETaggedConfig(name="inspec_ke_tagged", version=datasets.Version("1.0.0"), description="InspecKETagged "
-                                                                                                     "dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ke_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-KEY",
-                                "I-KEY",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148038/",
-            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 = []
-            ke_tags = []
-            for line in f:
-                if line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ke_tags": ke_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ke_tags = []
-                else:
-                    # the tokens are space separated
-                    splits = line.split()
-                    print(splits)
-                    tokens.append(splits[0])
-                    ke_tags.append(splits[1].rstrip())
-            # last example
-            yield guid, {
-                "id": str(guid),
-                "tokens": tokens,
-                "ke_tags": ke_tags,
-            }