Upload dengue_filipino.py with huggingface_hub

dengue_filipino.py

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+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{8459963,
+      author={E. D. {Livelo} and C. {Cheng}},
+      booktitle={2018 IEEE International Conference on Agents (ICA)},
+      title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
+      year={2018},
+      volume={},
+      number={},
+      pages={2-7},
+      doi={10.1109/AGENTS.2018.8459963}}
+    }
+"""
+
+_LANGUAGES = ["fil"]
+
+# copied from https://huggingface.co/datasets/dengue_filipino/blob/main/dengue_filipino.py
+_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/dengue/dengue_raw.zip"
+_DATASETNAME = "dengue_filipino"
+
+_DESCRIPTION = """\
+Benchmark dataset for low-resource multi-label classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.
+"""
+
+_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_SUPPORTED_TASKS = [Tasks.DOMAIN_KNOWLEDGE_MULTICLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_LOCAL = False
+
+
+class DengueFilipinoDataset(datasets.GeneratorBasedBuilder):
+    """Dengue Dataset Low-Resource Multi-label Text Classification Dataset in Filipino"""
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_text_multi",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema text multi",
+            schema="seacrowd_text_multi",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "absent": datasets.features.ClassLabel(names=["0", "1"]),
+                    "dengue": datasets.features.ClassLabel(names=["0", "1"]),
+                    "health": datasets.features.ClassLabel(names=["0", "1"]),
+                    "mosquito": datasets.features.ClassLabel(names=["0", "1"]),
+                    "sick": datasets.features.ClassLabel(names=["0", "1"]),
+                }
+            )
+        elif self.config.schema == "seacrowd_text_multi":
+            features = schemas.text_multi_features(["0", "1"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, split: str) -> Tuple[int, Dict]:
+        dataset = datasets.load_dataset(_DATASETNAME, split=split)
+        for id, data in enumerate(dataset):
+            if self.config.schema == "source":
+                yield id, {
+                    "text": data["text"],
+                    "absent": data["absent"],
+                    "dengue": data["dengue"],
+                    "health": data["health"],
+                    "mosquito": data["mosquito"],
+                    "sick": data["sick"],
+                }
+
+            elif self.config.schema == "seacrowd_text_multi":
+                yield id, {
+                    "id": id,
+                    "text": data["text"],
+                    "labels": [
+                        data["absent"],
+                        data["dengue"],
+                        data["health"],
+                        data["mosquito"],
+                        data["sick"],
+                    ],
+                }