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visim400.py

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+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{nguyen-etal-2018-introducing,
+    title = "Introducing Two {V}ietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness",
+    author = "Nguyen, Kim Anh  and
+      Schulte im Walde, Sabine  and
+      Vu, Ngoc Thang",
+    editor = "Walker, Marilyn  and
+      Ji, Heng  and
+      Stent, Amanda",
+    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
+    month = jun,
+    year = "2018",
+    address = "New Orleans, Louisiana",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N18-2032",
+    doi = "10.18653/v1/N18-2032",
+    pages = "199--205"
+    }
+"""
+
+_DATASETNAME = "visim400"
+
+_DESCRIPTION = """\
+ViSim-400 is a Vietnamese dataset of semantic relation \
+pairs for evaluation of models that reflect the \
+continuum between similarity and relatedness.
+
+We choose 'Sim2' instead of 'Sim1' for the label output of \
+our SEACrowd dataloader schema because it's been normalized to [1, 10].
+"""
+
+_HOMEPAGE = "https://www.ims.uni-stuttgart.de/forschung/ressourcen/experiment-daten/vnese-sem-datasets/"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = {_DATASETNAME: "https://www.ims.uni-stuttgart.de/documents/ressourcen/experiment-daten/ViData.zip"}
+
+_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ViSim400Dataset(datasets.GeneratorBasedBuilder):
+    """
+    ViSim-400 is a Vietnamese dataset of semantic relation \
+    pairs for evaluation of models that reflect the \
+    continuum between similarity and relatedness.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "pairs_score"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=_SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=_SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "Word1": datasets.Value("string"),
+                    "Word2": datasets.Value("string"),
+                    "POS": datasets.Value("string"),
+                    "Sim1": datasets.Value("string"),
+                    "Sim2": datasets.Value("string"),
+                    "STD": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.pairs_features_score()
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "ViData/ViSim-400/Visim-400.txt"),
+                    "split": "test",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        with open(filepath, "r", encoding="utf-8") as file:
+            lines = file.readlines()
+
+        data = []
+        for line in lines:
+            columns = line.strip().split("\t")
+            data.append(columns)
+
+        df = pd.DataFrame(data[1:], columns=data[0])
+
+        for index, row in df.iterrows():
+
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+
+                example = {
+                    "id": str(index),
+                    "text_1": str(row["Word1"]),
+                    "text_2": str(row["Word2"]),
+                    # I choose Sim2 instead of Sim1 because it's been normalized to [1, 10]
+                    "label": str(row["Sim2"]),
+                }
+
+            yield index, example