| from __future__ import annotations |
|
|
| import datasets |
|
|
| from mteb.abstasks import MultilingualTask |
| from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval |
| from mteb.abstasks.TaskMetadata import TaskMetadata |
|
|
| _LANGS = ["python", "javascript", "go", "ruby", "java", "php"] |
|
|
|
|
| class CodeSearchNetRetrieval(MultilingualTask, AbsTaskRetrieval): |
| _EVAL_SPLIT = "test" |
| metadata = TaskMetadata( |
| name="CodeSearchNetRetrieval", |
| description="The dataset is a collection of code snippets and their corresponding natural language queries. The task is to retrieve the most relevant code snippet for a given query.", |
| reference="https://huggingface.co/datasets/code_search_net/viewer", |
| dataset={ |
| "path": "code_search_net", |
| "revision": "fdc6a9e39575768c27eb8a2a5f702bf846eb4759", |
| }, |
| type="Retrieval", |
| category="p2p", |
| eval_splits=[_EVAL_SPLIT], |
| eval_langs={lang: [lang + "-Code"] for lang in _LANGS}, |
| main_score="ndcg_at_10", |
| date=("2019-01-01", "2019-12-31"), |
| form=["written"], |
| domains=["Programming"], |
| task_subtypes=["Code retrieval"], |
| license="Not specified", |
| socioeconomic_status="high", |
| annotations_creators="derived", |
| dialect=[], |
| text_creation="found", |
| bibtex_citation="@article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} }", |
| n_samples={ |
| _EVAL_SPLIT: 1000, |
| }, |
| avg_character_length={"test": 1196.4609}, |
| ) |
|
|
| def load_data(self, **kwargs): |
| if self.data_loaded: |
| return |
|
|
| data = datasets.load_dataset( |
| split=self._EVAL_SPLIT, |
| trust_remote_code=True, |
| streaming=True, |
| **self.metadata_dict["dataset"], |
| ) |
| data = data.shuffle(seed=42) |
|
|
| |
| data = data.map( |
| lambda ex: { |
| "func_code_string": ex["func_code_string"].replace( |
| ex["func_documentation_string"], "" |
| ) |
| } |
| ) |
|
|
| lang_subs = {lang: [] for lang in _LANGS} |
| for ex in data: |
| lang_subs[ex["language"]].append(ex) |
|
|
| self.queries = {} |
| self.corpus = {} |
| self.relevant_docs = {} |
|
|
| for lang, sub in lang_subs.items(): |
| sub = sub[ |
| : min(len(sub), self.metadata_dict["n_samples"][self._EVAL_SPLIT]) |
| ] |
|
|
| self.queries[lang] = { |
| self._EVAL_SPLIT: { |
| str(i): row["func_documentation_string"] |
| for i, row in enumerate(sub) |
| } |
| } |
| self.corpus[lang] = { |
| self._EVAL_SPLIT: { |
| str(row["func_code_url"]): {"text": row["func_code_string"]} |
| for row in sub |
| } |
| } |
| self.relevant_docs[lang] = { |
| self._EVAL_SPLIT: { |
| str(i): {row["func_code_url"]: 1} for i, row in enumerate(sub) |
| } |
| } |
|
|
| self.data_loaded = True |
|
|
