Upload su_emot.py with huggingface_hub

su_emot.py

@@ -0,0 +1,126 @@
+import os
+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 DEFAULT_SEACROWD_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks
+
+_DATASETNAME = "su_emot"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+
+_LANGUAGES = ["sun"]
+_LOCAL = False
+_CITATION = """\
+@INPROCEEDINGS{
+9297929,  
+author={Putra, Oddy Virgantara and Wasmanson, Fathin Muhammad and Harmini, Triana and Utama, Shoffin Nahwa},  
+booktitle={2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)},   
+title={Sundanese Twitter Dataset for Emotion Classification},   
+year={2020},  
+volume={},  
+number={},  
+pages={391--395},  
+doi={10.1109/CENIM51130.2020.9297929}
+}
+"""
+
+_DESCRIPTION = """\
+This is a dataset for emotion classification of Sundanese text. The dataset is gathered from Twitter API between January and March 2019 with 2518 tweets in total. 
+The tweets filtered by using some hashtags which are represented Sundanese emotion, for instance, #persib, #corona, #saredih, #nyakakak, #garoblog, #sangsara, #gumujeng, #bungah, #sararieun, #ceurik, and #hariwang. 
+This dataset contains four distinctive emotions: anger, joy, fear, and sadness. Each tweet is annotated using related emotion. For data
+validation, the authors consulted a Sundanese language teacher for expert validation.
+"""
+_HOMEPAGE = "https://github.com/virgantara/sundanese-twitter-dataset"
+
+_LICENSE = "UNKNOWN"
+
+_URLS = {
+	"datasets": "https://raw.githubusercontent.com/virgantara/sundanese-twitter-dataset/master/newdataset.csv"
+}
+
+_SUPPORTED_TASKS = [Tasks.EMOTION_CLASSIFICATION]
+
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class SuEmot(datasets.GeneratorBasedBuilder):
+    """This is a dataset for emotion classification of Sundanese text. The dataset is gathered from Twitter API between January and March 2019 with 2518 tweets in total."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="su_emot_source",
+            version=SOURCE_VERSION,
+            description="Sundanese Twitter Dataset for Emotion source schema",
+            schema="source",
+            subset_id="su_emot",
+        ),
+        SEACrowdConfig(
+            name="su_emot_seacrowd_text",
+            version=SEACROWD_VERSION,
+            description="Sundanese Twitter Dataset for Emotion Nusantara schema",
+            schema="seacrowd_text",
+            subset_id="su_emot",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "su_emot_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features({
+                "index": datasets.Value("string"),
+                "data": datasets.Value("string"), 
+                "label": datasets.Value("string")})
+
+        # For example seacrowd_kb, seacrowd_t2t
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(["anger", "joy", "fear", "sadness"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS
+        data_dir = Path(dl_manager.download_and_extract(urls['datasets']))
+        data_files = {"train":data_dir}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_files['train'],
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+
+        df = pd.read_csv(filepath, sep=",", header="infer").reset_index()
+        df.columns = ["index","label", "data"]
+
+        if self.config.schema == "source":
+            for row in df.itertuples():
+                ex = {"index": str(row.index+1), "data": row.data, "label": row.label}
+                yield row.index, ex
+        elif self.config.schema == "seacrowd_text":
+            for row in df.itertuples():
+                ex = {"id": str(row.index+1), "text": row.data, "label": row.label}
+                yield row.index, ex