Upload indolem_tweet_ordering.py with huggingface_hub

indolem_tweet_ordering.py

@@ -28,7 +28,7 @@ To create a dataset loading script you will create a class and implement 3 metho
 
 TODO: Before submitting your script, delete this doc string and replace it with a description of your dataset.
 
-[nusantara_schema_name] = (kb, pairs, qa, text, t2t, entailment)
+[seacrowd_schema_name] = (kb, pairs, qa, text, t2t, entailment)
 """
 from base64 import encode
 import json
@@ -37,10 +37,10 @@ from typing import Dict, List, Tuple
 
 import datasets
 
-from nusacrowd.utils import schemas
-from nusacrowd.utils.common_parser import load_conll_data
-from nusacrowd.utils.configs import NusantaraConfig
-from nusacrowd.utils.constants import Tasks
+from seacrowd.utils import schemas
+from seacrowd.utils.common_parser import load_conll_data
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
 
 # TODO: Add BibTeX citation
 _CITATION = """\
@@ -93,7 +93,7 @@ _LICENSE = "Creative Commons Attribution 4.0"
 #  For local datasets, this variable can be an empty dictionary.
 
 # For publicly available datasets you will most likely end up passing these URLs to dl_manager in _split_generators.
-# In most cases the URLs will be the same for the source and nusantara config.
+# In most cases the URLs will be the same for the source and seacrowd config.
 # However, if you need to access different files for each config you can have multiple entries in this dict.
 # This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
@@ -112,7 +112,7 @@ _SUPPORTED_TASKS = [Tasks.SENTENCE_ORDERING]  # example: [Tasks.TRANSLATION, Tas
 #  provided by the original dataset as a version goes.
 _SOURCE_VERSION = "1.0.0"
 
-_NUSANTARA_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
 
 
 # TODO: Name the dataset class to match the script name using CamelCase instead of snake_case
@@ -120,40 +120,40 @@ class IndolemTweetOrderingDataset(datasets.GeneratorBasedBuilder):
     """This task is based on the sentence ordering task of Barzilay and Lapata (2008) to assess text relatedness. We construct the data by shuffling Twitter threads (containing 3 to 5 tweets), and assessing the predicted ordering in terms of rank correlation (p) with the original."""
     label_classes = [0,1,2,3,4]
     SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
 
-    # You will be able to load the "source" or "nusanrata" configurations with
+    # You will be able to load the "source" or "seacrowd" configurations with
     # ds_source = datasets.load_dataset('my_dataset', name='source')
-    # ds_nusantara = datasets.load_dataset('my_dataset', name='nusantara')
+    # ds_seacrowd = datasets.load_dataset('my_dataset', name='seacrowd')
 
     # For local datasets you can make use of the `data_dir` and `data_files` kwargs
     # https://huggingface.co/docs/datasets/add_dataset.html#downloading-data-files-and-organizing-splits
     # ds_source = datasets.load_dataset('my_dataset', name='source', data_dir="/path/to/data/files")
-    # ds_nusantara = datasets.load_dataset('my_dataset', name='nusantara', data_dir="/path/to/data/files")
+    # ds_seacrowd = datasets.load_dataset('my_dataset', name='seacrowd', data_dir="/path/to/data/files")
 
     # TODO: For each dataset, implement Config for Source and Nusantara;
-    #  If dataset contains more than one subset (see nusantara/nusa_datasets/smsa.py) implement for EACH of them.
+    #  If dataset contains more than one subset (see seacrowd/sea_datasets/smsa.py) implement for EACH of them.
     #  Each of them should contain:
-    #   - name: should be unique for each dataset config eg. smsa_(source|nusantara)_[nusantara_schema_name]
-    #   - version: option = (SOURCE_VERSION|NUSANTARA_VERSION)
+    #   - name: should be unique for each dataset config eg. smsa_(source|seacrowd)_[seacrowd_schema_name]
+    #   - version: option = (SOURCE_VERSION|SEACROWD_VERSION)
     #   - description: one line description for the dataset
-    #   - schema: options = (source|nusantara_[nusantara_schema_name])
+    #   - schema: options = (source|seacrowd_[seacrowd_schema_name])
     #   - subset_id: subset id is the canonical name for the dataset (eg. smsa)
-    #  where [nusantara_schema_name] = (kb, pairs, qa, text, t2t)
+    #  where [seacrowd_schema_name] = (kb, pairs, qa, text, t2t)
 
     BUILDER_CONFIGS = [
-        NusantaraConfig(
+        SEACrowdConfig(
             name="indolem_tweet_ordering_source",
             version=SOURCE_VERSION,
             description="indolem_tweet_ordering source schema",
             schema="source",
             subset_id="indolem_tweet_ordering",
         ),
-        NusantaraConfig(
-            name="indolem_tweet_ordering_nusantara_seq_label",
-            version=NUSANTARA_VERSION,
+        SEACrowdConfig(
+            name="indolem_tweet_ordering_seacrowd_seq_label",
+            version=SEACROWD_VERSION,
             description="indolem_tweet_ordering Nusantara schema",
-            schema="nusantara_seq_label",
+            schema="seacrowd_seq_label",
             subset_id="indolem_tweet_ordering",
         ),
     ]
@@ -168,7 +168,7 @@ class IndolemTweetOrderingDataset(datasets.GeneratorBasedBuilder):
 
         if self.config.schema == "source":
             features = datasets.Features({"tweets":[datasets.Value("string")], "order": [datasets.Value("int32")]})
-        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(
@@ -182,7 +182,7 @@ class IndolemTweetOrderingDataset(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
         """Returns SplitGenerators."""
         # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If you need to access the "source" or "nusantara" config choice, that will be in self.config.name
+        # If you need to access the "source" or "seacrowd" config choice, that will be in self.config.name
         # LOCAL DATASETS: You do not need the dl_manager; you can ignore this argument. Make sure `gen_kwargs` in the return gets passed the right filepath
         # PUBLIC DATASETS: Assign your data-dir based on the dl_manager.
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs; many examples use the download_and_extract method; see the DownloadManager docs here: https://huggingface.co/docs/datasets/package_reference/builder_classes.html#datasets.DownloadManager
@@ -236,7 +236,7 @@ class IndolemTweetOrderingDataset(datasets.GeneratorBasedBuilder):
             ex = {}
             if self.config.schema == 'source':
                 ex = {'tweets': data[i]['tweets'], 'order': data[i]['order']}
-            elif self.config.schema == 'nusantara_seq_label':
+            elif self.config.schema == 'seacrowd_seq_label':
                 ex = {"id": str(i), "tokens": data[i]['tweets'], "labels": data[i]['order']}
             else:
                 raise ValueError(f"Invalid config: {self.config.name}")