Update files from the datasets library (from 1.8.0)

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

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- code
+- en
+licenses:
+- other-C-UDA
+multilinguality:
+- other-programming-languages
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-retrieval
+task_ids:
+- document-retrieval
+---
+# 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

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+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

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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 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}}

dummy/default/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca5e15a57bd9d5db3b5f4a5851768f221b73fb7d9265e5830033b187c5bc041d
+size 17498

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},
+    }
+}