Closes #116 | Add pho_ner_covid Dataloader

seacrowd/sea_datasets/pho_ner_covid/pho_ner_covid.py

@@ -0,0 +1,203 @@
+# 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.
+
+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 TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{PhoNER_COVID19,
+title     = {{COVID-19 Named Entity Recognition for Vietnamese}},
+author    = {Thinh Hung Truong and Mai Hoang Dao and Dat Quoc Nguyen},
+booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
+year      = {2021}
+}
+"""
+
+_DATASETNAME = "pho_ner_covid"
+
+_DESCRIPTION = """\
+A named entity recognition dataset for Vietnamese with 10 newly-defined entity types in the context of the COVID-19 pandemic.
+Data is extracted from news articles and manually annotated. In total, there are 34 984 entities over 10 027 sentences.
+"""
+
+_HOMEPAGE = "https://github.com/VinAIResearch/PhoNER_COVID19/tree/main"
+
+_LANGUAGES = ["vie"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "word_level": {
+            "dev": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/word/dev_word.json",
+            "train": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/word/train_word.json",
+            "test": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/word/test_word.json",
+        },
+        "syllable_level": {
+            "dev": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/syllable/dev_syllable.json",
+            "train": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/syllable/train_syllable.json",
+            "test": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/syllable/test_syllable.json",
+        },
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+_SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS]
+
+_SUBSETS = ["word_level", "syllable_level"]
+_SPLITS = ["train", "dev", "test"]
+_TAGS = [
+    "O",
+    "B-ORGANIZATION",
+    "I-ORGANIZATION",
+    "B-SYMPTOM_AND_DISEASE",
+    "I-SYMPTOM_AND_DISEASE",
+    "B-LOCATION",
+    "B-DATE",
+    "B-PATIENT_ID",
+    "B-AGE",
+    "B-NAME",
+    "I-DATE",
+    "B-JOB",
+    "I-LOCATION",
+    "B-TRANSPORTATION",
+    "B-GENDER",
+    "I-TRANSPORTATION",
+    "I-JOB",
+    "I-NAME",
+    "I-AGE",
+    "I-PATIENT_ID",
+    "I-GENDER"
+]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class PhoNerCovid(datasets.GeneratorBasedBuilder):
+    """A named entity recognition dataset for Vietnamese with 10 newly-defined entity types in the context of the COVID-19 pandemic."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+
+    for subset_id in _SUBSETS:
+        BUILDER_CONFIGS.append(
+            SEACrowdConfig(
+                name=f"{subset_id}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} source schema",
+                schema="source",
+                subset_id=subset_id,
+            )
+        )
+
+        seacrowd_schema_config: list[SEACrowdConfig] = []
+
+        for seacrowd_schema in _SUPPORTED_SCHEMA_STRINGS:
+
+            seacrowd_schema_config.append(
+                SEACrowdConfig(
+                    name=f"{subset_id}_{seacrowd_schema}",
+                    version=SEACROWD_VERSION,
+                    description=f"{_DATASETNAME} {seacrowd_schema} schema",
+                    schema=f"{seacrowd_schema}",
+                    subset_id=subset_id,
+                )
+            )
+
+        BUILDER_CONFIGS.extend(seacrowd_schema_config)
+
+    DEFAULT_CONFIG_NAME = f"{_SUBSETS[0]}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "words": datasets.Sequence(datasets.Value("string")),
+                    "tags": datasets.Sequence(datasets.ClassLabel(names=_TAGS)),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.NAMED_ENTITY_RECOGNITION]).lower()}":
+            features = schemas.seq_label_features(label_names=_TAGS)
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+        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."""
+
+        split_generators = []
+
+        for split in _SPLITS:
+            path = dl_manager.download_and_extract(_URLS[_DATASETNAME][self.config.subset_id][split])
+
+            split_generators.append(
+                datasets.SplitGenerator(
+                    name=split,
+                    gen_kwargs={
+                        "path": path,
+                    },
+                )
+            )
+
+        return split_generators
+
+    def _generate_examples(self, path: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        idx = 0
+        df = pd.read_json(path, lines=True)
+
+        if self.config.schema == "source":
+            for _, row in df.iterrows():
+                yield idx, row.to_dict()
+                idx += 1
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.NAMED_ENTITY_RECOGNITION]).lower()}":
+            df["id"] = df.index
+            df.rename(columns={"words": "tokens"}, inplace=True)
+            df.rename(columns={"tags": "labels"}, inplace=True)
+
+            for _, row in df.iterrows():
+                yield idx, row.to_dict()
+                idx += 1
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+
+if __name__ == "__main__":
+    datasets.load_dataset(__file__, name="word_level_source")