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DiscoEval.py

@@ -18,7 +18,6 @@ import datasets
 import constants
 import pickle
 import logging
-from huggingface_hub import snapshot_download, hf_hub_url, hf_hub_download
 
 _CITATION = """\
 @InProceedings{mchen-discoeval-19,
@@ -35,15 +34,7 @@ This dataset contains all tasks of the DiscoEval benchmark for sentence represen
 
 _HOMEPAGE = "https://github.com/ZeweiChu/DiscoEval"
 
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "DiscoEval": "https://huggingface.co/.zip",
-}
 
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
     """DiscoEval Benchmark"""
     VERSION = datasets.Version("1.1.0")
@@ -93,13 +84,24 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
             version=VERSION,
             description="The SSP dataset.",
         ),
+        datasets.BuilderConfig(
+            name=constants.BSOARXIV,
+            version=VERSION,
+            description="The BSO Task with the arxiv dataset.",
+        ),
+        datasets.BuilderConfig(
+            name=constants.BSOWIKI,
+            version=VERSION,
+            description="The BSO Task with the wiki dataset.",
+        ),
+        datasets.BuilderConfig(
+            name=constants.BSOROCSTORY,
+            version=VERSION,
+            description="The BSO Task with the rocstory dataset.",
+        ),
     ]
 
-    DEFAULT_CONFIG_NAME = constants.SPARXIV  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-
         if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI]:
             features_dict = {
                 constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
@@ -108,6 +110,14 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
             features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SP_LABELS)
             features = datasets.Features(features_dict)
 
+        elif self.config.name in [constants.BSOARXIV, constants.BSOWIKI, constants.BSOROCSTORY]:
+            features_dict = {
+                constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
+                for i in range(constants.BSO_TEXT_COLUMNS + 1)
+            }
+            features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.BSO_LABELS)
+            features = datasets.Features(features_dict)
+
         elif self.config.name in [constants.DCCHAT, constants.DCWIKI]:
             features_dict = {
                 constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
@@ -148,43 +158,14 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
             features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SSPABS_LABELS)
             features = datasets.Features(features_dict)
 
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
+            features=features,
             homepage=_HOMEPAGE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-
-        # urls = _URLS[self.config.name]
-        # data_dir = dl_manager.download_and_extract(urls)
-        data_dir = ''
-        train_name = ''
-        valid_name = ''
-        test_name = ''
         if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI]:
             data_dir = constants.SP_DATA_DIR + "/" + constants.SP_DIRS[self.config.name]
             train_name = constants.SP_TRAIN_NAME
@@ -235,7 +216,6 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": downloaded_files['train'],
                     "split": "train",
@@ -243,7 +223,6 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": downloaded_files['valid'],
                     "split": "dev",
@@ -251,7 +230,6 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": downloaded_files['test'],
                     "split": "test"
@@ -259,15 +237,11 @@ class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
             ),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
         logger = logging.getLogger(__name__)
         logger.info(f"Current working dir: {os.getcwd()}")
         logger.info("generating examples from = %s", filepath)
-        print(f"Current working dir: {os.getcwd()}")
-        print(f"Current working dir: {os.listdir(os.getcwd())}")
-
-        if self.config.name in [constants.RST]:
+        if self.config.name == constants.RST:
             data = pickle.load(open(filepath, "rb"))
             for key, line in enumerate(data):
                 example = {constants.TEXT_COLUMN_NAME[i]: sent for i, sent in enumerate(line[1:])}