Update parquet files

README.md

@@ -1,256 +0,0 @@
----
-annotations_creators:
-- found
-language_creators:
-- found
-language:
-- code
-- en
-license:
-- c-uda
-multilinguality:
-- other-programming-languages
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-task_categories:
-- text-retrieval
-task_ids:
-- document-retrieval
-pretty_name: CodeXGlueTcNlCodeSearchAdv
-dataset_info:
-  features:
-  - name: id
-    dtype: int32
-  - name: repo
-    dtype: string
-  - name: path
-    dtype: string
-  - name: func_name
-    dtype: string
-  - name: original_string
-    dtype: string
-  - name: language
-    dtype: string
-  - name: code
-    dtype: string
-  - name: code_tokens
-    sequence: string
-  - name: docstring
-    dtype: string
-  - name: docstring_tokens
-    sequence: string
-  - name: sha
-    dtype: string
-  - name: url
-    dtype: string
-  - name: docstring_summary
-    dtype: string
-  - name: parameters
-    dtype: string
-  - name: return_statement
-    dtype: string
-  - name: argument_list
-    dtype: string
-  - name: identifier
-    dtype: string
-  - name: nwo
-    dtype: string
-  - name: score
-    dtype: float32
-  splits:
-  - name: train
-    num_bytes: 820716084
-    num_examples: 251820
-  - name: validation
-    num_bytes: 23468834
-    num_examples: 9604
-  - name: test
-    num_bytes: 47433760
-    num_examples: 19210
-  download_size: 966025624
-  dataset_size: 891618678
----
-# Dataset Card for "code_x_glue_tc_nl_code_search_adv"
-
-## 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/Text-Code/NL-code-search-Adv
-
-### Dataset Summary
-
-CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv
-
-The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
-- Remove examples that codes cannot be parsed into an abstract syntax tree.
-- Remove examples that #tokens of documents is < 3 or >256
-- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
-- Remove examples that documents are not English.
-
-### Supported Tasks and Leaderboards
-
-- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes from a given **English** natural language query.
-
-### Languages
-
-- Python **programming** language
-- English **natural** language
-
-## Dataset Structure
-
-### Data Instances
-
-An example of 'validation' looks as follows.
-```
-{
-    "argument_list": "", 
-    "code": "def Func(arg_0, arg_1='.', arg_2=True, arg_3=False, **arg_4):\n    \"\"\"Downloads Dailymotion videos by URL.\n    \"\"\"\n\n    arg_5 = get_content(rebuilt_url(arg_0))\n    arg_6 = json.loads(match1(arg_5, r'qualities\":({.+?}),\"'))\n    arg_7 = match1(arg_5, r'\"video_title\"\\s*:\\s*\"([^\"]+)\"') or \\\n            match1(arg_5, r'\"title\"\\s*:\\s*\"([^\"]+)\"')\n    arg_7 = unicodize(arg_7)\n\n    for arg_8 in ['1080','720','480','380','240','144','auto']:\n        try:\n            arg_9 = arg_6[arg_8][1][\"url\"]\n            if arg_9:\n                break\n        except KeyError:\n            pass\n\n    arg_10, arg_11, arg_12 = url_info(arg_9)\n\n    print_info(site_info, arg_7, arg_10, arg_12)\n    if not arg_3:\n        download_urls([arg_9], arg_7, arg_11, arg_12, arg_1=arg_1, arg_2=arg_2)", 
-    "code_tokens": ["def", "Func", "(", "arg_0", ",", "arg_1", "=", "'.'", ",", "arg_2", "=", "True", ",", "arg_3", "=", "False", ",", "**", "arg_4", ")", ":", "arg_5", "=", "get_content", "(", "rebuilt_url", "(", "arg_0", ")", ")", "arg_6", "=", "json", ".", "loads", "(", "match1", "(", "arg_5", ",", "r'qualities\":({.+?}),\"'", ")", ")", "arg_7", "=", "match1", "(", "arg_5", ",", "r'\"video_title\"\\s*:\\s*\"([^\"]+)\"'", ")", "or", "match1", "(", "arg_5", ",", "r'\"title\"\\s*:\\s*\"([^\"]+)\"'", ")", "arg_7", "=", "unicodize", "(", "arg_7", ")", "for", "arg_8", "in", "[", "'1080'", ",", "'720'", ",", "'480'", ",", "'380'", ",", "'240'", ",", "'144'", ",", "'auto'", "]", ":", "try", ":", "arg_9", "=", "arg_6", "[", "arg_8", "]", "[", "1", "]", "[", "\"url\"", "]", "if", "arg_9", ":", "break", "except", "KeyError", ":", "pass", "arg_10", ",", "arg_11", ",", "arg_12", "=", "url_info", "(", "arg_9", ")", "print_info", "(", "site_info", ",", "arg_7", ",", "arg_10", ",", "arg_12", ")", "if", "not", "arg_3", ":", "download_urls", "(", "[", "arg_9", "]", ",", "arg_7", ",", "arg_11", ",", "arg_12", ",", "arg_1", "=", "arg_1", ",", "arg_2", "=", "arg_2", ")"], 
-    "docstring": "Downloads Dailymotion videos by URL.", 
-    "docstring_summary": "Downloads Dailymotion videos by URL.", 
-    "docstring_tokens": ["Downloads", "Dailymotion", "videos", "by", "URL", "."], 
-    "func_name": "", 
-    "id": 0, 
-    "identifier": "dailymotion_download", 
-    "language": "python", 
-    "nwo": "soimort/you-get", 
-    "original_string": "", 
-    "parameters": "(url, output_dir='.', merge=True, info_only=False, **kwargs)", 
-    "path": "src/you_get/extractors/dailymotion.py", 
-    "repo": "", 
-    "return_statement": "", 
-    "score": 0.9997601509094238, 
-    "sha": "b746ac01c9f39de94cac2d56f665285b0523b974", 
-    "url": "https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/dailymotion.py#L13-L35"
-}
-```
-
-### 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                                                               |
-|repo             |string                 | repo: the owner/repo                                                              |
-|path             |string                 | path: the full path to the original file                                          |
-|func_name        |string                 | func_name: the function or method name                                            |
-|original_string  |string                 | original_string: the raw string before tokenization or parsing                    |
-|language         |string                 | language: the programming language                                                |
-|code             |string                 | code/function: the part of the original_string that is code                       |
-|code_tokens      |Sequence[string]       | code_tokens/function_tokens: tokenized version of code                            |
-|docstring        |string                 | docstring: the top-level comment or docstring, if it exists in the original string|
-|docstring_tokens |Sequence[string]       | docstring_tokens: tokenized version of docstring                                  |
-|sha              |string                 | sha of the file                                                                   |
-|url              |string                 | url of the file                                                                   |
-|docstring_summary|string                 | Summary of the docstring                                                          |
-|parameters       |string                 | parameters of the function                                                        |
-|return_statement |string                 | return statement                                                                  |
-|argument_list    |string                 | list of arguments of the function                                                 |
-|identifier       |string                 | identifier                                                                        |
-|nwo              |string                 | nwo                                                                               |
-|score            |datasets.Value("float"]| score for this search                                                             |
-
-### Data Splits
-
-| name  |train |validation|test |
-|-------|-----:|---------:|----:|
-|default|251820|      9604|19210|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-
-#### Initial Data Collection and Normalization
-
-Data from CodeSearchNet Challenge dataset.
-[More Information Needed]
-
-#### Who are the source language producers?
-
-Software Engineering developers.
-
-### 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
-
-```
-@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}
-}
-```
-
-### Contributions
-
-Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

code_x_glue_tc_nl_code_search_adv.py

@@ -1,206 +0,0 @@
-import json
-import os
-import os.path
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
-- Remove examples that codes cannot be parsed into an abstract syntax tree.
-- Remove examples that #tokens of documents is < 3 or >256
-- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
-- Remove examples that documents are not English.
-"""
-_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}
-}"""
-
-
-class CodeXGlueCtCodeToTextBaseImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    # For each file, each line in the uncompressed file represents one function.
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "repo": datasets.Value("string"),  # repo: the owner/repo
-        "path": datasets.Value("string"),  # path: the full path to the original file
-        "func_name": datasets.Value("string"),  # func_name: the function or method name
-        "original_string": datasets.Value("string"),  # original_string: the raw string before tokenization or parsing
-        "language": datasets.Value("string"),  # language: the programming language name
-        "code": datasets.Value("string"),  # code/function: the part of the original_string that is code
-        "code_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # code_tokens/function_tokens: tokenized version of code
-        "docstring": datasets.Value(
-            "string"
-        ),  # docstring: the top-level comment or docstring, if it exists in the original string
-        "docstring_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # docstring_tokens: tokenized version of docstring
-        "sha": datasets.Value("string"),  # sha of the file
-        "url": datasets.Value("string"),  # url of the file
-    }
-
-    _SUPERVISED_KEYS = ["docstring", "docstring_tokens"]
-
-    def generate_urls(self, split_name, language):
-        yield "language", f"https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/{language}.zip"
-        yield "dataset", "dataset.zip"
-
-    def get_data_files(self, split_name, file_paths, language):
-        language_specific_path = file_paths["language"]
-        final_path = os.path.join(language_specific_path, language, "final")
-        # Make some cleanup to save space
-        for path in os.listdir(final_path):
-            if path.endswith(".pkl"):
-                os.unlink(path)
-
-        data_files = []
-        for root, dirs, files in os.walk(final_path):
-            for file in files:
-                temp = os.path.join(root, file)
-                if ".jsonl" in temp:
-                    if split_name in temp:
-                        data_files.append(temp)
-        return data_files
-
-    def post_process(self, split_name, language, js):
-        return js
-
-    def _generate_examples(self, split_name, file_paths, language):
-        import gzip
-
-        data_set_path = file_paths["dataset"]
-
-        data_files = self.get_data_files(split_name, file_paths, language)
-
-        urls = {}
-        f1_path_parts = [data_set_path, "dataset", language, f"{split_name}.txt"]
-        if self.SINGLE_LANGUAGE:
-            del f1_path_parts[2]
-
-        f1_path = os.path.join(*f1_path_parts)
-        with open(f1_path, encoding="utf-8") as f1:
-            for line in f1:
-                line = line.strip()
-                urls[line] = True
-
-        idx = 0
-        for file in data_files:
-            if ".gz" in file:
-                f = gzip.open(file)
-            else:
-                f = open(file, encoding="utf-8")
-
-            for line in f:
-                line = line.strip()
-                js = json.loads(line)
-                if js["url"] in urls:
-                    js["id"] = idx
-                    js = self.post_process(split_name, language, js)
-                    if "partition" in js:
-                        del js["partition"]
-                    yield idx, js
-                    idx += 1
-            f.close()
-
-
-class CodeXGlueTcNLCodeSearchAdvImpl(CodeXGlueCtCodeToTextBaseImpl):
-    LANGUAGE = "python"
-    SINGLE_LANGUAGE = True
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "repo": datasets.Value("string"),  # repo: the owner/repo
-        "path": datasets.Value("string"),  # path: the full path to the original file
-        "func_name": datasets.Value("string"),  # func_name: the function or method name
-        "original_string": datasets.Value("string"),  # original_string: the raw string before tokenization or parsing
-        "language": datasets.Value("string"),  # language: the programming language
-        "code": datasets.Value("string"),  # code/function: the part of the original_string that is code
-        "code_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # code_tokens/function_tokens: tokenized version of code
-        "docstring": datasets.Value(
-            "string"
-        ),  # docstring: the top-level comment or docstring, if it exists in the original string
-        "docstring_tokens": datasets.features.Sequence(
-            datasets.Value("string")
-        ),  # docstring_tokens: tokenized version of docstring
-        "sha": datasets.Value("string"),  # sha of the file
-        "url": datasets.Value("string"),  # url of the file
-        "docstring_summary": datasets.Value("string"),  # Summary of the docstring
-        "parameters": datasets.Value("string"),  # parameters of the function
-        "return_statement": datasets.Value("string"),  # return statement
-        "argument_list": datasets.Value("string"),  # list of arguments of the function
-        "identifier": datasets.Value("string"),  # identifier
-        "nwo": datasets.Value("string"),  # nwo
-        "score": datasets.Value("float"),  # score for this search
-    }
-
-    def post_process(self, split_name, language, js):
-        for suffix in "_tokens", "":
-            key = "function" + suffix
-            if key in js:
-                js["code" + suffix] = js[key]
-                del js[key]
-
-        for key in self._FEATURES:
-            if key not in js:
-                if key == "score":
-                    js[key] = -1
-                else:
-                    js[key] = ""
-
-        return js
-
-    def generate_urls(self, split_name):
-        for e in super().generate_urls(split_name, self.LANGUAGE):
-            yield e
-
-    def get_data_files(self, split_name, file_paths, language):
-        if split_name == "train":
-            return super().get_data_files(split_name, file_paths, language)
-        else:
-            data_set_path = file_paths["dataset"]
-            data_file = os.path.join(data_set_path, "dataset", "test_code.jsonl")
-            return [data_file]
-
-    def _generate_examples(self, split_name, file_paths):
-        for e in super()._generate_examples(split_name, file_paths, self.LANGUAGE):
-            yield e
-
-
-CLASS_MAPPING = {
-    "CodeXGlueTcNLCodeSearchAdv": CodeXGlueTcNLCodeSearchAdvImpl,
-}
-
-
-class CodeXGlueTcNlCodeSearchAdv(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

@@ -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

@@ -1 +0,0 @@
-{"default": {"description": "CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n- Remove examples that codes cannot be parsed into an abstract syntax tree.\n- Remove examples that #tokens of documents is < 3 or >256\n- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n- Remove examples that documents are not English.\n", "citation": "@article{husain2019codesearchnet,\ntitle={Codesearchnet challenge: Evaluating the state of semantic code search},\nauthor={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\njournal={arXiv preprint arXiv:1909.09436},\nyear={2019}\n}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "repo": {"dtype": "string", "id": null, "_type": "Value"}, "path": {"dtype": "string", "id": null, "_type": "Value"}, "func_name": {"dtype": "string", "id": null, "_type": "Value"}, "original_string": {"dtype": "string", "id": null, "_type": "Value"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "code": {"dtype": "string", "id": null, "_type": "Value"}, "code_tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "docstring": {"dtype": "string", "id": null, "_type": "Value"}, "docstring_tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sha": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "docstring_summary": {"dtype": "string", "id": null, "_type": "Value"}, "parameters": {"dtype": "string", "id": null, "_type": "Value"}, "return_statement": {"dtype": "string", "id": null, "_type": "Value"}, "argument_list": {"dtype": "string", "id": null, "_type": "Value"}, "identifier": {"dtype": "string", "id": null, "_type": "Value"}, "nwo": {"dtype": "string", "id": null, "_type": "Value"}, "score": {"dtype": "float32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "docstring", "output": "docstring_tokens"}, "task_templates": null, "builder_name": "code_x_glue_tc_nl_code_search_adv", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 820716084, "num_examples": 251820, "dataset_name": "code_x_glue_tc_nl_code_search_adv"}, "validation": {"name": "validation", "num_bytes": 23468834, "num_examples": 9604, "dataset_name": "code_x_glue_tc_nl_code_search_adv"}, "test": {"name": "test", "num_bytes": 47433760, "num_examples": 19210, "dataset_name": "code_x_glue_tc_nl_code_search_adv"}}, "download_checksums": {"https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/python.zip": {"num_bytes": 940909997, "checksum": "7223c6460bebfa85697b586da91e47bc5d64790a4d60bba5917106458ab6b40e"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Text-Code/NL-code-search-Adv/dataset.zip": {"num_bytes": 25115627, "checksum": "b4d5157699ca3bda7a33674f17d7b24294b4c8f36f650cea01d3d0dbcefdc656"}}, "download_size": 966025624, "post_processing_size": null, "dataset_size": 891618678, "size_in_bytes": 1857644302}}

default/code_x_glue_tc_nl_code_search_adv-test.parquet

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default/code_x_glue_tc_nl_code_search_adv-train-00000-of-00002.parquet

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default/code_x_glue_tc_nl_code_search_adv-train-00001-of-00002.parquet

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default/code_x_glue_tc_nl_code_search_adv-validation.parquet

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generated_definitions.py

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-DEFINITIONS = {
-    "default": {
-        "class_name": "CodeXGlueTcNLCodeSearchAdv",
-        "dataset_type": "Text-Code",
-        "description": "CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv",
-        "dir_name": "NL-code-search-Adv",
-        "name": "default",
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Text-Code/NL-code-search-Adv",
-        "sizes": {"test": 19210, "train": 251820, "validation": 9604},
-    }
-}