Convert dataset to Parquet (#3)

- Convert dataset to Parquet (30419eec6bcdb94fe60d390303908466b5ca371f)
- Delete loading script (7378970ed7ca5cf8368a571e0acabd0490457941)
- Delete data file (11e5825277dc0fbc57ee1460e78ade3bf614d217)

README.md

@@ -20,6 +20,24 @@ task_ids:
 - multiple-choice-qa
 paperswithcode_id: race
 dataset_info:
+  config_name: high
+  features:
+  - name: article
+    dtype: string
+  - name: problems
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 2741424
+    num_examples: 1045
+  download_size: 1628907
+  dataset_size: 2741424
+configs:
+- config_name: high
+  data_files:
+  - split: test
+    path: high/test-*
+  default: true
 ---
 
 

high/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b07da27f803881229a3208563c6b034b12b3ab5fc6f51140ad9a3825ee6949ef
+size 1628907

race.py

@@ -1,66 +0,0 @@
-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.
-"""
-
-_BASE_URL = "https://huggingface.co/datasets/bfattori/race/raw/main"
-_URLS = {
-    "high": f"{_BASE_URL}/race_high_test.jsonl",
-}
-
-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),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-                {
-                    "article": datasets.Value("string"),
-                    "problems": datasets.Value("string"),
-                }
-            )
-        return datasets.DatasetInfo(
-            description=f"{_DESCRIPTION}\n{self.config.description}",
-            features=features,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir,
-                    "split": datasets.Split.TEST,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                yield key, {"article": data["article"], "problems": data["problems"]}