Convert dataset to Parquet

README.md

@@ -1,15 +1,14 @@
 ---
 annotations_creators:
 - crowdsourced
-language:
-- en
 language_creators:
 - found
+language:
+- en
 license:
 - unknown
 multilinguality:
 - monolingual
-pretty_name: WebQuestions
 size_categories:
 - 1K<n<10K
 source_datasets:
@@ -19,6 +18,7 @@ task_categories:
 task_ids:
 - open-domain-qa
 paperswithcode_id: webquestions
+pretty_name: WebQuestions
 dataset_info:
   features:
   - name: url
@@ -29,13 +29,20 @@ dataset_info:
     sequence: string
   splits:
   - name: train
-    num_bytes: 533736
+    num_bytes: 530711
     num_examples: 3778
   - name: test
-    num_bytes: 289824
+    num_bytes: 288184
     num_examples: 2032
-  download_size: 1272965
-  dataset_size: 823560
+  download_size: 402395
+  dataset_size: 818895
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "web_questions"

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c81463f22162f645bdbdc0d501cfee9ad7d31bee819b3226f734523741c9faa
+size 142239

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b59a32059d001919540ec617afb30d5c06829cbd3159e3da5f915bbd1d973bc
+size 260156

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "This dataset consists of 6,642 question/answer pairs.\nThe questions are supposed to be answerable by Freebase, a large knowledge graph.\nThe questions are mostly centered around a single named entity.\nThe questions are popular ones asked on the web (at least in 2013).\n", "citation": "\n@inproceedings{berant-etal-2013-semantic,\n    title = \"Semantic Parsing on {F}reebase from Question-Answer Pairs\",\n    author = \"Berant, Jonathan  and\n      Chou, Andrew  and\n      Frostig, Roy  and\n      Liang, Percy\",\n    booktitle = \"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct,\n    year = \"2013\",\n    address = \"Seattle, Washington, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D13-1160\",\n    pages = \"1533--1544\",\n}\n", "homepage": "https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a", "license": "", "features": {"url": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "web_questions", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 533736, "num_examples": 3778, "dataset_name": "web_questions"}, "test": {"name": "test", "num_bytes": 289824, "num_examples": 2032, "dataset_name": "web_questions"}}, "download_checksums": {"https://worksheets.codalab.org/rest/bundles/0x4a763f8cde224c2da592b75f29e2f5c2/contents/blob/": {"num_bytes": 825320, "checksum": "fb1797e4554a1b1be642388367de1379f8c0d5afc609ac171492c67f7b70cb1e"}, "https://worksheets.codalab.org/rest/bundles/0xe7bac352fce7448c9ef238fb0a297ec2/contents/blob/": {"num_bytes": 447645, "checksum": "e3d4550e90660aaabe18458ba34b59f2624857273f375af7353273ce8b84ce6e"}}, "download_size": 1272965, "dataset_size": 823560, "size_in_bytes": 2096525}}

web_questions.py

@@ -1,98 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""WebQuestions Benchmark for Question Answering."""
-
-
-import json
-import re
-
-import datasets
-
-
-_CITATION = """
-@inproceedings{berant-etal-2013-semantic,
-    title = "Semantic Parsing on {F}reebase from Question-Answer Pairs",
-    author = "Berant, Jonathan  and
-      Chou, Andrew  and
-      Frostig, Roy  and
-      Liang, Percy",
-    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
-    month = oct,
-    year = "2013",
-    address = "Seattle, Washington, USA",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D13-1160",
-    pages = "1533--1544",
-}
-"""
-_SPLIT_DOWNLOAD_URL = {
-    "train": "https://worksheets.codalab.org/rest/bundles/0x4a763f8cde224c2da592b75f29e2f5c2/contents/blob/",
-    "test": "https://worksheets.codalab.org/rest/bundles/0xe7bac352fce7448c9ef238fb0a297ec2/contents/blob/",
-}
-
-_DESCRIPTION = """\
-This dataset consists of 6,642 question/answer pairs.
-The questions are supposed to be answerable by Freebase, a large knowledge graph.
-The questions are mostly centered around a single named entity.
-The questions are popular ones asked on the web (at least in 2013).
-"""
-
-
-class WebQuestions(datasets.GeneratorBasedBuilder):
-    """WebQuestions Benchmark for Question Answering."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "url": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(datasets.Value("string")),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        file_paths = dl_manager.download(_SPLIT_DOWNLOAD_URL)
-
-        return [
-            datasets.SplitGenerator(name=split, gen_kwargs={"file_path": file_path})
-            for split, file_path in file_paths.items()
-        ]
-
-    def _generate_examples(self, file_path):
-        """Parses split file and yields examples."""
-
-        def _target_to_answers(target):
-            target = re.sub(r"^\(list |\)$", "", target)
-            return ["".join(ans) for ans in re.findall(r'\(description (?:"([^"]+?)"|([^)]+?))\)\w*', target)]
-
-        with open(file_path, encoding="utf-8") as f:
-            examples = json.load(f)
-            for i, ex in enumerate(examples):
-                yield i, {
-                    "url": ex["url"],
-                    "question": ex["utterance"],
-                    "answers": _target_to_answers(ex["targetValue"]),
-                }