Upload sampiran.py with huggingface_hub

sampiran.py

@@ -15,12 +15,12 @@
 
 from pathlib import Path
 from typing import Dict, List, Tuple
-from nusacrowd.utils.constants import Tasks
-from nusacrowd.utils import schemas
+from seacrowd.utils.constants import Tasks
+from seacrowd.utils import schemas
 
 import datasets
 
-from nusacrowd.utils.configs import NusantaraConfig
+from seacrowd.utils.configs import SEACrowdConfig
 
 # TODO: Add BibTeX citation
 _CITATION = """\
@@ -52,7 +52,7 @@ _SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
 
 _SOURCE_VERSION = "1.0.0"
 
-_NUSANTARA_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
 
 
 class SampiranDataset(datasets.GeneratorBasedBuilder):
@@ -60,21 +60,21 @@ class SampiranDataset(datasets.GeneratorBasedBuilder):
     collected from various sources (online)."""
 
     SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
 
     BUILDER_CONFIGS = [
-        NusantaraConfig(
+        SEACrowdConfig(
             name="sampiran_source",
             version=SOURCE_VERSION,
             description="sampiran source schema",
             schema="source",
             subset_id="sampiran",
         ),
-        NusantaraConfig(
-            name="sampiran_nusantara_ssp",
-            version=NUSANTARA_VERSION,
+        SEACrowdConfig(
+            name="sampiran_seacrowd_ssp",
+            version=SEACROWD_VERSION,
             description="sampiran Nusantara schema",
-            schema="nusantara_ssp",
+            schema="seacrowd_ssp",
             subset_id="sampiran",
         ),
     ]
@@ -90,9 +90,9 @@ class SampiranDataset(datasets.GeneratorBasedBuilder):
                     "pantun": datasets.Value("string"),
                 }
             )
-        elif self.config.schema == "nusantara_ssp":
+        elif self.config.schema == "seacrowd_ssp":
             # e.g. features = schemas.kb_features
-            # TODO: Choose your nusantara schema here
+            # TODO: Choose your seacrowd schema here
             features = schemas.self_supervised_pretraining.features
 
         return datasets.DatasetInfo(
@@ -121,7 +121,7 @@ class SampiranDataset(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, filepath: Path, split: str = None) -> Tuple[int, Dict]:
         """Yields examples as (key, example) tuples."""
-        if self.config.schema != "source" and self.config.schema != "nusantara_ssp":
+        if self.config.schema != "source" and self.config.schema != "seacrowd_ssp":
             raise ValueError(f"Invalid config schema: {self.config.schema}")
 
         # Read the file line by line
@@ -135,7 +135,7 @@ class SampiranDataset(datasets.GeneratorBasedBuilder):
                     }
                     yield id_, ex
 
-        elif self.config.name == "sampiran_nusantara_ssp":
+        elif self.config.name == "sampiran_seacrowd_ssp":
             with open(filepath, encoding="utf-8") as f:
                 for id_, row in enumerate(f):
                     ex = {"id": str(id_), "text": str(row).rstrip()}