Upload id_wsd.py with huggingface_hub

id_wsd.py

@@ -0,0 +1,198 @@
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+from nusacrowd.utils.constants import Tasks
+from nusacrowd.utils import schemas
+
+import datasets
+import json
+
+from nusacrowd.utils.configs import NusantaraConfig
+
+_CITATION = """\
+@inproceedings{mahendra-etal-2018-cross,
+    title = "Cross-Lingual and Supervised Learning Approach for {I}ndonesian Word Sense Disambiguation Task",
+    author = "Mahendra, Rahmad  and
+      Septiantri, Heninggar  and
+      Wibowo, Haryo Akbarianto  and
+      Manurung, Ruli  and
+      Adriani, Mirna",
+    booktitle = "Proceedings of the 9th Global Wordnet Conference",
+    month = jan,
+    year = "2018",
+    address = "Nanyang Technological University (NTU), Singapore",
+    publisher = "Global Wordnet Association",
+    url = "https://aclanthology.org/2018.gwc-1.28",
+    pages = "245--250",
+    abstract = "Ambiguity is a problem we frequently face in Natural Language Processing. Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word. However, research in WSD for Indonesian is still rare to find. The availability of English-Indonesian parallel corpora and WordNet for both languages can be used as training data for WSD by applying Cross-Lingual WSD method. This training data is used as an input to build a model using supervised machine learning algorithms. Our research also examines the use of Word Embedding features to build the WSD model.",
+}
+"""
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_DATASETNAME = "indonesian_wsd"
+
+_DESCRIPTION = """\
+Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word.
+The training data was collected from news websites and manually annotated. The words in training data were processed using the morphological analysis to obtain lemma.
+The features being used were some words around the target word (including the words before and after the target word), the nearest verb from the
+target word, the transitive verb around the target word, and the document context. 
+"""
+
+_HOMEPAGE = "https://github.com/rmahendra/Indonesian-WSD"
+
+_LICENSE = "Unknown"
+
+_URLS = {
+    _DATASETNAME: "https://github.com/rmahendra/Indonesian-WSD/raw/master/dataset-clwsd-ina.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.WORD_SENSE_DISAMBIGUATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+_LABELS = [
+    {
+        "name": "atas",
+        "file_ext": ""
+    },
+    {
+        "name": "perdana",
+        "file_ext": ".tab"
+    },
+    {
+        "name": "alam",
+        "file_ext": ".tab"
+    },
+    {
+        "name": "dasar",
+        "file_ext": ".tab"
+    },
+    {
+        "name": "anggur",
+        "file_ext": ".tab"
+    },
+    {
+        "name": "kayu",
+        "file_ext": ""
+    }
+]
+
+class IndonesianWSD(datasets.GeneratorBasedBuilder):
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indonesian_wsd_source",
+            version=SOURCE_VERSION,
+            description="Indonesian WSD source schema",
+            schema="source",
+            subset_id="indonesian_wsd",
+        ),
+        NusantaraConfig(
+            name="indonesian_wsd_nusantara_t2t",
+            version=NUSANTARA_VERSION,
+            description="Indonesian WSD Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="indonesian_wsd",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indonesian_wsd_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "nusantara_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        data_dir = os.path.join(data_dir, "dataset")
+
+        datas = []
+
+        for label in _LABELS:
+            file_name = f"{label['name']}_t01"
+            if label["file_ext"] != "":
+                file_name = f"{file_name}{label['file_ext']}"
+            
+            parsed_data = self._parse_file(os.path.join(data_dir, file_name))
+            datas = datas + parsed_data
+
+        path_dumped_file = os.path.join(data_dir, "data.json")
+        
+        with open(path_dumped_file, 'w') as f:
+            f.write(json.dumps(datas))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": path_dumped_file,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        data = json.load(open(filepath, "r"))
+
+        if self.config.schema == "source":
+            key = 0
+            for each_data in data:
+                example = {
+                    "label": each_data["sense_id"],
+                    "text": each_data["text"]
+                }
+                yield key, example
+                key+=1
+
+        elif self.config.schema == "nusantara_t2t":
+            key = 0
+            for each_data in data:
+                example = {
+                    "id": str(key+1),
+                    "text_1": each_data["sense_id"],
+                    "text_1_name": "label",
+                    "text_2": each_data["text"],
+                    "text_2_name": "text"
+                }
+                yield key, example
+                key+=1
+
+    def _parse_file(self, file_path):
+        parsed_lines = open(file_path, "r").readlines()
+        data = []
+        for line in parsed_lines:
+            if len(line.strip()) > 0:
+                _, sense_id, text = line[:-1].split("\t")    
+                data.append({
+                    "sense_id": sense_id,
+                    "text": text
+                })
+        return data
+                
+