Upload te_en_syn_dataset.py

te_en_syn_dataset.py

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+# Loading script for the Ancora NER dataset. 
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """ """
+
+_DESCRIPTION = """AnCora Catalan NER.
+                  This is a dataset for Named Eentity Reacognition (NER) from Ancora corpus adapted for 
+                  Machine Learning and Language Model evaluation purposes.
+                  Since multiwords (including Named Entites) in the original Ancora corpus are aggregated as 
+                  a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") 
+                  we splitted them to align with word-per-line format, and added conventional Begin-Inside-Outside (IOB)
+                   tags to mark and classify Named Entites. 
+                   We did not filter out the different categories of NEs from Ancora (weak and strong). 
+                   We did 6 minor edits by hand.
+                  AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence.
+                  This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
+               """
+
+_HOMEPAGE = """"""
+
+_URL = "https://huggingface.co/datasets/anishka/Te_En_Syn_dataset/resolve/main/"
+_TRAINING_FILE = "te_syn-code_switch-train.conllu"
+_DEV_FILE = " te_syn-code_switch-dev.conllu"
+_TEST_FILE = "te_syn-code_switch-test.conllu"
+
+
+class AncoraCaNerConfig(datasets.BuilderConfig):
+    """ Builder config for the Ancora Ca NER dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for AncoraCaNer.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(AncoraCaNerConfig, self).__init__(**kwargs)
+
+
+class AncoraCaNer(datasets.GeneratorBasedBuilder):
+    """ AncoraCaNer dataset."""
+
+    BUILDER_CONFIGS = [
+        AncoraCaNerConfig(
+            name="AncoraCaNer",
+            version=datasets.Version("2.0.0"),
+            description="AncoraCaNer dataset"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "idx": datasets.Value("string"),
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "upos": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "NOUN",
+                                "PUNCT",
+                                "ADP",
+                                "NUM",
+                                "SYM",
+                                "SCONJ",
+                                "ADJ",
+                                "PART",
+                                "DET",
+                                "CCONJ",
+                                "PROPN",
+                                "PRON",
+                                "X",
+                                "_",
+                                "ADV",
+                                "INTJ",
+                                "VERB",
+                                "AUX",
+                            ]
+                        )
+                    ),
+                    "xpos": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+            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 = []
+            pos_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n" or line.startswith("#"):
+                    if tokens:
+                        yield guid, {
+                            "idx": str(guid),
+                            "text": tokens,
+                            "upos": pos_tags,
+                            "xpos": pos_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        pos_tags = []
+                else:
+                    # AncoraCaNer tokens are space separated
+                    splits = line.split('\t')
+                    tokens.append(splits[1])
+                    pos_tags.append(splits[3].rstrip())
+            # last example
+            yield guid, {
+                "idx": str(guid),
+                "text": tokens,
+                "upos": pos_tags,
+                "xpos": pos_tags,
+            }