Update files from the datasets library (from 1.8.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- code
+licenses:
+- other-C-UDA
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-retrieval
+task_ids:
+- document-retrieval
+---
+# 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

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+import os
+import os.path
+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_urls(self, split_name):
+        yield "data", "programs.tar.gz"
+
+    def _generate_examples(self, split_name, file_paths):
+        def files(path):
+            g = os.walk(path)
+            file = []
+            for path, dir_list, file_list in g:
+                for file_name in file_list:
+                    file.append(os.path.join(path, file_name))
+            return file
+
+        root_path = file_paths["data"]
+        cont = 0
+        for i in range(*self.SPLIT_RANGES[split_name]):
+            items = files(os.path.join(root_path, "ProgramData/{}".format(i)))
+            for item in items:
+                js = {}
+                js["label"] = item.split("/")[1]
+                js["id"] = cont
+                js["code"] = open(item, encoding="latin-1").read()
+                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]:
+        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

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+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}}

dummy/default/0.0.0/dummy_data.zip

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+oid sha256:829541e228b4c3cc9661425a32df14141be42f3cd2e794757fbaa5eefdcf609d
+size 2918

generated_definitions.py

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+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},
+    }
+}