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  ---
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622
 
mathematics_dataset/mathematics_dataset/ds.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:a3e513877e85246627c135bcc6d0aa35ee84132cde51b3ad6f32b13517057cc4
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portuguese-math-dataset.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ # Lint as: python3
17
+ """Mathematics database."""
18
+
19
+ import datasets
20
+ import os
21
+
22
+
23
+ logger = datasets.logging.get_logger(__name__)
24
+
25
+
26
+ _CITATION = """
27
+ @article{2019arXiv,
28
+ author = {Saxton, Grefenstette, Hill, Kohli},
29
+ title = {Analysing Mathematical Reasoning Abilities of Neural Models},
30
+ year = {2019},
31
+ journal = {arXiv:1904.01557}
32
+ }
33
+ """
34
+
35
+ _DESCRIPTION = """
36
+ Mathematics database.
37
+
38
+ This dataset code generates mathematical question and answer pairs,
39
+ from a range of question types at roughly school-level difficulty.
40
+ This is designed to test the mathematical learning and algebraic
41
+ reasoning skills of learning models.
42
+
43
+ Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
44
+ (Saxton, Grefenstette, Hill, Kohli).
45
+
46
+ Example usage:
47
+ train_examples, val_examples = datasets.load_dataset(
48
+ 'math_dataset/arithmetic__mul',
49
+ split=['train', 'test'],
50
+ as_supervised=True)
51
+ """
52
+
53
+ _DATA_URL = os.path.join('mathematics_dataset', 'mathematics_dataset', 'ds.zip')
54
+
55
+ _TRAIN_CATEGORY = [
56
+ "train-easy",
57
+ "train-medium",
58
+ "train-hard",
59
+ ]
60
+
61
+ _INTERPOLATE_CATEGORY = [
62
+ "interpolate",
63
+ ]
64
+
65
+ _MODULES = [
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+ # extrapolate
67
+ "measurement__conversion",
68
+ # interpolate
69
+ "algebra__linear_1d",
70
+ "algebra__linear_1d_composed",
71
+ "algebra__linear_2d",
72
+ "algebra__linear_2d_composed",
73
+ "algebra__polynomial_roots",
74
+ "algebra__polynomial_roots_composed",
75
+ "algebra__sequence_next_term",
76
+ "algebra__sequence_nth_term",
77
+ "arithmetic__add_or_sub",
78
+ "arithmetic__add_or_sub_in_base",
79
+ "arithmetic__add_sub_multiple",
80
+ "arithmetic__div",
81
+ "arithmetic__mixed",
82
+ "arithmetic__mul",
83
+ "arithmetic__mul_div_multiple",
84
+ "arithmetic__nearest_integer_root",
85
+ "arithmetic__simplify_surd",
86
+ "calculus__differentiate",
87
+ "calculus__differentiate_composed",
88
+ "comparison__closest",
89
+ "comparison__closest_composed",
90
+ "comparison__kth_biggest",
91
+ "comparison__kth_biggest_composed",
92
+ "comparison__pair",
93
+ "comparison__pair_composed",
94
+ "comparison__sort",
95
+ "comparison__sort_composed",
96
+ "measurement__conversion",
97
+ "measurement__time",
98
+ "numbers__base_conversion",
99
+ "numbers__div_remainder",
100
+ "numbers__div_remainder_composed",
101
+ "numbers__gcd",
102
+ "numbers__gcd_composed",
103
+ "numbers__is_factor",
104
+ "numbers__is_factor_composed",
105
+ "numbers__is_prime",
106
+ "numbers__is_prime_composed",
107
+ "numbers__lcm",
108
+ "numbers__lcm_composed",
109
+ "numbers__list_prime_factors",
110
+ "numbers__list_prime_factors_composed",
111
+ "numbers__place_value",
112
+ "numbers__place_value_composed",
113
+ "numbers__round_number",
114
+ "numbers__round_number_composed",
115
+ "polynomials__add",
116
+ "polynomials__coefficient_named",
117
+ "polynomials__collect",
118
+ "polynomials__compose",
119
+ "polynomials__evaluate",
120
+ "polynomials__evaluate_composed",
121
+ "polynomials__expand",
122
+ "polynomials__simplify_power",
123
+ "probability__swr_p_level_set",
124
+ "probability__swr_p_sequence",
125
+ # train-easy train-medium train-hard
126
+ "algebra__linear_1d",
127
+ "algebra__linear_1d_composed",
128
+ "algebra__linear_2d",
129
+ "algebra__linear_2d_composed",
130
+ "algebra__polynomial_roots",
131
+ "algebra__polynomial_roots_composed",
132
+ "algebra__sequence_next_term",
133
+ "algebra__sequence_nth_term",
134
+ "arithmetic__add_or_sub",
135
+ "arithmetic__add_or_sub_in_base",
136
+ "arithmetic__add_sub_multiple",
137
+ "arithmetic__div",
138
+ "arithmetic__mixed",
139
+ "arithmetic__mul",
140
+ "arithmetic__mul_div_multiple",
141
+ "arithmetic__nearest_integer_root",
142
+ "arithmetic__simplify_surd",
143
+ "calculus__differentiate",
144
+ "calculus__differentiate_composed",
145
+ "comparison__closest",
146
+ "comparison__closest_composed",
147
+ "comparison__kth_biggest",
148
+ "comparison__kth_biggest_composed",
149
+ "comparison__pair",
150
+ "comparison__pair_composed",
151
+ "comparison__sort",
152
+ "comparison__sort_composed",
153
+ "measurement__conversion",
154
+ "measurement__time",
155
+ "numbers__base_conversion",
156
+ "numbers__div_remainder",
157
+ "numbers__div_remainder_composed",
158
+ "numbers__gcd",
159
+ "numbers__gcd_composed",
160
+ "numbers__is_factor",
161
+ "numbers__is_factor_composed",
162
+ "numbers__is_prime",
163
+ "numbers__is_prime_composed",
164
+ "numbers__lcm",
165
+ "numbers__lcm_composed",
166
+ "numbers__list_prime_factors",
167
+ "numbers__list_prime_factors_composed",
168
+ "numbers__place_value",
169
+ "numbers__place_value_composed",
170
+ "numbers__round_number",
171
+ "numbers__round_number_composed",
172
+ "polynomials__add",
173
+ "polynomials__coefficient_named",
174
+ "polynomials__collect",
175
+ "polynomials__compose",
176
+ "polynomials__evaluate",
177
+ "polynomials__evaluate_composed",
178
+ "polynomials__expand",
179
+ "polynomials__simplify_power",
180
+ "probability__swr_p_level_set",
181
+ "probability__swr_p_sequence",
182
+ ]
183
+
184
+ _QUESTION = "question"
185
+ _ANSWER = "answer"
186
+
187
+ _DATASET_VERSION = "mathematics_dataset-v1.0"
188
+
189
+
190
+ def _generate_builder_configs():
191
+ """Generate configs with different subsets of mathematics dataset."""
192
+ configs = []
193
+ for module in sorted(set(_MODULES)):
194
+ configs.append(
195
+ datasets.BuilderConfig(
196
+ name=module,
197
+ version=datasets.Version("1.0.0"),
198
+ description=_DESCRIPTION,
199
+ )
200
+ )
201
+
202
+ return configs
203
+
204
+
205
+ class PortugueseMathDataset(datasets.GeneratorBasedBuilder):
206
+ """Math Dataset."""
207
+
208
+ BUILDER_CONFIGS = _generate_builder_configs()
209
+
210
+ def _info(self):
211
+ return datasets.DatasetInfo(
212
+ description=_DESCRIPTION,
213
+ features=datasets.Features(
214
+ {
215
+ _QUESTION: datasets.Value("string"),
216
+ _ANSWER: datasets.Value("string"),
217
+ }
218
+ ),
219
+ supervised_keys=(_QUESTION, _ANSWER),
220
+ homepage="https://github.com/deepmind/mathematics_dataset",
221
+ citation=_CITATION,
222
+ )
223
+
224
+ def _get_filepaths_from_categories(self, config, categories):
225
+ filepaths = []
226
+ for category in categories:
227
+ data_file = "/".join([_DATASET_VERSION, category, config])
228
+ filepaths.append(data_file)
229
+ return set(filepaths)
230
+
231
+ def _split_generators(self, dl_manager):
232
+ """Returns SplitGenerators."""
233
+
234
+ archive = dl_manager.download(_DATA_URL)
235
+ config = self.config.name + ".txt"
236
+
237
+ return [
238
+ datasets.SplitGenerator(
239
+ name=datasets.Split.TRAIN,
240
+ gen_kwargs={
241
+ "files": dl_manager.iter_archive(archive),
242
+ "config": config,
243
+ "categories": _TRAIN_CATEGORY,
244
+ },
245
+ ),
246
+ datasets.SplitGenerator(
247
+ name=datasets.Split.TEST,
248
+ gen_kwargs={
249
+ "files": dl_manager.iter_archive(archive),
250
+ "config": config,
251
+ "categories": _INTERPOLATE_CATEGORY,
252
+ },
253
+ ),
254
+ ]
255
+
256
+ def _generate_examples(self, files, config, categories):
257
+ """Yields examples based on directory, module file.."""
258
+
259
+ idx = 0
260
+ filepaths = self._get_filepaths_from_categories(config, categories)
261
+ for path, f in files:
262
+ if not filepaths:
263
+ break
264
+ elif path in filepaths:
265
+ for question in f:
266
+ if not question:
267
+ continue
268
+ else:
269
+ for answer in f:
270
+ if not answer:
271
+ continue
272
+ else:
273
+ yield idx, {_QUESTION: question, _ANSWER: answer}
274
+ idx += 1
275
+ break
276
+ filepaths.remove(path)