Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
code
Size:
10K - 100K
License:
Update files from the datasets library (from 1.16.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.16.0
- README.md +1 -0
- code_x_glue_cc_clone_detection_poj104.py +30 -24
README.md
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---
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annotations_creators:
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- found
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language_creators:
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---
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pretty_name: CodeXGlueCcCloneDetectionPoj104
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annotations_creators:
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- found
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language_creators:
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code_x_glue_cc_clone_detection_poj104.py
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import os
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import os.path
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from typing import List
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import datasets
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@@ -34,27 +32,16 @@ class CodeXGlueCcCloneDetectionPoj104Impl(TrainValidTestChild):
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SPLIT_RANGES = {"train": (1, 65), "valid": (65, 81), "test": (81, 195)}
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def
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yield "data", "programs.tar.gz"
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def _generate_examples(self, split_name, file_paths):
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def files(path):
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g = os.walk(path)
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file = []
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for path, dir_list, file_list in g:
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for file_name in file_list:
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file.append(os.path.join(path, file_name))
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return file
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root_path = file_paths["data"]
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cont = 0
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for
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js = {}
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js["label"] =
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js["id"] = cont
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js["code"] =
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yield cont, js
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cont += 1
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@@ -81,7 +68,26 @@ class CodeXGlueCcCloneDetectionPoj104(datasets.GeneratorBasedBuilder):
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return ret
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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return
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from typing import List
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import datasets
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SPLIT_RANGES = {"train": (1, 65), "valid": (65, 81), "test": (81, 195)}
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def _generate_examples(self, files, split_name):
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cont = 0
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for path, f in files:
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# path are in the format ProgramData/{index}/{filename}
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label = int(path.split("/")[1])
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if self.SPLIT_RANGES[split_name][0] <= label <= self.SPLIT_RANGES[split_name][1]:
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js = {}
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js["label"] = str(label)
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js["id"] = cont
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js["code"] = f.read().decode("latin-1")
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yield cont, js
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cont += 1
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return ret
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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name = self.config.name
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info = DEFINITIONS[name]
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archive = dl_manager.download(info["raw_url"] + "/programs.tar.gz")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "train"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "valid"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "test"},
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),
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]
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def _generate_examples(self, files, split_name):
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return self.child._generate_examples(files, split_name)
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