Convert dataset to Parquet (#2)

- Convert dataset to Parquet (35c5ed4389cac0e29b43368700af6de2ab63da7a)
- Add middle data files (2d9f932f15fa7696ffa4326695cf13b22a0c9a0e)
- Add all data files (e37ee6f69218713f994892b3e7563910cc3bc246)
- Delete loading script (05d5c34d00dcd337e897d86ed88ad4f9d787857d)
- Delete legacy dataset_infos.json (182594e7620a2c89a7b668bec71427dee5419fce)

README.md

@@ -1,15 +1,14 @@
 ---
 annotations_creators:
 - expert-generated
-language:
-- en
 language_creators:
 - found
+language:
+- en
 license:
 - other
 multilinguality:
 - monolingual
-pretty_name: RACE
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -19,8 +18,9 @@ task_categories:
 task_ids:
 - multiple-choice-qa
 paperswithcode_id: race
+pretty_name: RACE
 dataset_info:
-- config_name: high
+- config_name: all
   features:
   - name: example_id
     dtype: string
@@ -34,17 +34,17 @@ dataset_info:
     sequence: string
   splits:
   - name: test
-    num_bytes: 6989121
-    num_examples: 3498
+    num_bytes: 8775370
+    num_examples: 4934
   - name: train
-    num_bytes: 126243396
-    num_examples: 62445
+    num_bytes: 157308478
+    num_examples: 87866
   - name: validation
-    num_bytes: 6885287
-    num_examples: 3451
-  download_size: 25443609
-  dataset_size: 140117804
-- config_name: middle
+    num_bytes: 8647176
+    num_examples: 4887
+  download_size: 41500647
+  dataset_size: 174731024
+- config_name: high
   features:
   - name: example_id
     dtype: string
@@ -58,17 +58,17 @@ dataset_info:
     sequence: string
   splits:
   - name: test
-    num_bytes: 1786297
-    num_examples: 1436
+    num_bytes: 6989097
+    num_examples: 3498
   - name: train
-    num_bytes: 31065322
-    num_examples: 25421
+    num_bytes: 126243228
+    num_examples: 62445
   - name: validation
-    num_bytes: 1761937
-    num_examples: 1436
-  download_size: 25443609
-  dataset_size: 34613556
-- config_name: all
+    num_bytes: 6885263
+    num_examples: 3451
+  download_size: 33750880
+  dataset_size: 140117588
+- config_name: middle
   features:
   - name: example_id
     dtype: string
@@ -82,16 +82,41 @@ dataset_info:
     sequence: string
   splits:
   - name: test
-    num_bytes: 8775394
-    num_examples: 4934
+    num_bytes: 1786273
+    num_examples: 1436
   - name: train
-    num_bytes: 157308694
-    num_examples: 87866
+    num_bytes: 31065250
+    num_examples: 25421
   - name: validation
-    num_bytes: 8647200
-    num_examples: 4887
-  download_size: 25443609
-  dataset_size: 174731288
+    num_bytes: 1761913
+    num_examples: 1436
+  download_size: 7781596
+  dataset_size: 34613436
+configs:
+- config_name: all
+  data_files:
+  - split: test
+    path: all/test-*
+  - split: train
+    path: all/train-*
+  - split: validation
+    path: all/validation-*
+- config_name: high
+  data_files:
+  - split: test
+    path: high/test-*
+  - split: train
+    path: high/train-*
+  - split: validation
+    path: high/validation-*
+- config_name: middle
+  data_files:
+  - split: test
+    path: middle/test-*
+  - split: train
+    path: middle/train-*
+  - split: validation
+    path: middle/validation-*
 ---
 
 # Dataset Card for "race"

all/test-00000-of-00001.parquet

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+oid sha256:442e6b5f22f525811544016552b2ee7d8d1b8ef31e989132edc86e7925f94136
+size 2075881

all/train-00000-of-00001.parquet

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+oid sha256:d9986432dc7b5ced3d3e9372573eafdcfa599920bb5e65bbbdc245df3abfd09e
+size 37378263

all/validation-00000-of-00001.parquet

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+oid sha256:2b3668ef8556831d174fab9cf9a7557e565a312034bce715a28e116903fa6a53
+size 2046503

dataset_infos.json

@@ -1 +0,0 @@
-{"high": {"description": "Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The\n dataset is collected from English examinations in China, which are designed for middle school and high school students.\nThe dataset can be served as the training and test sets for machine comprehension.\n\n", "citation": "@article{lai2017large,\n    title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},\n    author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},\n    journal={arXiv preprint arXiv:1704.04683},\n    year={2017}\n}\n", "homepage": "http://www.cs.cmu.edu/~glai1/data/race/", "license": "", "features": {"example_id": {"dtype": "string", "id": null, "_type": "Value"}, "article": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "race", "config_name": "high", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 6989121, "num_examples": 3498, "dataset_name": "race"}, "train": {"name": "train", "num_bytes": 126243396, "num_examples": 62445, "dataset_name": "race"}, "validation": {"name": "validation", "num_bytes": 6885287, "num_examples": 3451, "dataset_name": "race"}}, "download_checksums": {"http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz": {"num_bytes": 25443609, "checksum": "b2769cc9fdc5c546a693300eb9a966cec6870bd349fbc44ed5225f8ad33006e5"}}, "download_size": 25443609, "post_processing_size": null, "dataset_size": 140117804, "size_in_bytes": 165561413}, "middle": {"description": "Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The\n dataset is collected from English examinations in China, which are designed for middle school and high school students.\nThe dataset can be served as the training and test sets for machine comprehension.\n\n", "citation": "@article{lai2017large,\n    title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},\n    author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},\n    journal={arXiv preprint arXiv:1704.04683},\n    year={2017}\n}\n", "homepage": "http://www.cs.cmu.edu/~glai1/data/race/", "license": "", "features": {"example_id": {"dtype": "string", "id": null, "_type": "Value"}, "article": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "race", "config_name": "middle", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1786297, "num_examples": 1436, "dataset_name": "race"}, "train": {"name": "train", "num_bytes": 31065322, "num_examples": 25421, "dataset_name": "race"}, "validation": {"name": "validation", "num_bytes": 1761937, "num_examples": 1436, "dataset_name": "race"}}, "download_checksums": {"http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz": {"num_bytes": 25443609, "checksum": "b2769cc9fdc5c546a693300eb9a966cec6870bd349fbc44ed5225f8ad33006e5"}}, "download_size": 25443609, "post_processing_size": null, "dataset_size": 34613556, "size_in_bytes": 60057165}, "all": {"description": "Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The\n dataset is collected from English examinations in China, which are designed for middle school and high school students.\nThe dataset can be served as the training and test sets for machine comprehension.\n\n", "citation": "@article{lai2017large,\n    title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},\n    author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},\n    journal={arXiv preprint arXiv:1704.04683},\n    year={2017}\n}\n", "homepage": "http://www.cs.cmu.edu/~glai1/data/race/", "license": "", "features": {"example_id": {"dtype": "string", "id": null, "_type": "Value"}, "article": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "race", "config_name": "all", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 8775394, "num_examples": 4934, "dataset_name": "race"}, "train": {"name": "train", "num_bytes": 157308694, "num_examples": 87866, "dataset_name": "race"}, "validation": {"name": "validation", "num_bytes": 8647200, "num_examples": 4887, "dataset_name": "race"}}, "download_checksums": {"http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz": {"num_bytes": 25443609, "checksum": "b2769cc9fdc5c546a693300eb9a966cec6870bd349fbc44ed5225f8ad33006e5"}}, "download_size": 25443609, "post_processing_size": null, "dataset_size": 174731288, "size_in_bytes": 200174897}}

high/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97ef697f4c81e804eb43e7a667a6ac7bb06c11a99cfa537997ae5e2f6c5db2b7
+size 1683666

high/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea4766ae684939d67e115c0abe7cbe803c198eef77ecfa8557b0d0f32cdbe58d
+size 30411742

high/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fc6a837dfa7839614bbfb78b54d98463d891d97509bdcd5ff9062bd9d446542
+size 1655472

middle/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ea3fb86ab6bbf8746dd0eb0ac1961a1fdbd1214d6d5a901c37b0cad5b5c5971
+size 404725

middle/train-00000-of-00001.parquet

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+oid sha256:33a9d28467c326b13684bddd651a3bdc057aea61b35f93258c95efd76a435f63
+size 6969931

middle/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8ede76683827fc6f5219643626d171c2556ee6b47193b192f399c2f7c4e60a0
+size 406940

race.py

@@ -1,111 +0,0 @@
-"""TODO(race): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-@article{lai2017large,
-    title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},
-    author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},
-    journal={arXiv preprint arXiv:1704.04683},
-    year={2017}
-}
-"""
-
-_DESCRIPTION = """\
-Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The
- dataset is collected from English examinations in China, which are designed for middle school and high school students.
-The dataset can be served as the training and test sets for machine comprehension.
-
-"""
-
-_URL = "http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz"
-
-
-class Race(datasets.GeneratorBasedBuilder):
-    """ReAding Comprehension Dataset From Examination dataset from CMU"""
-
-    VERSION = datasets.Version("0.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="high", description="Exams designed for high school students", version=VERSION),
-        datasets.BuilderConfig(
-            name="middle", description="Exams designed for middle school students", version=VERSION
-        ),
-        datasets.BuilderConfig(
-            name="all", description="Exams designed for both high school and middle school students", version=VERSION
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "example_id": datasets.Value("string"),
-                    "article": datasets.Value("string"),
-                    "answer": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "options": datasets.features.Sequence(datasets.Value("string"))
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="http://www.cs.cmu.edu/~glai1/data/race/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        archive = dl_manager.download(_URL)
-        case = str(self.config.name)
-        if case == "all":
-            case = ""
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"train_test_or_eval": f"RACE/test/{case}", "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"train_test_or_eval": f"RACE/train/{case}", "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"train_test_or_eval": f"RACE/dev/{case}", "files": dl_manager.iter_archive(archive)},
-            ),
-        ]
-
-    def _generate_examples(self, train_test_or_eval, files):
-        """Yields examples."""
-        for file_idx, (path, f) in enumerate(files):
-            if path.startswith(train_test_or_eval) and path.endswith(".txt"):
-                data = json.loads(f.read().decode("utf-8"))
-                questions = data["questions"]
-                answers = data["answers"]
-                options = data["options"]
-                for i in range(len(questions)):
-                    question = questions[i]
-                    answer = answers[i]
-                    option = options[i]
-                    yield f"{file_idx}_{i}", {
-                        "example_id": data["id"],
-                        "article": data["article"],
-                        "question": question,
-                        "answer": answer,
-                        "options": option,
-                    }