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vlsp2016_ner.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This dataset is collected from electronic newspapers published on the web and provided by VLSP organization.\
+It consists of approximately 15k sentences, each of which contain NE information in the IOB annotation format\
+"""
+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 = """\
+@article{nguyen-et-al-2019-vlsp-ner,
+author = {Nguyen, Huyen and Ngo, Quyen and Vu, Luong and Mai, Vu and Nguyen, Hien},
+year = {2019},
+month = {01},
+pages = {283-294},
+title = {VLSP Shared Task: Named Entity Recognition},
+volume = {34},
+journal = {Journal of Computer Science and Cybernetics},
+doi = {10.15625/1813-9663/34/4/13161}
+}
+"""
+
+_DATASETNAME = "vlsp2016_ner"
+
+_DESCRIPTION = """\
+This dataset is collected from electronic newspapers published on the web and provided by VLSP organization. \
+It consists of approximately 15k sentences, each of which contain NE information in the IOB annotation format
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/datnth1709/VLSP2016-NER-data"
+
+_LANGUAGES = ["vie"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.CC_BY_NC_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://huggingface.co/datasets/datnth1709/VLSP2016-NER-data/resolve/main/data/train-00000-of-00001-b0417886a268b83a.parquet?download=true",
+        "test": "https://huggingface.co/datasets/datnth1709/VLSP2016-NER-data/resolve/main/data/valid-00000-of-00001-846411c236133ba3.parquet?download=true",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class Visp2016NER(datasets.GeneratorBasedBuilder):
+    """This dataset is collected from electronic newspapers published on the web and provided by VLSP organization.
+    It consists of approximately 15k sentences, each of which contain NE information in the IOB annotation format"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="vlsp2016_ner_source",
+            version=SOURCE_VERSION,
+            description="vlsp2016_ner source schema",
+            schema="source",
+            subset_id="vlsp2016_ner",
+        ),
+        SEACrowdConfig(
+            name="vlsp2016_ner_seacrowd_seq_label",
+            version=SEACROWD_VERSION,
+            description="vlsp2016_ner SEACrowd schema",
+            schema="seacrowd_seq_label",
+            subset_id="vlsp2016_ner",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "vlsp2016_ner_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(datasets.Value("int64")),
+                }
+            )
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label.features([x for x in range(9)])
+
+        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."""
+        train_url = _URLS[_DATASETNAME]["train"]
+        train_path = dl_manager.download_and_extract(train_url)
+
+        test_url = _URLS[_DATASETNAME]["test"]
+        test_path = dl_manager.download_and_extract(test_url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_path,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        df = pd.read_parquet(filepath)
+        if self.config.schema == "source":
+            for i in range(len(df)):
+                row = df.iloc[i]
+                yield (
+                    i,
+                    {
+                        "tokens": row["tokens"],
+                        "ner_tags": row["ner_tags"],
+                    },
+                )
+        elif self.config.schema == "seacrowd_seq_label":
+            for i in range(len(df)):
+                row = df.iloc[i]
+                yield (
+                    i,
+                    {
+                        "id": i,
+                        "tokens": row["tokens"],
+                        "labels": row["ner_tags"],
+                    },
+                )