Delete loading script

patent-classification.py

@@ -1,109 +0,0 @@
-import json
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-_CITATION = None
-
-
-_DESCRIPTION = """
- Patent Classification Dataset: a classification of Patents (9 classes).
- It contains 9 unbalanced classes, 35k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
- Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang 
- See: https://aclanthology.org/P19-1212.pdf 
- See: https://evasharma.github.io/bigpatent/
-"""
-
-_LABELS = [
-    "Human Necessities",
-    "Performing Operations; Transporting",
-    "Chemistry; Metallurgy",
-    "Textiles; Paper",
-    "Fixed Constructions",
-    "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
-    "Physics",
-    "Electricity",
-    "General tagging of new or cross-sectional technology",
-]
-
-class PatentClassificationConfig(datasets.BuilderConfig):
-    """BuilderConfig for PatentClassification."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for PatentClassification.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(PatentClassificationConfig, self).__init__(**kwargs)
-
-
-class PatentClassificationDataset(datasets.GeneratorBasedBuilder):
-    """PatentClassification Dataset: classification of Patents (9 classes)."""
-    
-    _DOWNLOAD_URL = "https://huggingface.co/datasets/ccdv/patent-classification/resolve/main/"
-    _TRAIN_FILE = "train_data.txt"
-    _VAL_FILE = "val_data.txt"
-    _TEST_FILE = "test_data.txt"
-    _LABELS_DICT = {label: i for i, label in enumerate(_LABELS)}
-
-    BUILDER_CONFIGS = [
-        PatentClassificationConfig(
-            name="patent",
-            version=datasets.Version("1.0.0"),
-            description="Patent Classification Dataset: A classification task of Patents (9 classes)",
-        ),
-
-        PatentClassificationConfig(
-            name="abstract",
-            version=datasets.Version("1.0.0"),
-            description="Patent Classification Dataset: A classification task of Patents with abstracts (9 classes)",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "patent"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=_LABELS),
-                }
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-            task_templates=[TextClassification(
-                text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(self._TRAIN_FILE)
-        val_path = dl_manager.download_and_extract(self._VAL_FILE)
-        test_path = dl_manager.download_and_extract(self._TEST_FILE)
-        
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
-            ),
-        ]
-    
-    def _generate_examples(self, filepath):
-        """Generate PatentClassification examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                label = self._LABELS_DICT[data["label"]]
-
-                if self.config.name == "abstract":
-                    text = data["abstract"]
-                else:
-                    text = data["description"]
-                yield id_, {"text": text, "label": label}