Upload indolem_ner_ugm.py with huggingface_hub

indolem_ner_ugm.py

@@ -2,11 +2,11 @@ from pathlib import Path
 from typing import Dict, List, Tuple
 
 import datasets
-from nusacrowd.utils import schemas
-from nusacrowd.utils.common_parser import load_conll_data
+from seacrowd.utils import schemas
+from seacrowd.utils.common_parser import load_conll_data
 
-from nusacrowd.utils.configs import NusantaraConfig
-from nusacrowd.utils.constants import Tasks
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
 
 _CITATION = """\
 @inproceedings{koto-etal-2020-indolem,
@@ -56,7 +56,7 @@ _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
 
 _SOURCE_VERSION = "1.0.0"
 
-_NUSANTARA_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
 
 class IndolemNERUGM(datasets.GeneratorBasedBuilder):
     """NER UGM comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity; and based on 5-fold cross validation"""
@@ -64,11 +64,11 @@ class IndolemNERUGM(datasets.GeneratorBasedBuilder):
     label_classes = ["B-PERSON", "B-LOCATION", "B-ORGANIZATION", "B-TIME", "B-QUANTITY", "I-PERSON", "I-LOCATION", "I-ORGANIZATION", "I-TIME", "I-QUANTITY", "O"]
 
     SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
 
     BUILDER_CONFIGS = ( 
     [
-        NusantaraConfig(
+        SEACrowdConfig(
             name="indolem_ner_ugm_fold{fold_number}_source".format(fold_number=i),
             version=_SOURCE_VERSION,
             description="indolem_ner_ugm source schema",
@@ -77,11 +77,11 @@ class IndolemNERUGM(datasets.GeneratorBasedBuilder):
         ) for i in range(5)
     ] 
     +   [
-            NusantaraConfig(
-                name="indolem_ner_ugm_fold{fold_number}_nusantara_seq_label".format(fold_number=i),
-                version=_NUSANTARA_VERSION,
+            SEACrowdConfig(
+                name="indolem_ner_ugm_fold{fold_number}_seacrowd_seq_label".format(fold_number=i),
+                version=_SEACROWD_VERSION,
                 description="indolem_ner_ugm Nusantara schema",
-                schema="nusantara_seq_label",
+                schema="seacrowd_seq_label",
                 subset_id="indolem_ner_ugm_fold{fold_number}".format(fold_number=i),
             ) for i in range(5)
         ]
@@ -101,7 +101,7 @@ class IndolemNERUGM(datasets.GeneratorBasedBuilder):
                 }
             )
 
-        elif self.config.schema == "nusantara_seq_label":
+        elif self.config.schema == "seacrowd_seq_label":
             features = schemas.seq_label_features(self.label_classes)
 
         return datasets.DatasetInfo(
@@ -167,7 +167,7 @@ class IndolemNERUGM(datasets.GeneratorBasedBuilder):
                     "tags": row["label"]
                 }
                 yield i, ex
-        elif self.config.schema == "nusantara_seq_label":
+        elif self.config.schema == "seacrowd_seq_label":
             for i, row in enumerate(conll_dataset):
                 ex = {
                     "id": str(i),