Convert dataset to Parquet (#2)

- Convert dataset to Parquet (d972dc610532837ba9cfd665578f869862a082ed)
- Delete loading script (35e062508bef47c0842c2828d968eca25d6a7a3b)
- Delete legacy dataset_infos.json (ee3ee04ae885d5b1e821b3239e98fd282924290f)

README.md

@@ -1,15 +1,14 @@
 ---
 annotations_creators:
 - crowdsourced
-language:
-- en
 language_creators:
 - found
+language:
+- en
 license:
 - cc-by-4.0
 multilinguality:
 - monolingual
-pretty_name: Question Answering via Sentence Composition (QASC)
 size_categories:
 - 1K<n<10K
 source_datasets:
@@ -21,6 +20,7 @@ task_ids:
 - extractive-qa
 - multiple-choice-qa
 paperswithcode_id: qasc
+pretty_name: Question Answering via Sentence Composition (QASC)
 dataset_info:
   features:
   - name: id
@@ -44,17 +44,26 @@ dataset_info:
   - name: formatted_question
     dtype: string
   splits:
-  - name: test
-    num_bytes: 393683
-    num_examples: 920
   - name: train
-    num_bytes: 4919377
+    num_bytes: 4891878
     num_examples: 8134
+  - name: test
+    num_bytes: 390534
+    num_examples: 920
   - name: validation
-    num_bytes: 562352
+    num_bytes: 559180
     num_examples: 926
-  download_size: 1616514
-  dataset_size: 5875412
+  download_size: 2349698
+  dataset_size: 5841592
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for "qasc"

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:495cfbd17abc1720b54785cdce68825104aaf60d7b8bdb2acac62157a31eb517
+size 158241

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9a297b5ab55f1605c7682ffbb7042c26d7ecb9ff1e1aa5a820d4e791c8302d1
+size 1967904

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1ae34ae13c5fce2c55305372c203c6cdb789728d0d7e5ea2956d55bc33f40ae
+size 223553

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "\nQASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice \nquestions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.\n", "citation": "@article{allenai:qasc,\n      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},\n      title     = {QASC: A Dataset for Question Answering via Sentence Composition},\n      journal   = {arXiv:1910.11473v2},\n      year      = {2020},\n}\n", "homepage": "https://allenai.org/data/qasc", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}, "fact1": {"dtype": "string", "id": null, "_type": "Value"}, "fact2": {"dtype": "string", "id": null, "_type": "Value"}, "combinedfact": {"dtype": "string", "id": null, "_type": "Value"}, "formatted_question": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "qasc", "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": 393683, "num_examples": 920, "dataset_name": "qasc"}, "train": {"name": "train", "num_bytes": 4919377, "num_examples": 8134, "dataset_name": "qasc"}, "validation": {"name": "validation", "num_bytes": 562352, "num_examples": 926, "dataset_name": "qasc"}}, "download_checksums": {"http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz": {"num_bytes": 1616514, "checksum": "a7b3f2244f768974c609fd621346c931a72715609f171cb5544fc1da2a2ad55c"}}, "download_size": 1616514, "dataset_size": 5875412, "size_in_bytes": 7491926}}

qasc.py

@@ -1,123 +0,0 @@
-"""TODO(qasc): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-# TODO(qasc): BibTeX citation
-_CITATION = """\
-@article{allenai:qasc,
-      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
-      title     = {QASC: A Dataset for Question Answering via Sentence Composition},
-      journal   = {arXiv:1910.11473v2},
-      year      = {2020},
-}
-"""
-
-# TODO(qasc):
-_DESCRIPTION = """
-QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
-questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
-"""
-_URl = "http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz"
-
-
-class Qasc(datasets.GeneratorBasedBuilder):
-    """TODO(qasc): Short description of my dataset."""
-
-    # TODO(qasc): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(qasc): 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(
-                {
-                    "id": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "choices": datasets.features.Sequence(
-                        {"text": datasets.Value("string"), "label": datasets.Value("string")}
-                    ),
-                    "answerKey": datasets.Value("string"),
-                    "fact1": datasets.Value("string"),
-                    "fact2": datasets.Value("string"),
-                    "combinedfact": datasets.Value("string"),
-                    "formatted_question": 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="https://allenai.org/data/qasc",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(qasc): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        archive = dl_manager.download(_URl)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "train.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "test.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "dev.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Yields examples."""
-        # TODO(qasc): Yields (key, example) tuples from the dataset
-        for path, f in files:
-            if path == filepath:
-                for row in f:
-                    data = json.loads(row.decode("utf-8"))
-                    answerkey = data.get("answerKey", "")
-                    id_ = data["id"]
-                    question = data["question"]["stem"]
-                    choices = data["question"]["choices"]
-                    text_choices = [choice["text"] for choice in choices]
-                    label_choices = [choice["label"] for choice in choices]
-                    fact1 = data.get("fact1", "")
-                    fact2 = data.get("fact2", "")
-                    combined_fact = data.get("combinedfact", "")
-                    formatted_question = data.get("formatted_question", "")
-                    yield id_, {
-                        "id": id_,
-                        "answerKey": answerkey,
-                        "question": question,
-                        "choices": {"text": text_choices, "label": label_choices},
-                        "fact1": fact1,
-                        "fact2": fact2,
-                        "combinedfact": combined_fact,
-                        "formatted_question": formatted_question,
-                    }
-                break