Delete loading script

tlunified-ner.py

@@ -1,95 +0,0 @@
-from typing import List
-
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = """
-This dataset contains the annotated TLUnified corpora from Cruz and Cheng
-(2021). It is a curated sample of around 7,000 documents for the
-named entity recognition (NER) task.  The majority of the corpus are news
-reports in Tagalog, resembling the domain of the original ConLL 2003.  There
-are three entity types: Person (PER), Organization (ORG), and Location (LOC).
-"""
-_LICENSE = """GNU GPL v3.0"""
-_URL = "https://huggingface.co/ljvmiranda921/tlunified-ner"
-_CLASSES = ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
-_VERSION = "1.0.0"
-
-
-class TLUnifiedNERConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(TLUnifiedNER, self).__init__(**kwargs)
-
-
-class TLUnifiedNER(datasets.GeneratorBasedBuilder):
-    """Contains an annotated version of the TLUnified dataset from Cruz and Cheng (2021)."""
-
-    VERSION = datasets.Version(_VERSION)
-
-    def _info(self) -> "datasets.DatasetInfo":
-        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=_CLASSES)
-                    ),
-                }
-            ),
-            homepage=_URL,
-            supervised_keys=None,
-        )
-
-    def _split_generators(
-        self, dl_manager: "datasets.builder.DownloadManager"
-    ) -> List["datasets.SplitGenerator"]:
-        """Return a list of SplitGenerators that organizes the splits."""
-        # The file extracts into {train,dev,test}.spacy files. The _generate_examples function
-        # below will define how these files are parsed.
-        data_files = {
-            "train": dl_manager.download_and_extract("corpus/iob/train.iob"),
-            "dev": dl_manager.download_and_extract("corpus/iob/dev.iob"),
-            "test": dl_manager.download_and_extract("corpus/iob/test.iob"),
-        }
-
-        return [
-            # fmt: off
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
-            # fmt: on
-        ]
-
-    def _generate_examples(self, filepath: str):
-        """Defines how examples are parsed from the IOB file."""
-        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("-DOCSTART-") or line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    # TLUnified-NER iob are separated by \t
-                    token, ner_tag = line.split("\t")
-                    tokens.append(token)
-                    ner_tags.append(ner_tag.rstrip())
-            # Last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "ner_tags": ner_tags,
-                }