Upload indocoref.py with huggingface_hub

indocoref.py

@@ -9,24 +9,24 @@ except:
 
 import datasets
 
-from nusacrowd.nusa_datasets.indocoref.utils.text_preprocess import \
+from seacrowd.sea_datasets.indocoref.utils.text_preprocess import \
     TextPreprocess
-from nusacrowd.utils import schemas
-from nusacrowd.utils.configs import NusantaraConfig
-from nusacrowd.utils.constants import Tasks
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
 
 _CITATION = """\
 @inproceedings{artari-etal-2021-multi,
-  title        = {A Multi-Pass Sieve Coreference Resolution for {I}ndonesian},
-  author       = {Artari, Valentina Kania Prameswara  and Mahendra, Rahmad  and Jiwanggi, Meganingrum Arista  and Anggraito, Adityo  and Budi, Indra},
-  year         = 2021,
-  month        = sep,
-  booktitle    = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)},
-  publisher    = {INCOMA Ltd.},
-  address      = {Held Online},
-  pages        = {79--85},
-  url          = {https://aclanthology.org/2021.ranlp-1.10},
-  abstract     = {Coreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent mentions. The multi-pass sieve is based on the principle of high precision, followed by increased recall in each sieve. In this work, we examine the portability of the multi-pass sieve coreference resolution model to the Indonesian language. We conduct the experiment on 201 Wikipedia documents and the multi-pass sieve system yields 72.74{\%} of MUC F-measure and 52.18{\%} of BCUBED F-measure.}
+    title        = {{A Multi-Pass Sieve Coreference Resolution for Indonesian}},
+    author       = {Artari, Valentina Kania Prameswara  and Mahendra, Rahmad  and Jiwanggi, Meganingrum Arista  and Anggraito, Adityo  and Budi, Indra},
+    year         = 2021,
+    month        = {Sep},
+    booktitle    = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)},
+    publisher    = {INCOMA Ltd.},
+    address      = {Held Online},
+    pages        = {79--85},
+    url          = {https://aclanthology.org/2021.ranlp-1.10},
+    abstract     = {Coreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent mentions. The multi-pass sieve is based on the principle of high precision, followed by increased recall in each sieve. In this work, we examine the portability of the multi-pass sieve coreference resolution model to the Indonesian language. We conduct the experiment on 201 Wikipedia documents and the multi-pass sieve system yields 72.74{\%} of MUC F-measure and 52.18{\%} of BCUBED F-measure.}
 }
 """
 
@@ -62,28 +62,28 @@ _URLS = {
 _SUPPORTED_TASKS = [Tasks.COREFERENCE_RESOLUTION]
 # Does not seem to have versioning
 _SOURCE_VERSION = "1.0.0"
-_NUSANTARA_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
 
 
 class Indocoref(datasets.GeneratorBasedBuilder):
     """A collection of 210 curated articles from Wikipedia Bahasa Indonesia with Coreference Annotations"""
 
     SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
 
     BUILDER_CONFIGS = [
-        NusantaraConfig(
+        SEACrowdConfig(
             name="indocoref_source",
             version=SOURCE_VERSION,
             description="Indocoref source schema",
             schema="source",
             subset_id="indocoref",
         ),
-        NusantaraConfig(
-            name="indocoref_nusantara_kb",
-            version=NUSANTARA_VERSION,
+        SEACrowdConfig(
+            name="indocoref_seacrowd_kb",
+            version=SEACROWD_VERSION,
             description="Indocoref Nusantara schema",
-            schema="nusantara_kb",
+            schema="seacrowd_kb",
             subset_id="indocoref",
         ),
     ]
@@ -121,7 +121,7 @@ class Indocoref(datasets.GeneratorBasedBuilder):
                     ],
                 }
             )
-        elif self.config.schema == "nusantara_kb":
+        elif self.config.schema == "seacrowd_kb":
             features = schemas.kb_features
 
         return datasets.DatasetInfo(
@@ -209,7 +209,7 @@ class Indocoref(datasets.GeneratorBasedBuilder):
                 }
                 yield index, row
 
-        elif self.config.schema == "nusantara_kb":
+        elif self.config.schema == "seacrowd_kb":
             for index, example in enumerate(data):
                 passage, mentions = example["passage"], example["mentions"]
                 # Annotated text does not have any line breaks but the original passage does