Delete cogtext_old.py

cogtext_old.py

@@ -1,112 +0,0 @@
-
-"""CogText Dataset"""
-
-import datasets
-import pandas as pd
-
-
-_CITATION = """\
-@misc{cogtext2022,
-author = {Morteza Ansarinia and
-Paul Schrater and
-Pedro Cardoso-Leite},
-title = {Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control},
-year = {2022},
-url = {https://arxiv.org/abs/2203.11016}
-}
-"""
-
-_DESCRIPTION = """\
-CogText dataset contains a collection of PubMed abstracts, along with their GPT-3 embeddings and topic embeddings.
-"""
-_HOMEPAGE = "https://github.com/morteza/cogtext"
-_LICENSE = "CC-BY-4.0"
-
-_URLS = [
-    "pubmed/abstracts2023.csv.gz"
-]
-
-
-class CogText(datasets.GeneratorBasedBuilder):
-    """S collection of PubMed abstracts, along with their GPT-3 embeddings and topic embeddings."""
-
-    VERSION = datasets.Version("1.0.2023")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    # BUILDER_CONFIGS = [
-    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset"),
-    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset"),
-    # ]
-
-    DEFAULT_CONFIG_NAME = "abstracts"  # It's not mandatory to have a default configuration.
-
-    def _info(self):
-        # This method specifies the datasets.DatasetInfo object which contains information and typings for the dataset
-
-        features = datasets.Features(
-            {
-                "pmid": datasets.Value("int32"),
-                "doi": datasets.Value("string"),
-                "year": datasets.Value("int32"),
-                "journal_title": datasets.Value("string"),
-                "journal_iso_abbreviation": datasets.Value("string"),
-                "title": datasets.Value("string"),
-                "abstract": datasets.Value("string"),
-                "category": datasets.Value("string"),
-                "subcategory": datasets.Value("string"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=("abstract", "subcategory"),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        [abstracts_path] = dl_manager.download(_URLS)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split("abstracts"),
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": abstracts_path,
-                    "split": "abstracts",
-                },
-            )
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-
-        example_df = pd.read_csv(filepath, compression="gzip")
-
-        for key, row in example_df.iterrows():
-            yield key, {
-                "pmid": row['pmid'],
-                "year": row['year'],
-                "journal_title": row['journal_title'],
-                "journal_iso_abbreviation": row['journal_iso_abbreviation'],
-                'category': row['category'],
-                'subcategory': row['subcategory'],
-                'doi': row['doi'],
-                'title': row['title'],
-                'abstract': row['abstract'],
-            }