Upload id_vaccines_tweets.py with huggingface_hub

id_vaccines_tweets.py

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+import datasets
+import pandas
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
+                                      DEFAULT_SOURCE_VIEW_NAME, Tasks, Licenses)
+
+_DATASETNAME = "id_vaccines_tweets"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+_CITATION = """\
+@article{febriyanti2021analisis,
+  title={ANALISIS SENTIMEN MASYARAKAT INDONESIA TERHADAP PELAKSANAAN VAKSIN COVID'19},
+  author={Febriyanti, Syintya and Nursidah, Dea Ratu and Gustiara, Dela and Yulianti, Rika},
+  journal={Khazanah: Jurnal Mahasiswa},
+  volume={13},
+  number={2},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+Dataset containing tweets about COVID-19 vaccines with manually labelled information about whether they are a
+subjective tweet and their sentiment polarity. Tweets are from 20-27 June 2021 and 15-22 July 2021.
+"""
+
+_HOMEPAGE = "https://github.com/rayendito/id-vaccines-tweets"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_URL = "https://raw.githubusercontent.com/rayendito/id-vaccines-tweets/main/id_vaccines_tweets.csv"
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IdVaccinesTweetsDataset(datasets.GeneratorBasedBuilder):
+    """This is a seacrowd dataloader for id_vaccines_tweets, for every example in the dataset, it contains a subjective
+    tweet and their sentiment polarity.  Tweets are from 20-27 June 2021 and 15-22 July 2021."""
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=_DESCRIPTION,
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=_DESCRIPTION,
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "idx": datasets.Value("string"),
+                    "form_text": datasets.Value("string"),
+                    "norm_text": datasets.Value("string"),
+                    "subjective": datasets.Value("float"),
+                    "sentiment": datasets.Value("float"),
+                }
+            )
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features([-1.0, 0.0, 1.0])
+        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):
+        """ "return splitGenerators"""
+        downloaded_files = dl_manager.download_and_extract(_URL)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files})]
+
+    def _generate_examples(self, filepath):
+        data_lines = pandas.read_csv(filepath, skip_blank_lines=True)
+        keys = data_lines.keys()
+        indexes = data_lines[keys[0]][1:]
+        norms = data_lines[keys[1]][1:]
+        formals = data_lines[keys[2]][1:]
+        subjs = data_lines[keys[3]][1:]
+        posnegs = data_lines[keys[4]][1:]
+        if self.config.schema == "source":
+            for idx, (ind, norm, form, subj, posneg) in enumerate(zip(indexes, norms, formals, subjs, posnegs)):
+                yield idx, {
+                    "idx": str(ind),
+                    "form_text": form,
+                    "norm_text": norm,
+                    "subjective": float(subj),
+                    "sentiment": float(posneg),
+                }
+        if self.config.schema == "seacrowd_text":
+            for idx, (ind, norm, posneg) in enumerate(zip(indexes, norms, posnegs)):
+                yield idx, {"id": str(ind), "text": norm, "label": float(posneg)}