Convert dataset to Parquet

README.md

@@ -9,8 +9,6 @@ license:
 - cc-by-4.0
 multilinguality:
 - monolingual
-pretty_name: 'DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question
-  Answering'
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -20,6 +18,8 @@ task_categories:
 task_ids:
 - extractive-qa
 - open-domain-qa
+pretty_name: 'DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question
+  Answering'
 dataset_info:
   features:
   - name: squad_v2_id
@@ -40,16 +40,25 @@ dataset_info:
       dtype: int32
   splits:
   - name: train
-    num_bytes: 7712523
+    num_bytes: 7712491
     num_examples: 7182
   - name: test
-    num_bytes: 3865097
+    num_bytes: 3865065
     num_examples: 3643
   - name: validation
-    num_bytes: 1072731
+    num_bytes: 1072699
     num_examples: 1000
-  download_size: 48935038
-  dataset_size: 12650351
+  download_size: 4246350
+  dataset_size: 12650255
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 590102

data/train-00000-of-00001.parquet

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+oid sha256:0e4e82c5d2b8b5c03afe4b8cedb2816abcdcb59dedd9edfa009eb95bd49e3d15
+size 3221857

data/validation-00000-of-00001.parquet

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+oid sha256:7c88a0ae7fcbd0650cd414734948a89ee62ddf2593de5b93efb5c54ab2137b24
+size 434391

disfl_qa.py

@@ -1,199 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""A Benchmark Dataset for Understanding Disfluencies in Question Answering"""
-
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-_CITATION = """\
-@inproceedings{gupta-etal-2021-disflqa,
-    title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}",
-    author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal",
-    booktitle = "Findings of ACL",
-    year = "2021"
-}
-
-"""
-
-_DESCRIPTION = """\
-Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,
-namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)
-dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as
-a source of distractors.
-
-The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90% of the disfluencies are
-corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a
-major gap between speech and NLP research community. We hope the dataset can serve as a benchmark dataset for
-testing robustness of models against disfluent inputs.
-
-Our expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from
-Disfl-QA. Detailed experiments and analyses can be found in our paper.
-"""
-
-_HOMEPAGE = "https://github.com/google-research-datasets/disfl-qa"
-
-_LICENSE = "Disfl-QA dataset is licensed under CC BY 4.0"
-
-_URL = "https://raw.githubusercontent.com/google-research-datasets/Disfl-QA/main/"
-
-_URLS_squad_v2 = {
-    "train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/" + "train-v2.0.json",
-    "dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/" + "dev-v2.0.json",
-}
-
-
-class DisflQA(datasets.GeneratorBasedBuilder):
-    """A Benchmark Dataset for Understanding Disfluencies in Question Answering"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "squad_v2_id": datasets.Value("string"),
-                "original question": datasets.Value("string"),
-                "disfluent question": datasets.Value("string"),
-                "title": datasets.Value("string"),
-                "context": datasets.Value("string"),
-                "answers": datasets.features.Sequence(
-                    {
-                        "text": datasets.Value("string"),
-                        "answer_start": datasets.Value("int32"),
-                    }
-                ),
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # 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=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="disfluent question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        squad_v2_downloaded_files = dl_manager.download_and_extract(_URLS_squad_v2)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": dl_manager.download_and_extract(_URL + "train.json"),
-                    "split": "train",
-                    "squad_v2_data": squad_v2_downloaded_files,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": dl_manager.download_and_extract(_URL + "test.json"),
-                    "split": "test",
-                    "squad_v2_data": squad_v2_downloaded_files,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": dl_manager.download_and_extract(_URL + "dev.json"),
-                    "split": "dev",
-                    "squad_v2_data": squad_v2_downloaded_files,
-                },
-            ),
-        ]
-
-    def _generate_examples(
-        self,
-        filepath,
-        split,
-        squad_v2_data,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    ):
-        """Yields examples as (key, example) tuples."""
-
-        merge_squad_v2_json = {}
-
-        for file in squad_v2_data:
-            with open(squad_v2_data[file], encoding="utf-8") as f:
-                merge_squad_v2_json.update(json.load(f))
-
-        squad_v2_dict = _helper_dict(merge_squad_v2_json)  # contains all squad_v2 data in a dict with id as key
-
-        with open(filepath, encoding="utf-8") as f:
-            glob_id = 0
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                for i in data:
-                    yield glob_id, {
-                        "squad_v2_id": i,
-                        "disfluent question": data[i]["disfluent"],
-                        "title": squad_v2_dict[i]["title"],
-                        "context": squad_v2_dict[i]["context"],
-                        "original question": squad_v2_dict[i]["question"],
-                        "answers": {
-                            "answer_start": squad_v2_dict[i]["answers"]["answer_start"],
-                            "text": squad_v2_dict[i]["answers"]["text"],
-                        },
-                    }
-                    glob_id += 1
-
-
-def _helper_dict(row_squad_v2: dict):  # creates dict with id as key for combined squad_v2
-
-    squad_v2_dict = {}
-
-    for example in row_squad_v2["data"]:
-        title = example.get("title", "").strip()
-        for paragraph in example["paragraphs"]:
-            context = paragraph["context"].strip()
-            for qa in paragraph["qas"]:
-                question = qa["question"].strip()
-                id_ = qa["id"]
-
-                answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                squad_v2_dict[id_] = {
-                    "title": title,
-                    "context": context,
-                    "question": question,
-                    "id": id_,
-                    "answers": {
-                        "answer_start": answer_starts,
-                        "text": answers,
-                    },
-                }
-    return squad_v2_dict