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README.md

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-
-## Dataset Summary
-
-Scotch is a dataset of about 19 million functions collected from open-source repositiories from GitHub with permissive licenses. Each function has its corresponding code context and about 4 million functions have corresponding docstrings.
-
-### Languages
-The dataset includes functions written in programming languages Python, Java, Javascript, and Go.
-
-## Statistics
-
-### Split
-The functions with docstrings is splitted into train, valid, and test set of 3200626, 400077, 400080 functions respectively.
-
-## Features
-
-Each function consists of following features:
-
-* repository_name: Name of the repository the function belongs to.
-* function_path: Path of the function within the repository.
-* function_identifier: Function name/identifier.
-* language: Programming language the function is written in.
-* function: Function string.
-* docstring: Function docstring.
-* function_url: URL to the function code.
-* context: Code context.
-* license: License info of the repository (includes only repositories with permissive licenses).
-
-## Data Collection
-
-The dataset is collected from GitHub repositories of respective languages with 5 or more stars. Such repositories are listed using [SEART](https://seart-ghs.si.usi.ch/). Functions are parsed using a lightweight parser build on top of function parser from [CodeSearchNet dataset](https://github.com/github/CodeSearchNet/tree/master/function_parser) and repositories were collected with help of [github-downloader from EleutherAI](https://github.com/EleutherAI/github-downloader).
-
-### Data Processing
-All the code without permissive licenses are removed and deduplication is performed on the remaining set of functions. Afterwards, all the functions with single line of code, whose docstring contains non-English characters are removed. Files with multiple same functions are excluded. This results in about 19M functions. To obtain a dataset of NL-Code pairs, functions with no docstrings or doctrings less than 3 tokens separated by white-space are excluded. Following CodeSearchNet, functions with 'test' keyword in their name are excluded.
-
-## License
-
-This dataset is under MIT License. However, the repositories the functions are collected from may have several permissive licenses. Those licenses include MIT License, Apache License 2.0, BSD 3-Clause “New” or “Revised” License, BSD 2-Clause “Simplified” License, and ISC License.

Scotch.py

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-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""TODO: Add a description here."""
-
-
-import csv
-import json
-import os
-
-import datasets
-import pickle 
-from pathlib import Path
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """
-@inproceedings{
-dahal2022scotch,
-title={Scotch: A Semantic Code Search Engine for {IDE}s},
-author={Samip Dahal and Adyasha Maharana and Mohit Bansal},
-booktitle={Deep Learning for Code Workshop},
-year={2022},
-url={https://openreview.net/forum?id=rSxfCiOZk-c}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-Scotch is a dataset of about 19 million functions collected from open-source repositiories from GitHub with permissive licenses. Each function has its corresponding code context and about 4 million functions have corresponding docstrings. The dataset includes functions written in programming languages Python, Java, Javascript, and Go."""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/sdpmas/Scotch"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "The MIT License"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-languages=['python','javascript','java','go']
-language_map={'python':'py','javascript':'js','go':'go','java':'java'}
-_URLs = {lang:f'https://scotchdata.s3.amazonaws.com/{lang}.tar.gz' for lang in languages}
-_URLs['all']=_URLs.copy()
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class ScotchDataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="all", version=VERSION, description="All available data with docstrings"),
-        datasets.BuilderConfig(name="python", version=VERSION, description="Python data"),
-        datasets.BuilderConfig(name="javascript", version=VERSION, description="Javascript data"),
-        datasets.BuilderConfig(name="java", version=VERSION, description="Java data"),
-        datasets.BuilderConfig(name="go", version=VERSION, description="Go data"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "all"
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        
-        features = datasets.Features(
-            {
-                "repository_name": datasets.Value("string"),
-                "function_path": datasets.Value("string"),
-                "function_identifier": datasets.Value("string"),
-                "language": datasets.Value("string"),
-                "function": datasets.Value("string"),
-                "docstring": datasets.Value("string"),
-                "function_url": datasets.Value("string"),
-                "context":datasets.Value("string"),
-                "license":datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,  # Here we define them above because they are different between the two configurations
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        my_urls = _URLs[self.config.name]
-        if isinstance(my_urls, str):
-            my_urls = {self.config.name:my_urls}
-        data_dir = [os.path.join(lang_dir,lang) for lang,lang_dir in dl_manager.download_and_extract(my_urls).items()]
-        
-        # splitpaths={split:[os.path.join(lang_dir,f'{split}.bin') for lang_dir in data_dir] for split in ['train','valid','test']}
-        splitpaths={}
-        for split in ['train','valid','test']:
-            for lang_dir in data_dir:
-                # Path glob .bin files
-                lang_split_files=sorted(Path(os.path.join(lang_dir,split)).glob('*.bin'))
-                if not split in splitpaths:
-                    splitpaths[split]=lang_split_files
-                else:
-                    splitpaths[split].extend(lang_split_files)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": splitpaths['train'],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": splitpaths['test'],
-                    "split": "test"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": splitpaths['valid'],
-                    "split": "valid",
-                },
-            ),
-        ]
-
-    def _generate_examples(
-        self, filepath,split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    ):
-        """ Yields examples as (key, example) tuples. """
-        count=-1
-        for i,filepath in enumerate(filepath):
-            loaded_f=pickle.load(open(filepath,'rb'))
-            for j, func in enumerate(loaded_f):
-                count+=1
-                yield count,{
-                    "repository_name": str(func['nwo']),
-                    "function_path":str(func['path']),
-                    "function_identifier": str(func['identifier']),
-                    "language": str(func['language']),
-                    "function": str(func['function']),
-                    "docstring": str(func['docstring']),
-                    "function_url": str(func['url']),
-                    "context":str(func['context']),
-                    "license":str(func['license']), 
-                }
-               

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