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smsa.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 Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_NUSANTARA_VIEW_NAME
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_SEACROWD_VIEW_NAME
 
 _DATASETNAME = "smsa"
 _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 language code (https://iso639-3.sil.org/code_tables/639/data)
 _LOCAL = False
@@ -53,25 +53,25 @@ _URLs = {
 _SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
 
 _SOURCE_VERSION = "1.0.0"
-_NUSANTARA_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
 
 
 class SMSA(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="smsa_source",
             version=datasets.Version(_SOURCE_VERSION),
             description="SMSA source schema",
             schema="source",
             subset_id="smsa",
         ),
-        NusantaraConfig(
-            name="smsa_nusantara_text",
-            version=datasets.Version(_NUSANTARA_VERSION),
+        SEACrowdConfig(
+            name="smsa_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
             description="SMSA Nusantara schema",
-            schema="nusantara_text",
+            schema="seacrowd_text",
             subset_id="smsa",
         ),
     ]
@@ -81,7 +81,7 @@ class SMSA(datasets.GeneratorBasedBuilder):
     def _info(self):
         if self.config.schema == "source":
             features = datasets.Features({"index": datasets.Value("string"), "sentence": datasets.Value("string"), "label": datasets.Value("string")})
-        elif self.config.schema == "nusantara_text":
+        elif self.config.schema == "seacrowd_text":
             features = schemas.text_features(["negative", "neutral", "positive"])
 
         return datasets.DatasetInfo(
@@ -125,7 +125,7 @@ class SMSA(datasets.GeneratorBasedBuilder):
             for row in df.itertuples():
                 ex = {"index": str(row.id), "sentence": row.sentence, "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),