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albertvillanova HF staff commited on
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1 Parent(s): d12334e

Convert dataset to Parquet (#6)

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- Convert dataset to Parquet (8daec619ae393b5d7d1d9b7a961e6143dcace018)
- Delete loading script (402ee4baedf703562b586aba29cfb1fcdaa97847)

README.md CHANGED
@@ -1,6 +1,9 @@
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  ---
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  language:
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  - en
 
 
 
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  paperswithcode_id: comqa
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  pretty_name: ComQA
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  dataset_info:
@@ -13,19 +16,25 @@ dataset_info:
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  sequence: string
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  splits:
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  - name: train
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- num_bytes: 696645
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  num_examples: 3966
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  - name: test
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- num_bytes: 273384
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  num_examples: 2243
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  - name: validation
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- num_bytes: 131945
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  num_examples: 966
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- download_size: 1671684
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- dataset_size: 1101974
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- task_categories:
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- - question-answering
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- license: unknown
 
 
 
 
 
 
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  ---
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  # Dataset Card for "com_qa"
 
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  ---
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  language:
3
  - en
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+ license: unknown
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+ task_categories:
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+ - question-answering
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  paperswithcode_id: comqa
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  pretty_name: ComQA
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  dataset_info:
 
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  sequence: string
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  splits:
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  - name: train
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+ num_bytes: 692932
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  num_examples: 3966
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  - name: test
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+ num_bytes: 271554
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  num_examples: 2243
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  - name: validation
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+ num_bytes: 131129
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  num_examples: 966
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+ download_size: 474169
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+ dataset_size: 1095615
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: test
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+ path: data/test-*
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+ - split: validation
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+ path: data/validation-*
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  ---
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  # Dataset Card for "com_qa"
com_qa.py DELETED
@@ -1,122 +0,0 @@
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- """TODO(com_qa): Add a description here."""
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-
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-
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- import json
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-
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- import datasets
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-
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-
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- # TODO(com_qa): BibTeX citation
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- _CITATION = """\
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- @inproceedings{abujabal-etal-2019-comqa,
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- title = "{ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters",
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- author = {Abujabal, Abdalghani and
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- Saha Roy, Rishiraj and
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- Yahya, Mohamed and
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- Weikum, Gerhard},
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- booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
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- month = {jun},
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- year = {2019},
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- address = {Minneapolis, Minnesota},
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- publisher = {Association for Computational Linguistics},
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- url = {https://www.aclweb.org/anthology/N19-1027},
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- doi = {10.18653/v1/N19-1027{,
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- pages = {307--317},
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- }
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- """
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-
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- # TODO(com_qa):
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- _DESCRIPTION = """\
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- ComQA is a dataset of 11,214 questions, which were collected from WikiAnswers, a community question answering website.
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- By collecting questions from such a site we ensure that the information needs are ones of interest to actual users.
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- Moreover, questions posed there are often cannot be answered by commercial search engines or QA technology, making them
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- more interesting for driving future research compared to those collected from an engine's query log. The dataset contains
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- questions with various challenging phenomena such as the need for temporal reasoning, comparison (e.g., comparatives,
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- superlatives, ordinals), compositionality (multiple, possibly nested, subquestions with multiple entities), and
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- unanswerable questions (e.g., Who was the first human being on Mars?). Through a large crowdsourcing effort, questions
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- in ComQA are grouped into 4,834 paraphrase clusters that express the same information need. Each cluster is annotated
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- with its answer(s). ComQA answers come in the form of Wikipedia entities wherever possible. Wherever the answers are
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- temporal or measurable quantities, TIMEX3 and the International System of Units (SI) are used for normalization.
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- """
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-
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- _URL = "https://qa.mpi-inf.mpg.de/comqa/"
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- _URLS = {
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- "train": _URL + "comqa_train.json",
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- "dev": _URL + "comqa_dev.json",
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- "test": _URL + "comqa_test.json",
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- }
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-
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-
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- class ComQa(datasets.GeneratorBasedBuilder):
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- """TODO(com_qa): Short description of my dataset."""
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-
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- # TODO(com_qa): Set up version.
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- VERSION = datasets.Version("0.1.0")
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-
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- def _info(self):
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- # TODO(com_qa): Specifies the datasets.DatasetInfo object
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # datasets.features.FeatureConnectors
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- features=datasets.Features(
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- {
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- "cluster_id": datasets.Value("string"),
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- "questions": datasets.features.Sequence(datasets.Value("string")),
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- "answers": datasets.features.Sequence(datasets.Value("string")),
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- # These are the features of your dataset like images, labels ...
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- }
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- ),
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- # If there's a common (input, target) tuple from the features,
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- # specify them here. They'll be used if as_supervised=True in
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- # builder.as_dataset.
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- supervised_keys=None,
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- # Homepage of the dataset for documentation
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- homepage="http://qa.mpi-inf.mpg.de/comqa/",
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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- # TODO(com_qa): Downloads the data and defines the splits
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- # dl_manager is a datasets.download.DownloadManager that can be used to
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- # download and extract URLs
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- urls_to_download = _URLS
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- dl_dir = dl_manager.download_and_extract(urls_to_download)
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
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- ),
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- ]
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-
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- def _generate_examples(self, filepath, split):
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- """Yields examples."""
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- # TODO(com_qa): Yields (key, example) tuples from the dataset
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- with open(filepath, encoding="utf-8") as f:
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- data = json.load(f)
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- for id_, example in enumerate(data):
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- questions = []
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- if split == "test":
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- cluster_id = str(example["id"])
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- questions.append(example["question"])
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- else:
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- cluster_id = example["cluster_id"]
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- questions = example["questions"]
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- answers = example["answers"]
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- yield id_, {
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- "cluster_id": cluster_id,
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- "questions": questions,
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- "answers": answers,
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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