Datasets:
Sub-tasks:
slot-filling
Languages:
code
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
| import json | |
| from typing import List | |
| import datasets | |
| from .common import Child | |
| from .generated_definitions import DEFINITIONS | |
| _DESCRIPTION = """Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity. | |
| We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method call with specific parameters, a function signature, a loop condition, a variable definition and so on. When a software develop finish one or more tokens of the current line, the line level completion model is expected to generate the entire line of syntactically correct code. | |
| Line level code completion task shares the train/dev dataset with token level completion. After training a model on CodeCompletion-token, you could directly use it to test on line-level completion.""" | |
| _CITATION = """@article{raychev2016probabilistic, | |
| title={Probabilistic Model for Code with Decision Trees}, | |
| author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin}, | |
| journal={ACM SIGPLAN Notices}, | |
| pages={731--747}, | |
| year={2016}, | |
| publisher={ACM New York, NY, USA} | |
| } | |
| @inproceedings{allamanis2013mining, | |
| title={Mining Source Code Repositories at Massive Scale using Language Modeling}, | |
| author={Allamanis, Miltiadis and Sutton, Charles}, | |
| booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)}, | |
| pages={207--216}, | |
| year={2013}, | |
| organization={IEEE} | |
| }""" | |
| class CodeXGlueCcCodeCompletionLineImpl(Child): | |
| _DESCRIPTION = _DESCRIPTION | |
| _CITATION = _CITATION | |
| _FEATURES = { | |
| "id": datasets.Value("int32"), # Index of the sample | |
| "input": datasets.Value("string"), # Input code string | |
| "gt": datasets.Value("string"), # Code string to be predicted | |
| } | |
| _SUPERVISED_KEYS = ["gt"] | |
| def generate_urls(self, split_name): | |
| yield "data", "test.json" | |
| def _generate_examples(self, split_name, file_paths): | |
| with open(file_paths["data"], encoding="utf-8") as f: | |
| for idx, line in enumerate(f): | |
| entry = json.loads(line) | |
| entry["id"] = idx | |
| yield idx, entry | |
| CLASS_MAPPING = { | |
| "CodeXGlueCcCodeCompletionLine": CodeXGlueCcCodeCompletionLineImpl, | |
| } | |
| class CodeXGlueCcCodeCompletionLine(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) | |