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Update files from the datasets library (from 1.8.0)

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

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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1
+ ---
2
+ annotations_creators:
3
+ - found
4
+ language_creators:
5
+ - found
6
+ languages:
7
+ - code
8
+ - en
9
+ licenses:
10
+ - other-C-UDA
11
+ multilinguality:
12
+ - other-programming-languages
13
+ size_categories:
14
+ go:
15
+ - 100K<n<1M
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+ java:
17
+ - 100K<n<1M
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+ javascript:
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+ - 10K<n<100K
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+ php:
21
+ - 100K<n<1M
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+ python:
23
+ - 100K<n<1M
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+ ruby:
25
+ - 10K<n<100K
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+ source_datasets:
27
+ - original
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+ task_categories:
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+ - conditional-text-generation
30
+ task_ids:
31
+ - machine-translation
32
+ ---
33
+ # Dataset Card for "code_x_glue_ct_code_to_text"
34
+
35
+ ## Table of Contents
36
+ - [Dataset Description](#dataset-description)
37
+ - [Dataset Summary](#dataset-summary)
38
+ - [Supported Tasks and Leaderboards](#supported-tasks)
39
+ - [Languages](#languages)
40
+ - [Dataset Structure](#dataset-structure)
41
+ - [Data Instances](#data-instances)
42
+ - [Data Fields](#data-fields)
43
+ - [Data Splits](#data-splits-sample-size)
44
+ - [Dataset Creation](#dataset-creation)
45
+ - [Curation Rationale](#curation-rationale)
46
+ - [Source Data](#source-data)
47
+ - [Annotations](#annotations)
48
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
49
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
50
+ - [Social Impact of Dataset](#social-impact-of-dataset)
51
+ - [Discussion of Biases](#discussion-of-biases)
52
+ - [Other Known Limitations](#other-known-limitations)
53
+ - [Additional Information](#additional-information)
54
+ - [Dataset Curators](#dataset-curators)
55
+ - [Licensing Information](#licensing-information)
56
+ - [Citation Information](#citation-information)
57
+ - [Contributions](#contributions)
58
+
59
+ ## Dataset Description
60
+
61
+ - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text
62
+
63
+ ### Dataset Summary
64
+
65
+ CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text
66
+
67
+ The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
68
+ - Remove examples that codes cannot be parsed into an abstract syntax tree.
69
+ - Remove examples that #tokens of documents is < 3 or >256
70
+ - Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
71
+ - Remove examples that documents are not English.
72
+
73
+ ### Supported Tasks and Leaderboards
74
+
75
+ - `machine-translation`: The dataset can be used to train a model for automatically generating **English** docstrings for code.
76
+
77
+ ### Languages
78
+
79
+ - Go **programming** language
80
+ - Java **programming** language
81
+ - Javascript **programming** language
82
+ - PHP **programming** language
83
+ - Python **programming** language
84
+ - Ruby **programming** language
85
+ - English **natural** language
86
+
87
+ ## Dataset Structure
88
+
89
+ ### Data Instances
90
+
91
+ #### go
92
+
93
+ An example of 'test' looks as follows.
94
+ ```
95
+ {
96
+ "code": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}",
97
+ "code_tokens": ["func", "NewSTM", "(", "c", "*", "v3", ".", "Client", ",", "apply", "func", "(", "STM", ")", "error", ",", "so", "...", "stmOption", ")", "(", "*", "v3", ".", "TxnResponse", ",", "error", ")", "{", "opts", ":=", "&", "stmOptions", "{", "ctx", ":", "c", ".", "Ctx", "(", ")", "}", "\n", "for", "_", ",", "f", ":=", "range", "so", "{", "f", "(", "opts", ")", "\n", "}", "\n", "if", "len", "(", "opts", ".", "prefetch", ")", "!=", "0", "{", "f", ":=", "apply", "\n", "apply", "=", "func", "(", "s", "STM", ")", "error", "{", "s", ".", "Get", "(", "opts", ".", "prefetch", "...", ")", "\n", "return", "f", "(", "s", ")", "\n", "}", "\n", "}", "\n", "return", "runSTM", "(", "mkSTM", "(", "c", ",", "opts", ")", ",", "apply", ")", "\n", "}"],
98
+ "docstring": "// NewSTM initiates a new STM instance, using serializable snapshot isolation by default.",
99
+ "docstring_tokens": ["NewSTM", "initiates", "a", "new", "STM", "instance", "using", "serializable", "snapshot", "isolation", "by", "default", "."],
100
+ "func_name": "NewSTM",
101
+ "id": 0,
102
+ "language": "go",
103
+ "original_string": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}",
104
+ "path": "clientv3/concurrency/stm.go",
105
+ "repo": "etcd-io/etcd",
106
+ "sha": "616592d9ba993e3fe9798eef581316016df98906",
107
+ "url": "https://github.com/etcd-io/etcd/blob/616592d9ba993e3fe9798eef581316016df98906/clientv3/concurrency/stm.go#L89-L102"
108
+ }
109
+ ```
110
+
111
+ #### java
112
+
113
+ An example of 'test' looks as follows.
114
+ ```
115
+ {
116
+ "code": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }",
117
+ "code_tokens": ["protected", "final", "void", "fastPathOrderedEmit", "(", "U", "value", ",", "boolean", "delayError", ",", "Disposable", "disposable", ")", "{", "final", "Observer", "<", "?", "super", "V", ">", "observer", "=", "downstream", ";", "final", "SimplePlainQueue", "<", "U", ">", "q", "=", "queue", ";", "if", "(", "wip", ".", "get", "(", ")", "==", "0", "&&", "wip", ".", "compareAndSet", "(", "0", ",", "1", ")", ")", "{", "if", "(", "q", ".", "isEmpty", "(", ")", ")", "{", "accept", "(", "observer", ",", "value", ")", ";", "if", "(", "leave", "(", "-", "1", ")", "==", "0", ")", "{", "return", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "if", "(", "!", "enter", "(", ")", ")", "{", "return", ";", "}", "}", "QueueDrainHelper", ".", "drainLoop", "(", "q", ",", "observer", ",", "delayError", ",", "disposable", ",", "this", ")", ";", "}"],
118
+ "docstring": "Makes sure the fast-path emits in order.\n@param value the value to emit or queue up\n@param delayError if true, errors are delayed until the source has terminated\n@param disposable the resource to dispose if the drain terminates",
119
+ "docstring_tokens": ["Makes", "sure", "the", "fast", "-", "path", "emits", "in", "order", "."],
120
+ "func_name": "QueueDrainObserver.fastPathOrderedEmit",
121
+ "id": 0,
122
+ "language": "java",
123
+ "original_string": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }",
124
+ "path": "src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java",
125
+ "repo": "ReactiveX/RxJava",
126
+ "sha": "ac84182aa2bd866b53e01c8e3fe99683b882c60e",
127
+ "url": "https://github.com/ReactiveX/RxJava/blob/ac84182aa2bd866b53e01c8e3fe99683b882c60e/src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java#L88-L108"
128
+ }
129
+ ```
130
+
131
+ #### javascript
132
+
133
+ An example of 'test' looks as follows.
134
+ ```
135
+ {
136
+ "code": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}",
137
+ "code_tokens": ["function", "createInstance", "(", "defaultConfig", ")", "{", "var", "context", "=", "new", "Axios", "(", "defaultConfig", ")", ";", "var", "instance", "=", "bind", "(", "Axios", ".", "prototype", ".", "request", ",", "context", ")", ";", "// Copy axios.prototype to instance", "utils", ".", "extend", "(", "instance", ",", "Axios", ".", "prototype", ",", "context", ")", ";", "// Copy context to instance", "utils", ".", "extend", "(", "instance", ",", "context", ")", ";", "return", "instance", ";", "}"],
138
+ "docstring": "Create an instance of Axios\n\n@param {Object} defaultConfig The default config for the instance\n@return {Axios} A new instance of Axios",
139
+ "docstring_tokens": ["Create", "an", "instance", "of", "Axios"],
140
+ "func_name": "createInstance",
141
+ "id": 0,
142
+ "language": "javascript",
143
+ "original_string": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}",
144
+ "path": "lib/axios.js",
145
+ "repo": "axios/axios",
146
+ "sha": "92d231387fe2092f8736bc1746d4caa766b675f5",
147
+ "url": "https://github.com/axios/axios/blob/92d231387fe2092f8736bc1746d4caa766b675f5/lib/axios.js#L15-L26"
148
+ }
149
+ ```
150
+
151
+ #### php
152
+
153
+ An example of 'train' looks as follows.
154
+ ```
155
+ {
156
+ "code": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }",
157
+ "code_tokens": ["public", "static", "function", "build", "(", "$", "serviceAddress", ",", "$", "restConfigPath", ",", "array", "$", "config", "=", "[", "]", ")", "{", "$", "config", "+=", "[", "'httpHandler'", "=>", "null", ",", "]", ";", "list", "(", "$", "baseUri", ",", "$", "port", ")", "=", "self", "::", "normalizeServiceAddress", "(", "$", "serviceAddress", ")", ";", "$", "requestBuilder", "=", "new", "RequestBuilder", "(", "\"$baseUri:$port\"", ",", "$", "restConfigPath", ")", ";", "$", "httpHandler", "=", "$", "config", "[", "'httpHandler'", "]", "?", ":", "self", "::", "buildHttpHandlerAsync", "(", ")", ";", "return", "new", "RestTransport", "(", "$", "requestBuilder", ",", "$", "httpHandler", ")", ";", "}"],
158
+ "docstring": "Builds a RestTransport.\n\n@param string $serviceAddress\nThe address of the API remote host, for example \"example.googleapis.com\".\n@param string $restConfigPath\nPath to rest config file.\n@param array $config {\nConfig options used to construct the gRPC transport.\n\n@type callable $httpHandler A handler used to deliver PSR-7 requests.\n}\n@return RestTransport\n@throws ValidationException",
159
+ "docstring_tokens": ["Builds", "a", "RestTransport", "."],
160
+ "func_name": "RestTransport.build",
161
+ "id": 0,
162
+ "language": "php",
163
+ "original_string": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }",
164
+ "path": "src/Transport/RestTransport.php",
165
+ "repo": "googleapis/gax-php",
166
+ "sha": "48387fb818c6882296710a2302a0aa973b99afb2",
167
+ "url": "https://github.com/googleapis/gax-php/blob/48387fb818c6882296710a2302a0aa973b99afb2/src/Transport/RestTransport.php#L85-L94"
168
+ }
169
+ ```
170
+
171
+ #### python
172
+
173
+ An example of 'validation' looks as follows.
174
+ ```
175
+ {
176
+ "code": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)",
177
+ "code_tokens": ["def", "save_act", "(", "self", ",", "path", "=", "None", ")", ":", "if", "path", "is", "None", ":", "path", "=", "os", ".", "path", ".", "join", "(", "logger", ".", "get_dir", "(", ")", ",", "\"model.pkl\"", ")", "with", "tempfile", ".", "TemporaryDirectory", "(", ")", "as", "td", ":", "save_variables", "(", "os", ".", "path", ".", "join", "(", "td", ",", "\"model\"", ")", ")", "arc_name", "=", "os", ".", "path", ".", "join", "(", "td", ",", "\"packed.zip\"", ")", "with", "zipfile", ".", "ZipFile", "(", "arc_name", ",", "'w'", ")", "as", "zipf", ":", "for", "root", ",", "dirs", ",", "files", "in", "os", ".", "walk", "(", "td", ")", ":", "for", "fname", "in", "files", ":", "file_path", "=", "os", ".", "path", ".", "join", "(", "root", ",", "fname", ")", "if", "file_path", "!=", "arc_name", ":", "zipf", ".", "write", "(", "file_path", ",", "os", ".", "path", ".", "relpath", "(", "file_path", ",", "td", ")", ")", "with", "open", "(", "arc_name", ",", "\"rb\"", ")", "as", "f", ":", "model_data", "=", "f", ".", "read", "(", ")", "with", "open", "(", "path", ",", "\"wb\"", ")", "as", "f", ":", "cloudpickle", ".", "dump", "(", "(", "model_data", ",", "self", ".", "_act_params", ")", ",", "f", ")"],
178
+ "docstring": "Save model to a pickle located at `path`",
179
+ "docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"],
180
+ "func_name": "ActWrapper.save_act",
181
+ "id": 0,
182
+ "language": "python",
183
+ "original_string": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)",
184
+ "path": "baselines/deepq/deepq.py",
185
+ "repo": "openai/baselines",
186
+ "sha": "3301089b48c42b87b396e246ea3f56fa4bfc9678",
187
+ "url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72"
188
+ }
189
+ ```
190
+
191
+ #### ruby
192
+
193
+ An example of 'train' looks as follows.
194
+ ```
195
+ {
196
+ "code": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end",
197
+ "code_tokens": ["def", "render_body", "(", "context", ",", "options", ")", "if", "options", ".", "key?", "(", ":partial", ")", "[", "render_partial", "(", "context", ",", "options", ")", "]", "else", "StreamingTemplateRenderer", ".", "new", "(", "@lookup_context", ")", ".", "render", "(", "context", ",", "options", ")", "end", "end"],
198
+ "docstring": "Render but returns a valid Rack body. If fibers are defined, we return\n a streaming body that renders the template piece by piece.\n\n Note that partials are not supported to be rendered with streaming,\n so in such cases, we just wrap them in an array.",
199
+ "docstring_tokens": ["Render", "but", "returns", "a", "valid", "Rack", "body", ".", "If", "fibers", "are", "defined", "we", "return", "a", "streaming", "body", "that", "renders", "the", "template", "piece", "by", "piece", "."],
200
+ "func_name": "ActionView.Renderer.render_body",
201
+ "id": 0,
202
+ "language": "ruby",
203
+ "original_string": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end",
204
+ "path": "actionview/lib/action_view/renderer/renderer.rb",
205
+ "repo": "rails/rails",
206
+ "sha": "85a8bc644be69908f05740a5886ec19cd3679df5",
207
+ "url": "https://github.com/rails/rails/blob/85a8bc644be69908f05740a5886ec19cd3679df5/actionview/lib/action_view/renderer/renderer.rb#L38-L44"
208
+ }
209
+ ```
210
+
211
+ ### Data Fields
212
+
213
+ In the following each data field in go is explained for each config. The data fields are the same among all splits.
214
+
215
+ #### go, java, javascript, php, python, ruby
216
+
217
+ | field name | type | description |
218
+ |----------------|----------------|-----------------------------------------------------------------------------------|
219
+ |id |int32 | Index of the sample |
220
+ |repo |string | repo: the owner/repo |
221
+ |path |string | path: the full path to the original file |
222
+ |func_name |string | func_name: the function or method name |
223
+ |original_string |string | original_string: the raw string before tokenization or parsing |
224
+ |language |string | language: the programming language name |
225
+ |code |string | code/function: the part of the original_string that is code |
226
+ |code_tokens |Sequence[string]| code_tokens/function_tokens: tokenized version of code |
227
+ |docstring |string | docstring: the top-level comment or docstring, if it exists in the original string|
228
+ |docstring_tokens|Sequence[string]| docstring_tokens: tokenized version of docstring |
229
+ |sha |string | sha of the file |
230
+ |url |string | url of the file |
231
+
232
+ ### Data Splits
233
+
234
+ | name |train |validation|test |
235
+ |----------|-----:|---------:|----:|
236
+ |go |167288| 7325| 8122|
237
+ |java |164923| 5183|10955|
238
+ |javascript| 58025| 3885| 3291|
239
+ |php |241241| 12982|14014|
240
+ |python |251820| 13914|14918|
241
+ |ruby | 24927| 1400| 1261|
242
+
243
+ ## Dataset Creation
244
+
245
+ ### Curation Rationale
246
+
247
+ [More Information Needed]
248
+
249
+ ### Source Data
250
+
251
+ #### Initial Data Collection and Normalization
252
+
253
+ Data from CodeSearchNet Challenge dataset.
254
+ [More Information Needed]
255
+
256
+ #### Who are the source language producers?
257
+
258
+ Software Engineering developers.
259
+
260
+ ### Annotations
261
+
262
+ #### Annotation process
263
+
264
+ [More Information Needed]
265
+
266
+ #### Who are the annotators?
267
+
268
+ [More Information Needed]
269
+
270
+ ### Personal and Sensitive Information
271
+
272
+ [More Information Needed]
273
+
274
+ ## Considerations for Using the Data
275
+
276
+ ### Social Impact of Dataset
277
+
278
+ [More Information Needed]
279
+
280
+ ### Discussion of Biases
281
+
282
+ [More Information Needed]
283
+
284
+ ### Other Known Limitations
285
+
286
+ [More Information Needed]
287
+
288
+ ## Additional Information
289
+
290
+ ### Dataset Curators
291
+
292
+ https://github.com/microsoft, https://github.com/madlag
293
+
294
+ ### Licensing Information
295
+
296
+ Computational Use of Data Agreement (C-UDA) License.
297
+
298
+ ### Citation Information
299
+
300
+ ```
301
+ @article{husain2019codesearchnet,
302
+ title={Codesearchnet challenge: Evaluating the state of semantic code search},
303
+ author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
304
+ journal={arXiv preprint arXiv:1909.09436},
305
+ year={2019}
306
+ }
307
+ ```
308
+
309
+ ### Contributions
310
+
311
+ Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
code_x_glue_ct_code_to_text.py ADDED
@@ -0,0 +1,155 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import os
3
+ import os.path
4
+ from typing import List
5
+
6
+ import datasets
7
+
8
+ from .common import TrainValidTestChild
9
+ from .generated_definitions import DEFINITIONS
10
+
11
+
12
+ _DESCRIPTION = """The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
13
+ - Remove examples that codes cannot be parsed into an abstract syntax tree.
14
+ - Remove examples that #tokens of documents is < 3 or >256
15
+ - Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
16
+ - Remove examples that documents are not English.
17
+ """
18
+ _CITATION = """@article{husain2019codesearchnet,
19
+ title={Codesearchnet challenge: Evaluating the state of semantic code search},
20
+ author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
21
+ journal={arXiv preprint arXiv:1909.09436},
22
+ year={2019}
23
+ }"""
24
+
25
+
26
+ class CodeXGlueCtCodeToTextBaseImpl(TrainValidTestChild):
27
+ _DESCRIPTION = _DESCRIPTION
28
+ _CITATION = _CITATION
29
+
30
+ # For each file, each line in the uncompressed file represents one function.
31
+ _FEATURES = {
32
+ "id": datasets.Value("int32"), # Index of the sample
33
+ "repo": datasets.Value("string"), # repo: the owner/repo
34
+ "path": datasets.Value("string"), # path: the full path to the original file
35
+ "func_name": datasets.Value("string"), # func_name: the function or method name
36
+ "original_string": datasets.Value("string"), # original_string: the raw string before tokenization or parsing
37
+ "language": datasets.Value("string"), # language: the programming language name
38
+ "code": datasets.Value("string"), # code/function: the part of the original_string that is code
39
+ "code_tokens": datasets.features.Sequence(
40
+ datasets.Value("string")
41
+ ), # code_tokens/function_tokens: tokenized version of code
42
+ "docstring": datasets.Value(
43
+ "string"
44
+ ), # docstring: the top-level comment or docstring, if it exists in the original string
45
+ "docstring_tokens": datasets.features.Sequence(
46
+ datasets.Value("string")
47
+ ), # docstring_tokens: tokenized version of docstring
48
+ "sha": datasets.Value("string"), # sha of the file
49
+ "url": datasets.Value("string"), # url of the file
50
+ }
51
+
52
+ _SUPERVISED_KEYS = ["docstring", "docstring_tokens"]
53
+
54
+ def generate_urls(self, split_name, language):
55
+ yield "language", f"https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/{language}.zip"
56
+ yield "dataset", "dataset.zip"
57
+
58
+ def get_data_files(self, split_name, file_paths, language):
59
+ language_specific_path = file_paths["language"]
60
+ final_path = os.path.join(language_specific_path, language, "final")
61
+ # Make some cleanup to save space
62
+ for path in os.listdir(final_path):
63
+ if path.endswith(".pkl"):
64
+ os.unlink(path)
65
+
66
+ data_files = []
67
+ for root, dirs, files in os.walk(final_path):
68
+ for file in files:
69
+ temp = os.path.join(root, file)
70
+ if ".jsonl" in temp:
71
+ if split_name in temp:
72
+ data_files.append(temp)
73
+ return data_files
74
+
75
+ def post_process(self, split_name, language, js):
76
+ return js
77
+
78
+ def _generate_examples(self, split_name, file_paths, language):
79
+ import gzip
80
+
81
+ data_set_path = file_paths["dataset"]
82
+
83
+ data_files = self.get_data_files(split_name, file_paths, language)
84
+
85
+ urls = {}
86
+ f1_path_parts = [data_set_path, "dataset", language, f"{split_name}.txt"]
87
+ if self.SINGLE_LANGUAGE:
88
+ del f1_path_parts[2]
89
+
90
+ f1_path = os.path.join(*f1_path_parts)
91
+ with open(f1_path, encoding="utf-8") as f1:
92
+ for line in f1:
93
+ line = line.strip()
94
+ urls[line] = True
95
+
96
+ idx = 0
97
+ for file in data_files:
98
+ if ".gz" in file:
99
+ f = gzip.open(file)
100
+ else:
101
+ f = open(file, encoding="utf-8")
102
+
103
+ for line in f:
104
+ line = line.strip()
105
+ js = json.loads(line)
106
+ if js["url"] in urls:
107
+ js["id"] = idx
108
+ js = self.post_process(split_name, language, js)
109
+ if "partition" in js:
110
+ del js["partition"]
111
+ yield idx, js
112
+ idx += 1
113
+ f.close()
114
+
115
+
116
+ class CodeXGlueCtCodeToTextImpl(CodeXGlueCtCodeToTextBaseImpl):
117
+ SINGLE_LANGUAGE = False
118
+
119
+ def generate_urls(self, split_name):
120
+ language = self.info["parameters"]["language"]
121
+ for e in super().generate_urls(split_name, language):
122
+ yield e
123
+
124
+ def _generate_examples(self, split_name, file_paths):
125
+ language = self.info["parameters"]["language"]
126
+ for e in super()._generate_examples(split_name, file_paths, language):
127
+ yield e
128
+
129
+
130
+ CLASS_MAPPING = {
131
+ "CodeXGlueCtCodeToText": CodeXGlueCtCodeToTextImpl,
132
+ }
133
+
134
+
135
+ class CodeXGlueCtCodeToText(datasets.GeneratorBasedBuilder):
136
+ BUILDER_CONFIG_CLASS = datasets.BuilderConfig
137
+ BUILDER_CONFIGS = [
138
+ datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
139
+ ]
140
+
141
+ def _info(self):
142
+ name = self.config.name
143
+ info = DEFINITIONS[name]
144
+ if info["class_name"] in CLASS_MAPPING:
145
+ self.child = CLASS_MAPPING[info["class_name"]](info)
146
+ else:
147
+ raise RuntimeError(f"Unknown python class for dataset configuration {name}")
148
+ ret = self.child._info()
149
+ return ret
150
+
151
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
152
+ return self.child._split_generators(dl_manager=dl_manager)
153
+
154
+ def _generate_examples(self, split_name, file_paths):
155
+ return self.child._generate_examples(split_name, file_paths)
common.py ADDED
@@ -0,0 +1,75 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List
2
+
3
+ import datasets
4
+
5
+
6
+ # Citation, taken from https://github.com/microsoft/CodeXGLUE
7
+ _DEFAULT_CITATION = """@article{CodeXGLUE,
8
+ title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
9
+ year={2020},}"""
10
+
11
+
12
+ class Child:
13
+ _DESCRIPTION = None
14
+ _FEATURES = None
15
+ _CITATION = None
16
+ SPLITS = {"train": datasets.Split.TRAIN}
17
+ _SUPERVISED_KEYS = None
18
+
19
+ def __init__(self, info):
20
+ self.info = info
21
+
22
+ def homepage(self):
23
+ return self.info["project_url"]
24
+
25
+ def _info(self):
26
+ # This is the description that will appear on the datasets page.
27
+ return datasets.DatasetInfo(
28
+ description=self.info["description"] + "\n\n" + self._DESCRIPTION,
29
+ features=datasets.Features(self._FEATURES),
30
+ homepage=self.homepage(),
31
+ citation=self._CITATION or _DEFAULT_CITATION,
32
+ supervised_keys=self._SUPERVISED_KEYS,
33
+ )
34
+
35
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
36
+ SPLITS = self.SPLITS
37
+ _URL = self.info["raw_url"]
38
+ urls_to_download = {}
39
+ for split in SPLITS:
40
+ if split not in urls_to_download:
41
+ urls_to_download[split] = {}
42
+
43
+ for key, url in self.generate_urls(split):
44
+ if not url.startswith("http"):
45
+ url = _URL + "/" + url
46
+ urls_to_download[split][key] = url
47
+
48
+ downloaded_files = {}
49
+ for k, v in urls_to_download.items():
50
+ downloaded_files[k] = dl_manager.download_and_extract(v)
51
+
52
+ return [
53
+ datasets.SplitGenerator(
54
+ name=SPLITS[k],
55
+ gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
56
+ )
57
+ for k in SPLITS
58
+ ]
59
+
60
+ def check_empty(self, entries):
61
+ all_empty = all([v == "" for v in entries.values()])
62
+ all_non_empty = all([v != "" for v in entries.values()])
63
+
64
+ if not all_non_empty and not all_empty:
65
+ raise RuntimeError("Parallel data files should have the same number of lines.")
66
+
67
+ return all_empty
68
+
69
+
70
+ class TrainValidTestChild(Child):
71
+ SPLITS = {
72
+ "train": datasets.Split.TRAIN,
73
+ "valid": datasets.Split.VALIDATION,
74
+ "test": datasets.Split.TEST,
75
+ }
dataset_infos.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"go": {"description": "CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text\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/Code-Text/code-to-text", "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"}}, "post_processed": null, "supervised_keys": {"input": "docstring", "output": "docstring_tokens"}, "task_templates": null, "builder_name": "code_x_glue_ct_code_to_text", "config_name": "go", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 342244027, "num_examples": 167288, "dataset_name": "code_x_glue_ct_code_to_text"}, "validation": {"name": "validation", "num_bytes": 13721912, "num_examples": 7325, "dataset_name": "code_x_glue_ct_code_to_text"}, "test": {"name": "test", "num_bytes": 16328458, "num_examples": 8122, "dataset_name": "code_x_glue_ct_code_to_text"}}, "download_checksums": {"https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/go.zip": {"num_bytes": 487525935, "checksum": "15d23f01dc2796447e1736263e6830079289d5ef41f09988011afdcf8da6b6e5"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Text/code-to-text/dataset.zip": {"num_bytes": 12396864, "checksum": "31ec750805302ecd71b278a492d23d2ac916269f7ec645bba4f23b6f7c4bf217"}}, "download_size": 499922799, "post_processing_size": null, "dataset_size": 372294397, "size_in_bytes": 872217196}, "java": {"description": "CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text\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/Code-Text/code-to-text", "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"}}, "post_processed": null, "supervised_keys": {"input": "docstring", "output": "docstring_tokens"}, "task_templates": null, "builder_name": "code_x_glue_ct_code_to_text", "config_name": "java", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 452554719, "num_examples": 164923, "dataset_name": "code_x_glue_ct_code_to_text"}, "validation": {"name": "validation", "num_bytes": 13366396, "num_examples": 5183, "dataset_name": "code_x_glue_ct_code_to_text"}, "test": {"name": "test", "num_bytes": 29080857, "num_examples": 10955, "dataset_name": "code_x_glue_ct_code_to_text"}}, "download_checksums": {"https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/java.zip": {"num_bytes": 1060569153, "checksum": "05f9204b1808413fab30f0e69229e298f6de4ad468279d53a2aa5797e3a78c17"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Text/code-to-text/dataset.zip": {"num_bytes": 12396864, "checksum": "31ec750805302ecd71b278a492d23d2ac916269f7ec645bba4f23b6f7c4bf217"}}, "download_size": 1072966017, "post_processing_size": null, "dataset_size": 495001972, "size_in_bytes": 1567967989}, "javascript": {"description": "CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text\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/Code-Text/code-to-text", "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"}}, "post_processed": null, "supervised_keys": {"input": "docstring", "output": "docstring_tokens"}, "task_templates": null, "builder_name": "code_x_glue_ct_code_to_text", "config_name": "javascript", "version": {"version_str": "0.0.0", 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