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emot.py

@@ -4,13 +4,13 @@ from typing import List
 import datasets
 import pandas as pd
 
-from nusacrowd.utils import schemas
-from nusacrowd.utils.configs import NusantaraConfig
-from nusacrowd.utils.constants import DEFAULT_NUSANTARA_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks
+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 = "emot"
 _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
-_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
 
 _LANGUAGES = ["ind"]  # We follow ISO639-3 langauge code (https://iso639-3.sil.org/code_tables/639/data)
 _LOCAL = False
@@ -51,25 +51,25 @@ _URLs = {
 _SUPPORTED_TASKS = [Tasks.EMOTION_CLASSIFICATION]
 
 _SOURCE_VERSION = "1.0.0"
-_NUSANTARA_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
 
 
 class EmoT(datasets.GeneratorBasedBuilder):
     """SMSA is a sentiment analysis dataset consisting of 3 labels (positive, neutral, and negative) which comes from comments and reviews collected from multiple online platforms."""
 
     BUILDER_CONFIGS = [
-        NusantaraConfig(
+        SEACrowdConfig(
             name="emot_source",
             version=datasets.Version(_SOURCE_VERSION),
             description="EmoT source schema",
             schema="source",
             subset_id="emot",
         ),
-        NusantaraConfig(
-            name="emot_nusantara_text",
-            version=datasets.Version(_NUSANTARA_VERSION),
+        SEACrowdConfig(
+            name="emot_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
             description="EmoT Nusantara schema",
-            schema="nusantara_text",
+            schema="seacrowd_text",
             subset_id="emot",
         ),
     ]
@@ -85,7 +85,7 @@ class EmoT(datasets.GeneratorBasedBuilder):
                     "label": datasets.Value("string"),
                 }
             )
-        elif self.config.schema == "nusantara_text":
+        elif self.config.schema == "seacrowd_text":
             features = schemas.text_features(["happy", "love", "fear", "anger", "sadness"])
 
         return datasets.DatasetInfo(
@@ -129,7 +129,7 @@ class EmoT(datasets.GeneratorBasedBuilder):
             for row in df.itertuples():
                 ex = {"index": str(row.id), "tweet": row.tweet, "label": row.label}
                 yield row.id, ex
-        elif self.config.schema == "nusantara_text":
+        elif self.config.schema == "seacrowd_text":
             for row in df.itertuples():
                 ex = {"id": str(row.id), "text": row.tweet, "label": row.label}
                 yield row.id, ex