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
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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- code
+licenses:
+- other-C-UDA
+multilinguality:
+- monolingual
+size_categories:
+  go:
+  - 10K<n<100K
+  java:
+  - 10K<n<100K
+  javascript:
+  - 10K<n<100K
+  php:
+  - 10K<n<100K
+  python:
+  - 10K<n<100K
+  ruby:
+  - 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- slot-filling
+---
+# Dataset Card for "code_x_glue_cc_cloze_testing_maxmin"
+
+## 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/Code-Code/ClozeTesting-maxmin
+
+### Dataset Summary
+
+CodeXGLUE ClozeTesting-maxmin dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin
+
+Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
+Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
+The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.
+
+### Supported Tasks and Leaderboards
+
+- `slot-filling`: The dataset can be used to train a model for predicting the missing token from a piece of code, similar to the Cloze test.
+
+### Languages
+
+- Go **programming** language
+- Java **programming** language
+- Javascript **programming** language
+- PHP **programming** language
+- Python **programming** language
+- Ruby **programming** language
+
+## Dataset Structure
+
+### Data Instances
+
+#### go
+
+An example of 'train' looks as follows.
+```
+{
+    "id": 0, 
+    "idx": "maxmin-1", 
+    "nl_tokens": ["SetMaxStructPoolSize", "sets", "the", "struct", "pools", "max", "size", ".", "this", "may", "be", "usefull", "for", "fine", "grained", "performance", "tuning", "towards", "your", "application", "however", "the", "default", "should", "be", "fine", "for", "nearly", "all", "cases", ".", "only", "increase", "if", "you", "have", "a", "deeply", "nested", "struct", "structure", ".", "NOTE", ":", "this", "method", "is", "not", "thread", "-", "safe", "NOTE", ":", "this", "is", "only", "here", "to", "keep", "compatibility", "with", "v5", "in", "v6", "the", "method", "will", "be", "removed"], 
+    "pl_tokens": ["func", "(", "v", "*", "Validate", ")", "SetMaxStructPoolSize", "(", "<mask>", "int", ")", "{", "structPool", "=", "&", "sync", ".", "Pool", "{", "New", ":", "newStructErrors", "}", "\n", "}"]
+}
+```
+
+#### java
+
+An example of 'train' looks as follows.
+```
+{
+    "id": 0, 
+    "idx": "maxmin-1", 
+    "nl_tokens": ["Test", "whether", "find", "can", "be", "found", "at", "position", "startPos", "in", "the", "string", "src", "."], 
+    "pl_tokens": ["public", "static", "boolean", "startsWith", "(", "char", "[", "]", "src", ",", "char", "[", "]", "find", ",", "int", "startAt", ")", "{", "int", "startPos", "=", "startAt", ";", "boolean", "result", "=", "true", ";", "// Check ranges", "if", "(", "src", ".", "length", "<", "startPos", "+", "find", ".", "length", ")", "{", "result", "=", "false", ";", "}", "else", "{", "final", "int", "<mask>", "=", "find", ".", "length", ";", "for", "(", "int", "a", "=", "0", ";", "a", "<", "max", "&&", "result", ";", "a", "++", ")", "{", "if", "(", "src", "[", "startPos", "]", "!=", "find", "[", "a", "]", ")", "{", "result", "=", "false", ";", "}", "startPos", "++", ";", "}", "}", "return", "result", ";", "}"]
+}
+```
+
+#### javascript
+
+An example of 'train' looks as follows.
+```
+{
+    "id": 0, 
+    "idx": "maxmin-1", 
+    "nl_tokens": ["string", ".", "max", "Maximum", "length", "of", "the", "string"], 
+    "pl_tokens": ["function", "(", "string", ")", "{", "// string.check check sting type and size", "return", "(", "(", "typeof", "string", "===", "'string'", "||", "string", "instanceof", "String", ")", "&&", "string", ".", "length", ">=", "this", ".", "<mask>", "&&", "string", ".", "length", "<=", "this", ".", "max", "&&", "(", "!", "this", ".", "match", "||", "string", ".", "match", "(", "this", ".", "match", ")", ")", ")", ";", "}"]
+}
+```
+
+#### php
+
+An example of 'train' looks as follows.
+```
+{
+    "id": 0, 
+    "idx": "maxmin-1", 
+    "nl_tokens": ["Read", "the", "next", "character", "from", "the", "supplied", "string", ".", "Return", "null", "when", "we", "have", "run", "out", "of", "characters", "."], 
+    "pl_tokens": ["public", "function", "readOne", "(", ")", "{", "if", "(", "$", "this", "->", "pos", "<=", "$", "this", "->", "<mask>", ")", "{", "$", "value", "=", "$", "this", "->", "string", "[", "$", "this", "->", "pos", "]", ";", "$", "this", "->", "pos", "+=", "1", ";", "}", "else", "{", "$", "value", "=", "null", ";", "}", "return", "$", "value", ";", "}"]
+}
+```
+
+#### python
+
+An example of 'train' looks as follows.
+```
+{
+    "id": 0, 
+    "idx": "maxmin-1", 
+    "nl_tokens": ["Returns", "intermediary", "colors", "for", "given", "list", "of", "colors", "."], 
+    "pl_tokens": ["def", "_interpolate", "(", "self", ",", "colors", ",", "n", "=", "100", ")", ":", "gradient", "=", "[", "]", "for", "i", "in", "_range", "(", "n", ")", ":", "l", "=", "len", "(", "colors", ")", "-", "1", "x", "=", "int", "(", "1.0", "*", "i", "/", "n", "*", "l", ")", "x", "=", "<mask>", "(", "x", "+", "0", ",", "l", ")", "y", "=", "min", "(", "x", "+", "1", ",", "l", ")", "base", "=", "1.0", "*", "n", "/", "l", "*", "x", "d", "=", "(", "i", "-", "base", ")", "/", "(", "1.0", "*", "n", "/", "l", ")", "r", "=", "colors", "[", "x", "]", ".", "r", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "r", "*", "d", "g", "=", "colors", "[", "x", "]", ".", "g", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "g", "*", "d", "b", "=", "colors", "[", "x", "]", ".", "b", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "b", "*", "d", "a", "=", "colors", "[", "x", "]", ".", "a", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "a", "*", "d", "gradient", ".", "append", "(", "color", "(", "r", ",", "g", ",", "b", ",", "a", ",", "mode", "=", "\"rgb\"", ")", ")", "gradient", ".", "append", "(", "colors", "[", "-", "1", "]", ")", "return", "gradient"]
+}
+```
+
+#### ruby
+
+An example of 'train' looks as follows.
+```
+{
+    "id": 0, 
+    "idx": "maxmin-1", 
+    "nl_tokens": ["Delete", "all", "copies", "that", "are", "older", "than", "the", "max", "age", "provided", "in", "seconds", "."], 
+    "pl_tokens": ["def", "clean", "(", "<mask>", ":", "24", "*", "60", "*", "60", ")", "Futex", ".", "new", "(", "file", ",", "log", ":", "@log", ")", ".", "open", "do", "list", "=", "load", "list", ".", "reject!", "do", "|", "s", "|", "if", "s", "[", ":time", "]", ">=", "Time", ".", "now", "-", "max", "false", "else", "@log", ".", "debug", "(", "\"Copy ##{s[:name]}/#{s[:host]}:#{s[:port]} is too old, over #{Age.new(s[:time])}\"", ")", "true", "end", "end", "save", "(", "list", ")", "deleted", "=", "0", "files", ".", "each", "do", "|", "f", "|", "next", "unless", "list", ".", "find", "{", "|", "s", "|", "s", "[", ":name", "]", "==", "File", ".", "basename", "(", "f", ",", "Copies", "::", "EXT", ")", "}", ".", "nil?", "file", "=", "File", ".", "join", "(", "@dir", ",", "f", ")", "size", "=", "File", ".", "size", "(", "file", ")", "File", ".", "delete", "(", "file", ")", "@log", ".", "debug", "(", "\"Copy at #{f} deleted: #{Size.new(size)}\"", ")", "deleted", "+=", "1", "end", "list", ".", "select!", "do", "|", "s", "|", "cp", "=", "File", ".", "join", "(", "@dir", ",", "\"#{s[:name]}#{Copies::EXT}\"", ")", "wallet", "=", "Wallet", ".", "new", "(", "cp", ")", "begin", "wallet", ".", "refurbish", "raise", "\"Invalid protocol #{wallet.protocol} in #{cp}\"", "unless", "wallet", ".", "protocol", "==", "Zold", "::", "PROTOCOL", "true", "rescue", "StandardError", "=>", "e", "FileUtils", ".", "rm_rf", "(", "cp", ")", "@log", ".", "debug", "(", "\"Copy at #{cp} deleted: #{Backtrace.new(e)}\"", ")", "deleted", "+=", "1", "false", "end", "end", "save", "(", "list", ")", "deleted", "end", "end"]
+}
+```
+
+### Data Fields
+
+In the following each data field in go is explained for each config. The data fields are the same among all splits.
+
+#### go, java, javascript, php, python, ruby
+
+|field name|      type      |         description          |
+|----------|----------------|------------------------------|
+|id        |int32           | Index of the sample          |
+|idx       |string          | Original index in the dataset|
+|nl_tokens |Sequence[string]| Natural language tokens      |
+|pl_tokens |Sequence[string]| Programming language tokens  |
+
+### Data Splits
+
+|   name   |train|
+|----------|----:|
+|go        |  152|
+|java      |  482|
+|javascript|  272|
+|php       |  407|
+|python    | 1264|
+|ruby      |   38|
+
+## 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{CodeXGLUE,
+  title={CodeXGLUE: An Open Challenge for Code Intelligence},
+  journal={arXiv},
+  year={2020},
+}
+@article{feng2020codebert,
+  title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
+  author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
+  journal={arXiv preprint arXiv:2002.08155},
+  year={2020}
+}
+@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_cc_cloze_testing_maxmin.py

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+import json
+from typing import List
+
+import datasets
+
+from .common import Child
+from .generated_definitions import DEFINITIONS
+
+
+_DESCRIPTION = """Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
+Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
+The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words."""
+
+_CITATION = """@article{CodeXGLUE,
+title={CodeXGLUE: An Open Challenge for Code Intelligence},
+journal={arXiv},
+year={2020},
+}
+@article{feng2020codebert,
+title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
+author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
+journal={arXiv preprint arXiv:2002.08155},
+year={2020}
+}
+@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 CodeXGlueCcClozeTestingImpl(Child):
+    _DESCRIPTION = _DESCRIPTION
+    _CITATION = _CITATION
+
+    _FEATURES = {
+        "id": datasets.Value("int32"),  # Index of the sample
+        "idx": datasets.Value("string"),  # Original index in the dataset
+        "nl_tokens": datasets.features.Sequence(datasets.Value("string")),  # Natural language tokens
+        "pl_tokens": datasets.features.Sequence(datasets.Value("string")),  # Programming language tokens
+    }
+
+    def generate_urls(self, split_name):
+        yield "data", "clozeTest.json"
+
+    def _generate_examples(self, split_name, file_paths):
+        with open(file_paths["data"], encoding="utf-8") as f:
+            j = json.load(f)
+            index = 0
+            for entry in j:
+                yield index, dict(
+                    id=index, idx=entry["idx"], nl_tokens=entry["nl_tokens"], pl_tokens=entry["pl_tokens"]
+                )
+                index += 1
+
+
+CLASS_MAPPING = {
+    "CodeXGlueCcClozeTestingMaxmin": CodeXGlueCcClozeTestingImpl,
+}
+
+
+class CodeXGlueCcClozeTestingMaxmin(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

@@ -0,0 +1 @@
+{"go": {"description": "CodeXGLUE ClozeTesting-maxmin dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin\n\nCloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.\nHere we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. 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The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.\nHere we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.\nThe only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.", "citation": "@article{CodeXGLUE,\ntitle={CodeXGLUE: An Open Challenge for Code Intelligence},\njournal={arXiv},\nyear={2020},\n}\n@article{feng2020codebert,\ntitle={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},\nauthor={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},\njournal={arXiv preprint arXiv:2002.08155},\nyear={2020}\n}\n@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/Code-Code/ClozeTesting-maxmin", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "idx": {"dtype": "string", "id": null, "_type": "Value"}, "nl_tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pl_tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "code_x_glue_cc_cloze_testing_maxmin", "config_name": "java", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 785754, "num_examples": 482, "dataset_name": "code_x_glue_cc_cloze_testing_maxmin"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/ClozeTesting-maxmin/data/cloze-maxmin/java/clozeTest.json": {"num_bytes": 1097733, "checksum": "ee2e7b0ad7d75ecb5e53b668f04fb39bdcebbacda820220b9bbb093136dd4082"}}, "download_size": 1097733, "post_processing_size": null, "dataset_size": 785754, "size_in_bytes": 1883487}, "javascript": {"description": "CodeXGLUE ClozeTesting-maxmin dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin\n\nCloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.\nHere we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.\nThe only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.", "citation": "@article{CodeXGLUE,\ntitle={CodeXGLUE: An Open Challenge for Code Intelligence},\njournal={arXiv},\nyear={2020},\n}\n@article{feng2020codebert,\ntitle={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},\nauthor={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},\njournal={arXiv preprint arXiv:2002.08155},\nyear={2020}\n}\n@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/Code-Code/ClozeTesting-maxmin", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "idx": {"dtype": "string", "id": null, "_type": "Value"}, "nl_tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pl_tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "code_x_glue_cc_cloze_testing_maxmin", "config_name": "javascript", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 594347, "num_examples": 272, "dataset_name": "code_x_glue_cc_cloze_testing_maxmin"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/ClozeTesting-maxmin/data/cloze-maxmin/javascript/clozeTest.json": {"num_bytes": 836112, "checksum": "8517b7b0ecfcc59ccb19c3253a907e6b4f65549e4b68a8ac249807e50a001204"}}, "download_size": 836112, "post_processing_size": null, "dataset_size": 594347, "size_in_bytes": 1430459}, "php": {"description": "CodeXGLUE ClozeTesting-maxmin dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin\n\nCloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. 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The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.\nHere we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. 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+        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/ClozeTesting-maxmin/data/cloze-maxmin/go",
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+        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/ClozeTesting-maxmin/data/cloze-maxmin/javascript",
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+        "dir_name": "ClozeTesting-maxmin",
+        "name": "python",
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+        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/ClozeTesting-maxmin/data/cloze-maxmin/python",
+        "sizes": {"train": 1264},
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+        "dir_name": "ClozeTesting-maxmin",
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+        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin",
+        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/ClozeTesting-maxmin/data/cloze-maxmin/ruby",
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+}