Delete loading script

code_x_glue_cc_defect_detection.py

@@ -1,78 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
-The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test."""
-_CITATION = """@inproceedings{zhou2019devign,
-title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
-author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
-booktitle={Advances in Neural Information Processing Systems},
-pages={10197--10207}, year={2019}"""
-
-
-class CodeXGlueCcDefectDetectionImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "func": datasets.Value("string"),  # The source code
-        "target": datasets.Value("bool"),  # 0 or 1 (vulnerability or not)
-        "project": datasets.Value("string"),  # Original project that contains this code
-        "commit_id": datasets.Value("string"),  # Commit identifier in the original project
-    }
-    _SUPERVISED_KEYS = ["target"]
-
-    def generate_urls(self, split_name):
-        yield "index", f"{split_name}.txt"
-        yield "data", "function.json"
-
-    def _generate_examples(self, split_name, file_paths):
-        import json
-
-        js_all = json.load(open(file_paths["data"], encoding="utf-8"))
-
-        index = set()
-        with open(file_paths["index"], encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                index.add(int(line))
-
-        for idx, js in enumerate(js_all):
-            if idx in index:
-                js["id"] = idx
-                js["target"] = int(js["target"]) == 1
-                yield idx, js
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcDefectDetection": CodeXGlueCcDefectDetectionImpl,
-}
-
-
-class CodeXGlueCcDefectDetection(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = datasets.BuilderConfig
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
-    ]
-
-    def _info(self):
-        name = self.config.name
-        info = DEFINITIONS[name]
-        if info["class_name"] in CLASS_MAPPING:
-            self.child = CLASS_MAPPING[info["class_name"]](info)
-        else:
-            raise RuntimeError(f"Unknown python class for dataset configuration {name}")
-        ret = self.child._info()
-        return ret
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        return self.child._split_generators(dl_manager=dl_manager)
-
-    def _generate_examples(self, split_name, file_paths):
-        return self.child._generate_examples(split_name, file_paths)