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README.md

@@ -1,186 +0,0 @@
----
-pretty_name: CodeXGlueCcCloneDetectionPoj104
-annotations_creators:
-- found
-language_creators:
-- found
-language:
-- code
-license:
-- c-uda
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-retrieval
-task_ids:
-- document-retrieval
-dataset_info:
-  features:
-  - name: id
-    dtype: int32
-  - name: code
-    dtype: string
-  - name: label
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 18878686
-    num_examples: 32000
-  - name: validation
-    num_bytes: 5765303
-    num_examples: 8000
-  - name: test
-    num_bytes: 6852864
-    num_examples: 12000
-  download_size: 8658581
-  dataset_size: 31496853
----
-# Dataset Card for "code_x_glue_cc_clone_detection_poj_104"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits-sample-size)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
-
-### Dataset Summary
-
-CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
-
-Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
-We use POJ-104 dataset on this task.
-
-### Supported Tasks and Leaderboards
-
-- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes with the same semantics.
-
-### Languages
-
-- C++ **programming** language
-
-## Dataset Structure
-
-### Data Instances
-
-An example of 'train' looks as follows.
-```
-{
-    "code": "\nint f(int shu,int min)\n{ \n  int k=1;\n  if(shu < min)\n  { \n    k= 0; \n   return k;\n  } \n  else\n {\n  for(int i = min;i<shu;i++)\n  { \n    if(shu%i == 0)\n    { \n         k=k+ f(shu/i,i); \n    } \n  \n    \n  } \n    return k; \n}\n} \n\nmain()\n{\n      int n,i,a;\n      scanf(\"%d\",&n);\n      \n      for(i=0;i<n;i++)\n      {\n          scanf(\"%d\",&a);\n          \n          if(i!=n-1)                                                        \n           printf(\"%d\\n\",f(a,2));\n           else\n           printf(\"%d\",f(a,2));                           \n                                      \n                     \n                      \n      }              \n                     \n                      \n                      }", 
-    "id": 0, 
-    "label": "home"
-}
-```
-
-### Data Fields
-
-In the following each data field in go is explained for each config. The data fields are the same among all splits.
-
-#### default
-
-|field name| type |                 description                  |
-|----------|------|----------------------------------------------|
-|id        |int32 | Index of the sample                          |
-|code      |string| The full text of the function                |
-|label     |string| The id of problem that the source code solves|
-
-### Data Splits
-
-| name  |train|validation|test |
-|-------|----:|---------:|----:|
-|default|32000|      8000|12000|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-https://github.com/microsoft, https://github.com/madlag
-
-### Licensing Information
-
-Computational Use of Data Agreement (C-UDA) License.
-
-### Citation Information
-
-```
-@inproceedings{mou2016convolutional,
-  title={Convolutional neural networks over tree structures for programming language processing},
-  author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
-  booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
-  pages={1287--1293},
-  year={2016}
-}
-```
-
-### Contributions
-
-Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

code_x_glue_cc_clone_detection_poj104.py

@@ -1,93 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
-We use POJ-104 dataset on this task."""
-
-_CITATION = """@inproceedings{mou2016convolutional,
-title={Convolutional neural networks over tree structures for programming language processing},
-author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
-booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
-pages={1287--1293},
-year={2016}
-}"""
-
-
-class CodeXGlueCcCloneDetectionPoj104Impl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "code": datasets.Value("string"),  # The full text of the function
-        "label": datasets.Value("string"),  # The id of problem that the source code solves
-    }
-
-    _SUPERVISED_KEYS = ["label"]
-
-    SPLIT_RANGES = {"train": (1, 65), "valid": (65, 81), "test": (81, 195)}
-
-    def _generate_examples(self, files, split_name):
-        cont = 0
-        for path, f in files:
-            # path are in the format ProgramData/{index}/{filename}
-            label = int(path.split("/")[1])
-            if self.SPLIT_RANGES[split_name][0] <= label <= self.SPLIT_RANGES[split_name][1]:
-                js = {}
-                js["label"] = str(label)
-                js["id"] = cont
-                js["code"] = f.read().decode("latin-1")
-                yield cont, js
-                cont += 1
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCloneDetectionPoj104": CodeXGlueCcCloneDetectionPoj104Impl,
-}
-
-
-class CodeXGlueCcCloneDetectionPoj104(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]:
-        name = self.config.name
-        info = DEFINITIONS[name]
-        archive = dl_manager.download(info["raw_url"] + "/programs.tar.gz")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "valid"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split_name": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, files, split_name):
-        return self.child._generate_examples(files, split_name)

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,
-    }

dataset_infos.json

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-{"default": {"description": "CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104\n\nGiven a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.\nWe use POJ-104 dataset on this task.", "citation": "@inproceedings{mou2016convolutional,\ntitle={Convolutional neural networks over tree structures for programming language processing},\nauthor={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},\nbooktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},\npages={1287--1293},\nyear={2016}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "code": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "label", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_clone_detection_poj104", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 18878686, "num_examples": 32000, "dataset_name": "code_x_glue_cc_clone_detection_poj104"}, "validation": {"name": "validation", "num_bytes": 5765303, "num_examples": 8000, "dataset_name": "code_x_glue_cc_clone_detection_poj104"}, "test": {"name": "test", "num_bytes": 6852864, "num_examples": 12000, "dataset_name": "code_x_glue_cc_clone_detection_poj104"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-POJ-104/dataset/programs.tar.gz": {"num_bytes": 8658581, "checksum": "c0b8ef3ee9c9159c882dc9337cb46da0e612a28e24852a83f8a1cd68c838f390"}}, "download_size": 8658581, "post_processing_size": null, "dataset_size": 31496853, "size_in_bytes": 40155434}}

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generated_definitions.py

@@ -1,12 +0,0 @@
-DEFINITIONS = {
-    "default": {
-        "class_name": "CodeXGlueCcCloneDetectionPoj104",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104",
-        "dir_name": "Clone-detection-POJ-104",
-        "name": "default",
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Clone-detection-POJ-104/dataset",
-        "sizes": {"test": 12000, "train": 32000, "validation": 8000},
-    }
-}