Convert dataset to Parquet (#3)

- Convert dataset to Parquet (07ecd480fd82c5ed63432a5acfe5a1ed0965d0e2)
- Delete loading script (55824d81f085d0f456dd6ebc9702d37af02425ef)
- Delete loading script auxiliary file (35e27b7c70ba03996789a7a74edbb6489e195c1c)
- Delete loading script auxiliary file (5070ee2a98326313e2e257747c8d2326388d721a)

README.md

@@ -34,16 +34,25 @@ dataset_info:
     dtype: bool
   splits:
   - name: train
-    num_bytes: 2888035757
+    num_bytes: 2888035029
     num_examples: 901028
   - name: validation
-    num_bytes: 1371399694
+    num_bytes: 1371399358
     num_examples: 415416
   - name: test
-    num_bytes: 1220662901
+    num_bytes: 1220662565
     num_examples: 415416
-  download_size: 47955874
-  dataset_size: 5480098352
+  download_size: 1279275281
+  dataset_size: 5480096952
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 # Dataset Card for "code_x_glue_cc_clone_detection_big_clone_bench"
 

code_x_glue_cc_clone_detection_big_clone_bench.py

@@ -1,95 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
-The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree."""
-
-_CITATION = """@inproceedings{svajlenko2014towards,
-title={Towards a big data curated benchmark of inter-project code clones},
-author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
-booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
-pages={476--480},
-year={2014},
-organization={IEEE}
-}
-
-@inproceedings{wang2020detecting,
-title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
-author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
-booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
-pages={261--271},
-year={2020},
-organization={IEEE}
-}"""
-
-
-class CodeXGlueCcCloneDetectionBigCloneBenchImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "id1": datasets.Value("int32"),  # The first function id
-        "id2": datasets.Value("int32"),  # The second function id
-        "func1": datasets.Value("string"),  # The full text of the first function
-        "func2": datasets.Value("string"),  # The full text of the second function
-        "label": datasets.Value("bool"),  # 1 is the functions are not equivalent, 0 otherwise
-    }
-
-    _SUPERVISED_KEYS = ["label"]
-
-    def generate_urls(self, split_name):
-        yield "index", f"{split_name}.txt"
-        yield "data", "data.jsonl"
-
-    def _generate_examples(self, split_name, file_paths):
-        import json
-
-        js_all = {}
-
-        with open(file_paths["data"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                entry = json.loads(line)
-                js_all[int(entry["idx"])] = entry["func"]
-
-        with open(file_paths["index"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                line = line.strip()
-                idx1, idx2, label = [int(i) for i in line.split("\t")]
-                func1 = js_all[idx1]
-                func2 = js_all[idx2]
-
-                yield idx, dict(id=idx, id1=idx1, id2=idx2, func1=func1, func2=func2, label=(label == 1))
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCloneDetectionBigCloneBench": CodeXGlueCcCloneDetectionBigCloneBenchImpl,
-}
-
-
-class CodeXGlueCcCloneDetectionBigCloneBench(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)

common.py

@@ -1,75 +0,0 @@
-from typing import List
-
-import datasets
-
-
-# Citation, taken from https://github.com/microsoft/CodeXGLUE
-_DEFAULT_CITATION = """@article{CodeXGLUE,
-         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
-         year={2020},}"""
-
-
-class Child:
-    _DESCRIPTION = None
-    _FEATURES = None
-    _CITATION = None
-    SPLITS = {"train": datasets.Split.TRAIN}
-    _SUPERVISED_KEYS = None
-
-    def __init__(self, info):
-        self.info = info
-
-    def homepage(self):
-        return self.info["project_url"]
-
-    def _info(self):
-        # This is the description that will appear on the datasets page.
-        return datasets.DatasetInfo(
-            description=self.info["description"] + "\n\n" + self._DESCRIPTION,
-            features=datasets.Features(self._FEATURES),
-            homepage=self.homepage(),
-            citation=self._CITATION or _DEFAULT_CITATION,
-            supervised_keys=self._SUPERVISED_KEYS,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        SPLITS = self.SPLITS
-        _URL = self.info["raw_url"]
-        urls_to_download = {}
-        for split in SPLITS:
-            if split not in urls_to_download:
-                urls_to_download[split] = {}
-
-            for key, url in self.generate_urls(split):
-                if not url.startswith("http"):
-                    url = _URL + "/" + url
-                urls_to_download[split][key] = url
-
-        downloaded_files = {}
-        for k, v in urls_to_download.items():
-            downloaded_files[k] = dl_manager.download_and_extract(v)
-
-        return [
-            datasets.SplitGenerator(
-                name=SPLITS[k],
-                gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
-            )
-            for k in SPLITS
-        ]
-
-    def check_empty(self, entries):
-        all_empty = all([v == "" for v in entries.values()])
-        all_non_empty = all([v != "" for v in entries.values()])
-
-        if not all_non_empty and not all_empty:
-            raise RuntimeError("Parallel data files should have the same number of lines.")
-
-        return all_empty
-
-
-class TrainValidTestChild(Child):
-    SPLITS = {
-        "train": datasets.Split.TRAIN,
-        "valid": datasets.Split.VALIDATION,
-        "test": datasets.Split.TEST,
-    }

data/test-00000-of-00003.parquet

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generated_definitions.py

@@ -1,12 +0,0 @@
-DEFINITIONS = {
-    "default": {
-        "class_name": "CodeXGlueCcCloneDetectionBigCloneBench",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench",
-        "dir_name": "Clone-detection-BigCloneBench",
-        "name": "default",
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-BigCloneBench/dataset",
-        "sizes": {"test": 415416, "train": 901028, "validation": 415416},
-    }
-}