first commit

PLOD-unfiltered.py

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+import os
+
+import datasets
+from typing import List
+import json
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """
+This is the dataset repository for PLOD Dataset accepted to be published at LREC 2022.
+The dataset can help build sequence labelling models for the task Abbreviation Detection.
+"""
+
+class PLODfilteredConfig(datasets.BuilderConfig):
+    """BuilderConfig for Conll2003"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forConll2003.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PLODfilteredConfig, self).__init__(**kwargs)
+
+
+class PLODfilteredConfig(datasets.GeneratorBasedBuilder):
+    """PLOD Filtered dataset."""
+
+    BUILDER_CONFIGS = [
+        PLODfilteredConfig(name="PLODfiltered", version=datasets.Version("0.0.2"), description="PLOD filtered dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "ADJ",
+                                "ADP",
+                                "ADV",
+                                "AUX",
+                                "CONJ",
+                                "CCONJ",
+                                "DET",
+                                "INTJ",
+                                "NOUN",
+                                "NUM",
+                                "PART",
+                                "PRON",
+                                "PROPN",
+                                "PUNCT",
+                                "SCONJ",
+                                "SYM",
+                                "VERB",
+                                "X",
+                                "SPACE"
+                            ]
+                        )
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "B-O",
+                                "B-AC",
+                                "I-AC",
+                                "B-LF",
+                                "I-LF"
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/surrey-nlp/PLOD-AbbreviationDetection",
+            citation=_CITATION,
+        )
+
+    _URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/"
+    _URLS = {
+        "train": _URL + "PLOS-train70-filtered-pos_bio.json",
+        "dev": _URL + "PLOS-val15-filtered-pos_bio.json",
+        "test": _URL + "PLOS-test15-filtered-pos_bio.json"
+    }
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls_to_download = self._URLS
+        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):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath) as f:
+            plod = json.load(f)
+            for object in plod:
+                id_ = int(object['id'])
+                yield id_, {
+                    "id": str(id_),
+                    "tokens": object['tokens'],
+                    "pos_tags": object['pos_tags'],
+                    "ner_tags": object['ner_tags'],
+                }