Delete parstwiner.py

parstwiner.py

@@ -1,107 +0,0 @@
-import csv
-from ast import literal_eval
-
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """
-@inproceedings{aghajani-etal-2021-parstwiner,
-    title = "{P}ars{T}wi{NER}: A Corpus for Named Entity Recognition at Informal {P}ersian",
-    author = "Aghajani, MohammadMahdi  and
-      Badri, AliAkbar  and
-      Beigy, Hamid",
-    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
-    month = nov,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.wnut-1.16",
-    pages = "131--136",
-    abstract = "As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort. ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen{'}s Kappa coefficient, the consistency of annotators is 0.95, a high score. In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture. Experimental results show that the model works well in informal Persian as well as in formal Persian.",
-}
-"""
-
-_DESCRIPTION = """"""
-
-_DOWNLOAD_URLS = {
-    "train": "https://huggingface.co/datasets/hezarai/parstwiner/resolve/main/parstwiner_train.csv",
-    "test": "https://huggingface.co/datasets/hezarai/parstwiner/resolve/main/parstwiner_test.csv",
-}
-
-
-class ParsTwiNERConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(ParsTwiNERConfig, self).__init__(**kwargs)
-
-
-class ParsTwiNER(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        ParsTwiNERConfig(
-            name="ParsTwiNER",
-            version=datasets.Version("1.0.0"),
-            description=_DESCRIPTION,
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-POG",
-                                "I-POG",
-                                "B-PER",
-                                "I-PER",
-                                "B-ORG",
-                                "I-ORG",
-                                "B-NAT",
-                                "I-NAT",
-                                "B-LOC",
-                                "I-LOC",
-                                "B-EVE",
-                                "I-EVE",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            homepage="https://github.com/overfit-ir/parstwiner",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """
-        Return SplitGenerators.
-        """
-
-        train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
-        test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
-
-            next(csv_reader, None)
-
-            for id_, row in enumerate(csv_reader):
-                tokens, ner_tags = row
-                # Optional preprocessing here
-                tokens = literal_eval(tokens)
-                ner_tags = literal_eval(ner_tags)
-                yield id_, {"tokens": tokens, "ner_tags": ner_tags}