Convert dataset to Parquet

#3
by albertvillanova HF staff - opened
README.md CHANGED
@@ -34,16 +34,25 @@ dataset_info:
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  dtype: bool
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  splits:
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  - name: train
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- num_bytes: 2888035757
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  num_examples: 901028
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  - name: validation
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- num_bytes: 1371399694
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  num_examples: 415416
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  - name: test
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- num_bytes: 1220662901
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  num_examples: 415416
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- download_size: 47955874
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- dataset_size: 5480098352
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for "code_x_glue_cc_clone_detection_big_clone_bench"
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  dtype: bool
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  splits:
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  - name: train
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+ num_bytes: 2888035029
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  num_examples: 901028
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  - name: validation
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+ num_bytes: 1371399358
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  num_examples: 415416
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  - name: test
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+ num_bytes: 1220662565
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  num_examples: 415416
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+ download_size: 1279275281
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+ dataset_size: 5480096952
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: validation
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+ path: data/validation-*
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+ - split: test
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+ path: data/test-*
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  ---
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  # Dataset Card for "code_x_glue_cc_clone_detection_big_clone_bench"
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code_x_glue_cc_clone_detection_big_clone_bench.py DELETED
@@ -1,95 +0,0 @@
1
- from typing import List
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-
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- import datasets
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-
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- from .common import TrainValidTestChild
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- from .generated_definitions import DEFINITIONS
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-
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-
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- _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.
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- The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree."""
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-
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- _CITATION = """@inproceedings{svajlenko2014towards,
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- title={Towards a big data curated benchmark of inter-project code clones},
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- author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
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- booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
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- pages={476--480},
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- year={2014},
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- organization={IEEE}
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- }
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-
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- @inproceedings{wang2020detecting,
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- title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
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- author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
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- booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
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- pages={261--271},
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- year={2020},
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- organization={IEEE}
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- }"""
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-
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-
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- class CodeXGlueCcCloneDetectionBigCloneBenchImpl(TrainValidTestChild):
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- _DESCRIPTION = _DESCRIPTION
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- _CITATION = _CITATION
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-
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- _FEATURES = {
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- "id": datasets.Value("int32"), # Index of the sample
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- "id1": datasets.Value("int32"), # The first function id
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- "id2": datasets.Value("int32"), # The second function id
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- "func1": datasets.Value("string"), # The full text of the first function
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- "func2": datasets.Value("string"), # The full text of the second function
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- "label": datasets.Value("bool"), # 1 is the functions are not equivalent, 0 otherwise
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- }
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-
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- _SUPERVISED_KEYS = ["label"]
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-
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- def generate_urls(self, split_name):
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- yield "index", f"{split_name}.txt"
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- yield "data", "data.jsonl"
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-
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- def _generate_examples(self, split_name, file_paths):
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- import json
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-
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- js_all = {}
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-
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- with open(file_paths["data"], encoding="utf-8") as f:
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- for idx, line in enumerate(f):
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- entry = json.loads(line)
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- js_all[int(entry["idx"])] = entry["func"]
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-
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- with open(file_paths["index"], encoding="utf-8") as f:
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- for idx, line in enumerate(f):
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- line = line.strip()
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- idx1, idx2, label = [int(i) for i in line.split("\t")]
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- func1 = js_all[idx1]
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- func2 = js_all[idx2]
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-
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- yield idx, dict(id=idx, id1=idx1, id2=idx2, func1=func1, func2=func2, label=(label == 1))
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-
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-
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- CLASS_MAPPING = {
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- "CodeXGlueCcCloneDetectionBigCloneBench": CodeXGlueCcCloneDetectionBigCloneBenchImpl,
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- }
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-
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-
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- class CodeXGlueCcCloneDetectionBigCloneBench(datasets.GeneratorBasedBuilder):
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- BUILDER_CONFIG_CLASS = datasets.BuilderConfig
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- BUILDER_CONFIGS = [
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- datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
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- ]
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-
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- def _info(self):
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- name = self.config.name
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- info = DEFINITIONS[name]
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- if info["class_name"] in CLASS_MAPPING:
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- self.child = CLASS_MAPPING[info["class_name"]](info)
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- else:
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- raise RuntimeError(f"Unknown python class for dataset configuration {name}")
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- ret = self.child._info()
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- return ret
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-
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- def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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- return self.child._split_generators(dl_manager=dl_manager)
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-
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- def _generate_examples(self, split_name, file_paths):
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- return self.child._generate_examples(split_name, file_paths)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
common.py DELETED
@@ -1,75 +0,0 @@
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- from typing import List
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-
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- import datasets
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-
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-
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- # Citation, taken from https://github.com/microsoft/CodeXGLUE
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- _DEFAULT_CITATION = """@article{CodeXGLUE,
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- title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
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- year={2020},}"""
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-
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-
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- class Child:
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- _DESCRIPTION = None
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- _FEATURES = None
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- _CITATION = None
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- SPLITS = {"train": datasets.Split.TRAIN}
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- _SUPERVISED_KEYS = None
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-
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- def __init__(self, info):
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- self.info = info
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-
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- def homepage(self):
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- return self.info["project_url"]
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-
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- def _info(self):
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- # This is the description that will appear on the datasets page.
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- return datasets.DatasetInfo(
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- description=self.info["description"] + "\n\n" + self._DESCRIPTION,
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- features=datasets.Features(self._FEATURES),
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- homepage=self.homepage(),
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- citation=self._CITATION or _DEFAULT_CITATION,
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- supervised_keys=self._SUPERVISED_KEYS,
33
- )
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-
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- def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
36
- SPLITS = self.SPLITS
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- _URL = self.info["raw_url"]
38
- urls_to_download = {}
39
- for split in SPLITS:
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- if split not in urls_to_download:
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- urls_to_download[split] = {}
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-
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- for key, url in self.generate_urls(split):
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- if not url.startswith("http"):
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- url = _URL + "/" + url
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- urls_to_download[split][key] = url
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-
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- downloaded_files = {}
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- for k, v in urls_to_download.items():
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- downloaded_files[k] = dl_manager.download_and_extract(v)
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-
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- return [
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- datasets.SplitGenerator(
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- name=SPLITS[k],
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- gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
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- )
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- for k in SPLITS
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- ]
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-
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- def check_empty(self, entries):
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- all_empty = all([v == "" for v in entries.values()])
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- all_non_empty = all([v != "" for v in entries.values()])
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-
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- if not all_non_empty and not all_empty:
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- raise RuntimeError("Parallel data files should have the same number of lines.")
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-
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- return all_empty
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-
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-
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- class TrainValidTestChild(Child):
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- SPLITS = {
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- "train": datasets.Split.TRAIN,
73
- "valid": datasets.Split.VALIDATION,
74
- "test": datasets.Split.TEST,
75
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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generated_definitions.py DELETED
@@ -1,12 +0,0 @@
1
- DEFINITIONS = {
2
- "default": {
3
- "class_name": "CodeXGlueCcCloneDetectionBigCloneBench",
4
- "dataset_type": "Code-Code",
5
- "description": "CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench",
6
- "dir_name": "Clone-detection-BigCloneBench",
7
- "name": "default",
8
- "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench",
9
- "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-BigCloneBench/dataset",
10
- "sizes": {"test": 415416, "train": 901028, "validation": 415416},
11
- }
12
- }