Update files from the datasets library (from 1.0.2)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.2

dataset_infos.json

@@ -1 +1 @@
-{"default": {"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": {"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"}}, "supervised_keys": null, "builder_name": "race", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 6935145, "num_examples": 3498, "dataset_name": "race"}, "train": {"name": "train", "num_bytes": 125280697, "num_examples": 62445, "dataset_name": "race"}, "validation": {"name": "validation", "num_bytes": 6832070, "num_examples": 3451, "dataset_name": "race"}}, "download_checksums": {"http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz": {"num_bytes": 88627200, "checksum": "98b54b7e656bc9e2e434f99f07863a83f89647a1f4c9428a041c3a4c51db6787"}}, "download_size": 88627200, "dataset_size": 139047912, "size_in_bytes": 227675112}}
+{"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}}

dummy/{0.1.0 → all/0.1.0}/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3f23aa67f2655e2757852e90787c98e91bd88dfa8f0663bfe275129a437755a4
-size 10762
+oid sha256:86b18d4638b1557a5c2360e62cd07877c0475324c6ff47403c8c768ed63afe66
+size 25917

dummy/high/0.1.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b10107665a293521ceebd7354c8d77e5cd3bb16305c26f16d73630b2844eae9
+size 13484

dummy/middle/0.1.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88b445588ec0540d6b11d9f0e82773dc48e91562faa64b91f1d7ec5f4a562ca1
+size 16125

race.py

@@ -4,11 +4,11 @@ from __future__ import absolute_import, division, print_function
 
 import json
 import os
+from pathlib import Path
 
 import datasets
 
 
-# TODO(race): BibTeX citation
 _CITATION = """\
 @article{lai2017large,
     title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},
@@ -18,7 +18,6 @@ _CITATION = """\
 }
 """
 
-# TODO(race):
 _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.
@@ -30,19 +29,28 @@ _URL = "http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz"
 
 
 class Race(datasets.GeneratorBasedBuilder):
-    """TODO(race): Short description of my dataset."""
+    """ReAding Comprehension Dataset From Examination dataset from CMU"""
 
-    # TODO(race): Set up version.
     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):
-        # TODO(race): Specifies the datasets.DatasetInfo object
         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"),
@@ -61,43 +69,36 @@ class Race(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # TODO(race): Downloads the data and defines the splits
+        # Downloads the data and defines the splits
         # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
         dl_dir = dl_manager.download_and_extract(_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={
-                    "files": sorted(os.listdir(os.path.join(dl_dir, "RACE/test/high"))),
-                    "filespath": os.path.join(dl_dir, "RACE/test/high"),
-                },
+                gen_kwargs={"train_test_or_eval": os.path.join(dl_dir, f"RACE/test/{case}")},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "files": sorted(os.listdir(os.path.join(dl_dir, "RACE/train/high"))),
-                    "filespath": os.path.join(dl_dir, "RACE/train/high"),
-                },
+                gen_kwargs={"train_test_or_eval": os.path.join(dl_dir, f"RACE/train/{case}")},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "files": sorted(os.listdir(os.path.join(dl_dir, "RACE/dev/high"))),
-                    "filespath": os.path.join(dl_dir, "RACE/dev/high"),
-                },
+                gen_kwargs={"train_test_or_eval": os.path.join(dl_dir, f"RACE/dev/{case}")},
             ),
         ]
 
-    def _generate_examples(self, files, filespath):
+    def _generate_examples(self, train_test_or_eval):
         """Yields examples."""
-        # TODO(race): Yields (key, example) tuples from the dataset
-        for file in files:
-            filepath = os.path.join(filespath, file)
-            with open(filepath, encoding="utf-8") as f:
+        current_path = Path(train_test_or_eval)
+        files_in_dir = [str(f.absolute()) for f in sorted(current_path.glob("**/*.txt"))]
+        for file in sorted(files_in_dir):
+            with open(file, encoding="utf-8") as f:
                 data = json.load(f)
                 questions = data["questions"]
                 answers = data["answers"]
@@ -107,6 +108,7 @@ class Race(datasets.GeneratorBasedBuilder):
                     answer = answers[i]
                     option = options[i]
                     yield i, {
+                        "example_id": data["id"],
                         "article": data["article"],
                         "question": question,
                         "answer": answer,