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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

.gitattributes ADDED
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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
+ licenses:
9
+ - other-C-UDA
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ go:
14
+ - 10K<n<100K
15
+ java:
16
+ - 10K<n<100K
17
+ javascript:
18
+ - 10K<n<100K
19
+ php:
20
+ - 10K<n<100K
21
+ python:
22
+ - 10K<n<100K
23
+ ruby:
24
+ - 1K<n<10K
25
+ source_datasets:
26
+ - original
27
+ task_categories:
28
+ - sequence-modeling
29
+ task_ids:
30
+ - slot-filling
31
+ ---
32
+ # Dataset Card for "code_x_glue_cc_cloze_testing_all"
33
+
34
+ ## Table of Contents
35
+ - [Dataset Description](#dataset-description)
36
+ - [Dataset Summary](#dataset-summary)
37
+ - [Supported Tasks and Leaderboards](#supported-tasks)
38
+ - [Languages](#languages)
39
+ - [Dataset Structure](#dataset-structure)
40
+ - [Data Instances](#data-instances)
41
+ - [Data Fields](#data-fields)
42
+ - [Data Splits](#data-splits-sample-size)
43
+ - [Dataset Creation](#dataset-creation)
44
+ - [Curation Rationale](#curation-rationale)
45
+ - [Source Data](#source-data)
46
+ - [Annotations](#annotations)
47
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
48
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
49
+ - [Social Impact of Dataset](#social-impact-of-dataset)
50
+ - [Discussion of Biases](#discussion-of-biases)
51
+ - [Other Known Limitations](#other-known-limitations)
52
+ - [Additional Information](#additional-information)
53
+ - [Dataset Curators](#dataset-curators)
54
+ - [Licensing Information](#licensing-information)
55
+ - [Citation Information](#citation-information)
56
+ - [Contributions](#contributions)
57
+
58
+ ## Dataset Description
59
+
60
+ - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-all
61
+
62
+ ### Dataset Summary
63
+
64
+ CodeXGLUE ClozeTesting-all dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-all
65
+
66
+ 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.
67
+ 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.
68
+ 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.
69
+
70
+ ### Supported Tasks and Leaderboards
71
+
72
+ - `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.
73
+
74
+ ### Languages
75
+
76
+ - Go **programming** language
77
+ - Java **programming** language
78
+ - Javascript **programming** language
79
+ - PHP **programming** language
80
+ - Python **programming** language
81
+ - Ruby **programming** language
82
+
83
+ ## Dataset Structure
84
+
85
+ ### Data Instances
86
+
87
+ #### go
88
+
89
+ An example of 'train' looks as follows.
90
+ ```
91
+ {
92
+ "id": 0,
93
+ "idx": "all-1",
94
+ "nl_tokens": ["MarshalJSON", "supports", "json", ".", "Marshaler", "interface"],
95
+ "pl_tokens": ["func", "(", "v", "ContextRealtimeData", ")", "MarshalJSON", "(", ")", "(", "[", "]", "byte", ",", "error", ")", "{", "w", ":=", "jwriter", ".", "<mask>", "{", "}", "\n", "easyjsonC5a4559bEncodeGithubComChromedpCdprotoWebaudio7", "(", "&", "w", ",", "v", ")", "\n", "return", "w", ".", "Buffer", ".", "BuildBytes", "(", ")", ",", "w", ".", "Error", "\n", "}"]
96
+ }
97
+ ```
98
+
99
+ #### java
100
+
101
+ An example of 'train' looks as follows.
102
+ ```
103
+ {
104
+ "id": 0,
105
+ "idx": "all-1",
106
+ "nl_tokens": ["/", "*", "(", "non", "-", "Javadoc", ")"],
107
+ "pl_tokens": ["@", "Override", "public", "int", "peekBit", "(", ")", "throws", "AACException", "{", "int", "ret", ";", "if", "(", "bitsCached", ">", "0", ")", "{", "ret", "=", "(", "cache", ">>", "(", "bitsCached", "-", "1", ")", ")", "&", "1", ";", "}", "else", "{", "final", "int", "word", "=", "readCache", "(", "true", ")", ";", "ret", "=", "(", "<mask>", ">>", "WORD_BITS", "-", "1", ")", "&", "1", ";", "}", "return", "ret", ";", "}"]
108
+ }
109
+ ```
110
+
111
+ #### javascript
112
+
113
+ An example of 'train' looks as follows.
114
+ ```
115
+ {
116
+ "id": 0,
117
+ "idx": "all-1",
118
+ "nl_tokens": ["Cast", "query", "params", "according", "to", "type"],
119
+ "pl_tokens": ["function", "castQueryParams", "(", "relId", ",", "data", ",", "{", "relationships", "}", ")", "{", "const", "relationship", "=", "relationships", "[", "relId", "]", "if", "(", "!", "relationship", ".", "query", ")", "{", "return", "{", "}", "}", "return", "Object", ".", "keys", "(", "relationship", ".", "query", ")", ".", "reduce", "(", "(", "params", ",", "<mask>", ")", "=>", "{", "const", "value", "=", "getField", "(", "data", ",", "relationship", ".", "query", "[", "key", "]", ")", "if", "(", "value", "===", "undefined", ")", "{", "throw", "new", "TypeError", "(", "'Missing value for query param'", ")", "}", "return", "{", "...", "params", ",", "[", "key", "]", ":", "value", "}", "}", ",", "{", "}", ")", "}"]
120
+ }
121
+ ```
122
+
123
+ #### php
124
+
125
+ An example of 'train' looks as follows.
126
+ ```
127
+ {
128
+ "id": 0,
129
+ "idx": "all-1",
130
+ "nl_tokens": ["Get", "choices", "."],
131
+ "pl_tokens": ["protected", "<mask>", "getChoices", "(", "FormFieldTranslation", "$", "translation", ")", "{", "$", "choices", "=", "preg_split", "(", "'/\\r\\n|\\r|\\n/'", ",", "$", "translation", "->", "getOption", "(", "'choices'", ")", ",", "-", "1", ",", "PREG_SPLIT_NO_EMPTY", ")", ";", "return", "array_combine", "(", "$", "choices", ",", "$", "choices", ")", ";", "}"]
132
+ }
133
+ ```
134
+
135
+ #### python
136
+
137
+ An example of 'train' looks as follows.
138
+ ```
139
+ {
140
+ "id": 0,
141
+ "idx": "all-1",
142
+ "nl_tokens": ["Post", "a", "review"],
143
+ "pl_tokens": ["def", "post_review", "(", "session", ",", "review", ")", ":", "# POST /api/projects/0.1/reviews/", "<mask>", "=", "make_post_request", "(", "session", ",", "'reviews'", ",", "json_data", "=", "review", ")", "json_data", "=", "response", ".", "json", "(", ")", "if", "response", ".", "status_code", "==", "200", ":", "return", "json_data", "[", "'status'", "]", "else", ":", "raise", "ReviewNotPostedException", "(", "message", "=", "json_data", "[", "'message'", "]", ",", "error_code", "=", "json_data", "[", "'error_code'", "]", ",", "request_id", "=", "json_data", "[", "'request_id'", "]", ")"]
144
+ }
145
+ ```
146
+
147
+ #### ruby
148
+
149
+ An example of 'train' looks as follows.
150
+ ```
151
+ {
152
+ "id": 0,
153
+ "idx": "all-1",
154
+ "nl_tokens": ["By", "default", "taskers", "don", "t", "see", "the", "flor", "variables", "in", "the", "execution", ".", "If", "include_vars", "or", "exclude_vars", "is", "present", "in", "the", "configuration", "of", "the", "tasker", "some", "or", "all", "of", "the", "variables", "are", "passed", "."],
155
+ "pl_tokens": ["def", "gather_vars", "(", "executor", ",", "tconf", ",", "message", ")", "# try to return before a potentially costly call to executor.vars(nid)", "return", "nil", "if", "(", "tconf", ".", "keys", "&", "%w[", "include_vars", "exclude_vars", "]", ")", ".", "empty?", "# default behaviour, don't pass variables to taskers", "iv", "=", "expand_filter", "(", "tconf", "[", "'include_vars'", "]", ")", "return", "nil", "if", "iv", "==", "false", "ev", "=", "expand_filter", "(", "tconf", "[", "'exclude_vars'", "]", ")", "return", "{", "}", "if", "ev", "==", "true", "vars", "=", "executor", ".", "vars", "(", "message", "[", "'nid'", "]", ")", "return", "vars", "if", "iv", "==", "true", "vars", "=", "vars", ".", "select", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "iv", ")", "}", "if", "<mask>", "vars", "=", "vars", ".", "reject", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "ev", ")", "}", "if", "ev", "vars", "end"]
156
+ }
157
+ ```
158
+
159
+ ### Data Fields
160
+
161
+ In the following each data field in go is explained for each config. The data fields are the same among all splits.
162
+
163
+ #### go, java, javascript, php, python, ruby
164
+
165
+ |field name| type | description |
166
+ |----------|----------------|------------------------------|
167
+ |id |int32 | Index of the sample |
168
+ |idx |string | Original index in the dataset|
169
+ |nl_tokens |Sequence[string]| Natural language tokens |
170
+ |pl_tokens |Sequence[string]| Programming language tokens |
171
+
172
+ ### Data Splits
173
+
174
+ | name |train|
175
+ |----------|----:|
176
+ |go |25282|
177
+ |java |40492|
178
+ |javascript|13837|
179
+ |php |51930|
180
+ |python |40137|
181
+ |ruby | 4437|
182
+
183
+ ## Dataset Creation
184
+
185
+ ### Curation Rationale
186
+
187
+ [More Information Needed]
188
+
189
+ ### Source Data
190
+
191
+ #### Initial Data Collection and Normalization
192
+
193
+ Data from CodeSearchNet Challenge dataset.
194
+ [More Information Needed]
195
+
196
+ #### Who are the source language producers?
197
+
198
+ Software Engineering developers.
199
+
200
+ ### Annotations
201
+
202
+ #### Annotation process
203
+
204
+ [More Information Needed]
205
+
206
+ #### Who are the annotators?
207
+
208
+ [More Information Needed]
209
+
210
+ ### Personal and Sensitive Information
211
+
212
+ [More Information Needed]
213
+
214
+ ## Considerations for Using the Data
215
+
216
+ ### Social Impact of Dataset
217
+
218
+ [More Information Needed]
219
+
220
+ ### Discussion of Biases
221
+
222
+ [More Information Needed]
223
+
224
+ ### Other Known Limitations
225
+
226
+ [More Information Needed]
227
+
228
+ ## Additional Information
229
+
230
+ ### Dataset Curators
231
+
232
+ https://github.com/microsoft, https://github.com/madlag
233
+
234
+ ### Licensing Information
235
+
236
+ Computational Use of Data Agreement (C-UDA) License.
237
+
238
+ ### Citation Information
239
+
240
+ ```
241
+ @article{CodeXGLUE,
242
+ title={CodeXGLUE: An Open Challenge for Code Intelligence},
243
+ journal={arXiv},
244
+ year={2020},
245
+ }
246
+ @article{feng2020codebert,
247
+ title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
248
+ 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},
249
+ journal={arXiv preprint arXiv:2002.08155},
250
+ year={2020}
251
+ }
252
+ @article{husain2019codesearchnet,
253
+ title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
254
+ author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
255
+ journal={arXiv preprint arXiv:1909.09436},
256
+ year={2019}
257
+ }
258
+ ```
259
+
260
+ ### Contributions
261
+
262
+ Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
code_x_glue_cc_cloze_testing_all.py ADDED
@@ -0,0 +1,83 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ from typing import List
3
+
4
+ import datasets
5
+
6
+ from .common import Child
7
+ from .generated_definitions import DEFINITIONS
8
+
9
+
10
+ _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.
11
+ 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.
12
+ 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."""
13
+
14
+ _CITATION = """@article{CodeXGLUE,
15
+ title={CodeXGLUE: An Open Challenge for Code Intelligence},
16
+ journal={arXiv},
17
+ year={2020},
18
+ }
19
+ @article{feng2020codebert,
20
+ title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
21
+ 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},
22
+ journal={arXiv preprint arXiv:2002.08155},
23
+ year={2020}
24
+ }
25
+ @article{husain2019codesearchnet,
26
+ title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
27
+ author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
28
+ journal={arXiv preprint arXiv:1909.09436},
29
+ year={2019}
30
+ }"""
31
+
32
+
33
+ class CodeXGlueCcClozeTestingImpl(Child):
34
+ _DESCRIPTION = _DESCRIPTION
35
+ _CITATION = _CITATION
36
+
37
+ _FEATURES = {
38
+ "id": datasets.Value("int32"), # Index of the sample
39
+ "idx": datasets.Value("string"), # Original index in the dataset
40
+ "nl_tokens": datasets.features.Sequence(datasets.Value("string")), # Natural language tokens
41
+ "pl_tokens": datasets.features.Sequence(datasets.Value("string")), # Programming language tokens
42
+ }
43
+
44
+ def generate_urls(self, split_name):
45
+ yield "data", "clozeTest.json"
46
+
47
+ def _generate_examples(self, split_name, file_paths):
48
+ with open(file_paths["data"], encoding="utf-8") as f:
49
+ j = json.load(f)
50
+ index = 0
51
+ for entry in j:
52
+ yield index, dict(
53
+ id=index, idx=entry["idx"], nl_tokens=entry["nl_tokens"], pl_tokens=entry["pl_tokens"]
54
+ )
55
+ index += 1
56
+
57
+
58
+ CLASS_MAPPING = {
59
+ "CodeXGlueCcClozeTestingAll": CodeXGlueCcClozeTestingImpl,
60
+ }
61
+
62
+
63
+ class CodeXGlueCcClozeTestingAll(datasets.GeneratorBasedBuilder):
64
+ BUILDER_CONFIG_CLASS = datasets.BuilderConfig
65
+ BUILDER_CONFIGS = [
66
+ datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
67
+ ]
68
+
69
+ def _info(self):
70
+ name = self.config.name
71
+ info = DEFINITIONS[name]
72
+ if info["class_name"] in CLASS_MAPPING:
73
+ self.child = CLASS_MAPPING[info["class_name"]](info)
74
+ else:
75
+ raise RuntimeError(f"Unknown python class for dataset configuration {name}")
76
+ ret = self.child._info()
77
+ return ret
78
+
79
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
80
+ return self.child._split_generators(dl_manager=dl_manager)
81
+
82
+ def _generate_examples(self, split_name, file_paths):
83
+ 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
+ }
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