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uit_visd4sa.py

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+# coding=utf-8
+
+import json
+from pathlib import Path
+import re
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{thanh-etal-2021-span,
+    title = "Span Detection for Aspect-Based Sentiment Analysis in Vietnamese",
+    author = "Thanh, Kim Nguyen Thi  and
+      Khai, Sieu Huynh  and
+      Huynh, Phuc Pham  and
+      Luc, Luong Phan  and
+      Nguyen, Duc-Vu  and
+      Van, Kiet Nguyen",
+    booktitle = "Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation",
+    year = "2021",
+    publisher = "Association for Computational Lingustics",
+    url = "https://aclanthology.org/2021.paclic-1.34",
+    pages = "318--328",
+}
+"""
+
+_DATASETNAME = "uit_visd4sa"
+
+_DESCRIPTION = """\
+This dataset is designed for span detection for aspect-based sentiment analysis NLP task.
+A Vietnamese dataset consisting of 35,396 human-annotated spans on 11,122 feedback
+comments for evaluating span detection for aspect-based sentiment analysis for mobile e-commerce
+"""
+
+_HOMEPAGE = "https://github.com/kimkim00/UIT-ViSD4SA"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LANGUAGES = ["vie"]
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/kimkim00/UIT-ViSD4SA/main/data/train.jsonl",
+    "dev": "https://raw.githubusercontent.com/kimkim00/UIT-ViSD4SA/main/data/dev.jsonl",
+    "test": "https://raw.githubusercontent.com/kimkim00/UIT-ViSD4SA/main/data/test.jsonl",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPAN_BASED_ABSA]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_LOCAL = False
+
+
+def construct_label_classes():
+    IOB_tag = ["I", "O", "B"]
+    aspects = ["SCREEN", "CAMERA", "FEATURES", "BATTERY", "PERFORMANCE", "STORAGE", "DESIGN", "PRICE", "GENERAL", "SER&ACC"]
+    ratings = ["POSITIVE", "NEUTRAL", "NEGATIVE"]
+    label_classes = []
+    for iob in IOB_tag:
+        if iob == "O":
+            label_classes.append("O")
+        else:
+            for aspect in aspects:
+                for rating in ratings:
+                    label_classes.append("{iob}-{aspect}#{rating}".format(iob=iob, aspect=aspect, rating=rating))
+    return label_classes
+
+
+def construct_IOB_sequences(text, labels):
+    labels.sort()
+    word_start = [0] + [match.start() + 1 for match in re.finditer(" ", text)]
+    is_not_O = False
+    iob_sequence = []
+    word_count = 0
+    lb_count = 0
+
+    while word_count < len(word_start):
+        if lb_count == len(labels):
+            for x in range(word_count, len(word_start)):
+                iob_sequence.append("O")
+            break
+        if not is_not_O:
+            if word_start[word_count] >= labels[lb_count][0]:
+                is_not_O = True
+                iob_sequence.append("B-" + labels[lb_count][-1])
+                word_count += 1
+            else:
+                iob_sequence.append("O")
+                word_count += 1
+        else:
+            if word_start[word_count] > labels[lb_count][1]:
+                is_not_O = False
+                lb_count += 1
+            else:
+                iob_sequence.append("I-" + labels[lb_count][-1])
+                word_count += 1
+    return iob_sequence
+
+
+class UITViSD4SADataset(datasets.GeneratorBasedBuilder):
+    """This dataset is designed for span detection for aspect-based sentiment analysis NLP task.
+    A Vietnamese dataset consisting of 35,396 human-annotated spans on 11,122 feedback
+    comments for evaluating span detection for aspect-based sentiment analysis for mobile e-commerce"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description="uit_visd4sa source schema",
+            schema="source",
+            subset_id="uit_visd4sa",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_seq_label",
+            version=SEACROWD_VERSION,
+            description="uit_visd4sa SEACrowd schema",
+            schema="seacrowd_seq_label",
+            subset_id="uit_visd4sa",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.Sequence({"start": datasets.Value("int32"), "end": datasets.Value("int32"), "aspect": datasets.Value("string"), "rating": datasets.Value("string")}),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label_features(construct_label_classes())
+
+        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."""
+        path_dict = dl_manager.download_and_extract(_URLS)
+        train_path, dev_path, test_path = path_dict["train"], path_dict["dev"], path_dict["test"]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": dev_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath, "r") as f:
+            df = [json.loads(line) for line in f.readlines()]
+        f.close()
+        if self.config.schema == "source":
+            for _id, row in enumerate(df):
+                labels = row["labels"]
+                entry_labels = []
+                for lb in labels:
+                    entry_labels.append({"start": lb[0], "end": lb[1], "aspect": lb[-1].split("#")[0], "rating": lb[-1].split("#")[-1]})
+                entry = {
+                    "text": row["text"],
+                    "label": entry_labels,
+                }
+                yield _id, entry
+        elif self.config.schema == "seacrowd_seq_label":
+            for _id, row in enumerate(df):
+                entry = {
+                    "id": str(_id),
+                    "tokens": row["text"].split(" "),
+                    "labels": construct_IOB_sequences(row["text"], row["labels"]),
+                }
+                yield _id, entry