Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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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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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dummy/algebra__linear_1d/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:af12a948b5d88ad0ceb435b1abd3ccc0d98ab41a3a9a7e4f982545b85b8ea782
+size 2688

math_dataset.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Mathematics database."""
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+import os
+
+import datasets
+
+
+_CITATION = """
+@article{2019arXiv,
+  author = {Saxton, Grefenstette, Hill, Kohli},
+  title = {Analysing Mathematical Reasoning Abilities of Neural Models},
+  year = {2019},
+  journal = {arXiv:1904.01557}
+}
+"""
+
+_DESCRIPTION = """
+Mathematics database.
+
+This dataset code generates mathematical question and answer pairs,
+from a range of question types at roughly school-level difficulty.
+This is designed to test the mathematical learning and algebraic
+reasoning skills of learning models.
+
+Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
+(Saxton, Grefenstette, Hill, Kohli).
+
+Example usage:
+train_examples, val_examples = datasets.load_dataset(
+    'math_dataset/arithmetic__mul',
+    split=['train', 'test'],
+    as_supervised=True)
+"""
+
+_DATA_URL = "https://storage.googleapis.com/mathematics-dataset/mathematics_dataset-v1.0.tar.gz"
+
+_TRAIN_CATEGORY = [
+    "train-easy",
+    "train-medium",
+    "train-hard",
+]
+
+_INTERPOLATE_CATEGORY = [
+    "interpolate",
+]
+
+_MODULES = [
+    # extrapolate
+    "measurement__conversion",
+    # interpolate
+    "algebra__linear_1d",
+    "algebra__linear_1d_composed",
+    "algebra__linear_2d",
+    "algebra__linear_2d_composed",
+    "algebra__polynomial_roots",
+    "algebra__polynomial_roots_composed",
+    "algebra__sequence_next_term",
+    "algebra__sequence_nth_term",
+    "arithmetic__add_or_sub",
+    "arithmetic__add_or_sub_in_base",
+    "arithmetic__add_sub_multiple",
+    "arithmetic__div",
+    "arithmetic__mixed",
+    "arithmetic__mul",
+    "arithmetic__mul_div_multiple",
+    "arithmetic__nearest_integer_root",
+    "arithmetic__simplify_surd",
+    "calculus__differentiate",
+    "calculus__differentiate_composed",
+    "comparison__closest",
+    "comparison__closest_composed",
+    "comparison__kth_biggest",
+    "comparison__kth_biggest_composed",
+    "comparison__pair",
+    "comparison__pair_composed",
+    "comparison__sort",
+    "comparison__sort_composed",
+    "measurement__conversion",
+    "measurement__time",
+    "numbers__base_conversion",
+    "numbers__div_remainder",
+    "numbers__div_remainder_composed",
+    "numbers__gcd",
+    "numbers__gcd_composed",
+    "numbers__is_factor",
+    "numbers__is_factor_composed",
+    "numbers__is_prime",
+    "numbers__is_prime_composed",
+    "numbers__lcm",
+    "numbers__lcm_composed",
+    "numbers__list_prime_factors",
+    "numbers__list_prime_factors_composed",
+    "numbers__place_value",
+    "numbers__place_value_composed",
+    "numbers__round_number",
+    "numbers__round_number_composed",
+    "polynomials__add",
+    "polynomials__coefficient_named",
+    "polynomials__collect",
+    "polynomials__compose",
+    "polynomials__evaluate",
+    "polynomials__evaluate_composed",
+    "polynomials__expand",
+    "polynomials__simplify_power",
+    "probability__swr_p_level_set",
+    "probability__swr_p_sequence",
+    # train-easy train-medium train-hard
+    "algebra__linear_1d",
+    "algebra__linear_1d_composed",
+    "algebra__linear_2d",
+    "algebra__linear_2d_composed",
+    "algebra__polynomial_roots",
+    "algebra__polynomial_roots_composed",
+    "algebra__sequence_next_term",
+    "algebra__sequence_nth_term",
+    "arithmetic__add_or_sub",
+    "arithmetic__add_or_sub_in_base",
+    "arithmetic__add_sub_multiple",
+    "arithmetic__div",
+    "arithmetic__mixed",
+    "arithmetic__mul",
+    "arithmetic__mul_div_multiple",
+    "arithmetic__nearest_integer_root",
+    "arithmetic__simplify_surd",
+    "calculus__differentiate",
+    "calculus__differentiate_composed",
+    "comparison__closest",
+    "comparison__closest_composed",
+    "comparison__kth_biggest",
+    "comparison__kth_biggest_composed",
+    "comparison__pair",
+    "comparison__pair_composed",
+    "comparison__sort",
+    "comparison__sort_composed",
+    "measurement__conversion",
+    "measurement__time",
+    "numbers__base_conversion",
+    "numbers__div_remainder",
+    "numbers__div_remainder_composed",
+    "numbers__gcd",
+    "numbers__gcd_composed",
+    "numbers__is_factor",
+    "numbers__is_factor_composed",
+    "numbers__is_prime",
+    "numbers__is_prime_composed",
+    "numbers__lcm",
+    "numbers__lcm_composed",
+    "numbers__list_prime_factors",
+    "numbers__list_prime_factors_composed",
+    "numbers__place_value",
+    "numbers__place_value_composed",
+    "numbers__round_number",
+    "numbers__round_number_composed",
+    "polynomials__add",
+    "polynomials__coefficient_named",
+    "polynomials__collect",
+    "polynomials__compose",
+    "polynomials__evaluate",
+    "polynomials__evaluate_composed",
+    "polynomials__expand",
+    "polynomials__simplify_power",
+    "probability__swr_p_level_set",
+    "probability__swr_p_sequence",
+]
+
+_QUESTION = "question"
+_ANSWER = "answer"
+
+_DATASET_VERSION = "mathematics_dataset-v1.0"
+
+
+def _generate_builder_configs():
+    """Generate configs with different subsets of mathematics dataset."""
+    configs = []
+    for module in sorted(set(_MODULES)):
+        configs.append(
+            datasets.BuilderConfig(
+                name=module,
+                version=datasets.Version("1.0.0"),
+                description=_DESCRIPTION,
+            )
+        )
+
+    return configs
+
+
+class MathDataset(datasets.GeneratorBasedBuilder):
+    """Math Dataset."""
+
+    BUILDER_CONFIGS = _generate_builder_configs()
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    _QUESTION: datasets.Value("string"),
+                    _ANSWER: datasets.Value("string"),
+                }
+            ),
+            supervised_keys=(_QUESTION, _ANSWER),
+            homepage="https://github.com/deepmind/mathematics_dataset",
+            citation=_CITATION,
+        )
+
+    def _read_data_from_all_categories(self, directory, config, categories):
+        lines = []
+        for category in categories:
+            data_file = os.path.join(directory, _DATASET_VERSION, category, config)
+            if os.path.exists(data_file):
+                with open(data_file, encoding="utf-8") as f:
+                    ls = f.read().split("\n")
+
+                    for l in ls[::-1]:
+                        if not l:
+                            ls.remove(l)
+
+                    lines.extend(ls)
+
+        return lines
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        directory = dl_manager.download_and_extract(_DATA_URL)
+        config = self.config.name + ".txt"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "directory": directory,
+                    "config": config,
+                    "categories": _TRAIN_CATEGORY,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "directory": directory,
+                    "config": config,
+                    "categories": _INTERPOLATE_CATEGORY,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, directory, config, categories):
+        """Yields examples based on directory, module file.."""
+
+        lines = self._read_data_from_all_categories(directory, config, categories)
+        logging.info("%s: %s contains total: %d", categories, config, len(lines))
+        questions = lines[::2]
+        answers = lines[1::2]
+
+        assert len(answers) == len(questions), "answers: %d do not match questions: %d" % (
+            len(answers),
+            len(questions),
+        )
+
+        for idx, (q, a) in enumerate(zip(questions, answers)):
+            result = {_QUESTION: q, _ANSWER: a}
+            if all(result.values()):
+                yield idx, result