Convert dataset to Parquet (#4)

- Convert dataset to Parquet (c7ea11f06ac799e1206befb10da77d7a34c8ce46)
- Delete loading script (04c3d428e8bfe0bfc89838e1f5efc20f8880e0e7)
- Delete legacy dataset_infos.json (ff6ff3901bdb539d0c822ddd27fff0301722c7f9)

README.md

@@ -35,16 +35,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 6556427
+    num_bytes: 6546183
     num_examples: 11679
   - name: validation
-    num_bytes: 555019
+    num_bytes: 554120
     num_examples: 1000
   - name: test
-    num_bytes: 564826
+    num_bytes: 563927
     num_examples: 1000
-  download_size: 2821345
-  dataset_size: 7676272
+  download_size: 4674410
+  dataset_size: 7664230
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "sciq"

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a719356a29b127fc54ef3c7f51a034db4bd105d5717215e8c85d2aa58d60667
+size 342808

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19644360954006d06e9ad3df07bddb34f8535c081b831d48f604603c713ac167
+size 3993099

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:455dd9f1d725cd3ecbce369799a2fbbdbbfecf51ab84a86d56ba3370dc847b8a
+size 338503

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided.\n\n", "citation": "@inproceedings{SciQ,\n    title={Crowdsourcing Multiple Choice Science Questions},\n    author={Johannes Welbl, Nelson F. Liu, Matt Gardner},\n    year={2017},\n    journal={arXiv:1707.06209v1}\n}\n", "homepage": "https://allenai.org/data/sciq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "distractor3": {"dtype": "string", "id": null, "_type": "Value"}, "distractor1": {"dtype": "string", "id": null, "_type": "Value"}, "distractor2": {"dtype": "string", "id": null, "_type": "Value"}, "correct_answer": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "sciq", "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": 564826, "num_examples": 1000, "dataset_name": "sciq"}, "train": {"name": "train", "num_bytes": 6556427, "num_examples": 11679, "dataset_name": "sciq"}, "validation": {"name": "validation", "num_bytes": 555019, "num_examples": 1000, "dataset_name": "sciq"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-website/data/SciQ.zip": {"num_bytes": 2821345, "checksum": "7f3312f6ac6b09970b32942d106a8c44ec0dad46a0369f17d635aff8e348a87c"}}, "download_size": 2821345, "dataset_size": 7676272, "size_in_bytes": 10497617}}

sciq.py

@@ -1,91 +0,0 @@
-"""TODO(sciQ): Add a description here."""
-
-
-import json
-import os
-
-import datasets
-
-
-# TODO(sciQ): BibTeX citation
-_CITATION = """\
-@inproceedings{SciQ,
-    title={Crowdsourcing Multiple Choice Science Questions},
-    author={Johannes Welbl, Nelson F. Liu, Matt Gardner},
-    year={2017},
-    journal={arXiv:1707.06209v1}
-}
-"""
-
-# TODO(sciQ):
-_DESCRIPTION = """\
-The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided.
-
-"""
-_URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/SciQ.zip"
-
-
-class Sciq(datasets.GeneratorBasedBuilder):
-    """TODO(sciQ): Short description of my dataset."""
-
-    # TODO(sciQ): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(sciQ): 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(
-                {
-                    # These are the features of your dataset like images, labels ...
-                    "question": datasets.Value("string"),
-                    "distractor3": datasets.Value("string"),
-                    "distractor1": datasets.Value("string"),
-                    "distractor2": datasets.Value("string"),
-                    "correct_answer": datasets.Value("string"),
-                    "support": datasets.Value("string"),
-                }
-            ),
-            # 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="https://allenai.org/data/sciq",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(sciQ): 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)
-        data_dir = os.path.join(dl_dir, "SciQ dataset-2 3")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "train.json")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "valid.json")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, "test.json")},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(sciQ): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for id_, row in enumerate(data):
-                yield id_, row