Convert dataset to Parquet

README.md

@@ -5,8 +5,6 @@ language_creators:
 - crowdsourced
 language:
 - fr
-language_bcp47:
-- fr-FR
 license:
 - mit
 multilinguality:
@@ -20,9 +18,11 @@ task_categories:
 task_ids:
 - extractive-qa
 - open-domain-qa
-paperswithcode_id: null
 pretty_name: Piaf
+language_bcp47:
+- fr-FR
 dataset_info:
+  config_name: plain_text
   features:
   - name: id
     dtype: string
@@ -38,13 +38,18 @@ dataset_info:
       dtype: string
     - name: answer_start
       dtype: int32
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 3332905
+    num_bytes: 3332877
     num_examples: 3835
-  download_size: 1370384
-  dataset_size: 3332905
+  download_size: 650352
+  dataset_size: 3332877
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  default: true
 ---
 
 # Dataset Card for Piaf

dataset_infos.json

@@ -1 +0,0 @@
-{"plain_text": {"description": "Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.\n", "citation": "@InProceedings{keraron-EtAl:2020:LREC,\n  author    = {Keraron, Rachel  and  Lancrenon, Guillaume  and  Bras, Mathilde  and  Allary, Fr\u00e9d\u00e9ric  and  Moyse, Gilles  and  Scialom, Thomas  and  Soriano-Morales, Edmundo-Pavel  and  Staiano, Jacopo},\n  title     = {Project PIAF: Building a Native French Question-Answering Dataset},\n  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},\n  month          = {May},\n  year           = {2020},\n  address        = {Marseille, France},\n  publisher      = {European Language Resources Association},\n  pages     = {5483--5492},\n  abstract  = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},\n  url       = {https://www.aclweb.org/anthology/2020.lrec-1.673}\n}\n", "homepage": "https://piaf.etalab.studio", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "piaf", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3332905, "num_examples": 3835, "dataset_name": "piaf"}}, "download_checksums": {"https://github.com/etalab-ia/piaf-code/raw/master/piaf-v1.0.json": {"num_bytes": 1370384, "checksum": "008229ccefa0195d7e809d777d33149cab03433059c9477bdbadb4838a277cd2"}}, "download_size": 1370384, "post_processing_size": null, "dataset_size": 3332905, "size_in_bytes": 4703289}}

piaf.py

@@ -1,139 +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
-"""PIAF Question Answering Dataset"""
-
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@InProceedings{keraron-EtAl:2020:LREC,
-  author    = {Keraron, Rachel  and  Lancrenon, Guillaume  and  Bras, Mathilde  and  Allary, Frédéric  and  Moyse, Gilles  and  Scialom, Thomas  and  Soriano-Morales, Edmundo-Pavel  and  Staiano, Jacopo},
-  title     = {Project PIAF: Building a Native French Question-Answering Dataset},
-  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},
-  month          = {May},
-  year           = {2020},
-  address        = {Marseille, France},
-  publisher      = {European Language Resources Association},
-  pages     = {5483--5492},
-  abstract  = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},
-  url       = {https://www.aclweb.org/anthology/2020.lrec-1.673}
-}
-"""
-
-_DESCRIPTION = """\
-Piaf is a reading comprehension \
-dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.
-"""
-
-_URLS = {"train": "https://github.com/etalab-ia/piaf-code/raw/master/piaf-v1.0.json"}
-
-
-class PiafConfig(datasets.BuilderConfig):
-    """BuilderConfig for PIAF."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for PIAF.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(PiafConfig, self).__init__(**kwargs)
-
-
-class Piaf(datasets.GeneratorBasedBuilder):
-    """The Piaf Question Answering Dataset. Version 1.0."""
-
-    BUILDER_CONFIGS = [
-        PiafConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            homepage="https://piaf.etalab.studio",
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            dataset = json.load(f)
-            for article in dataset["data"]:
-                title = article.get("title", "").strip()
-                for paragraph in article["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"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "title": title,
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }

plain_text/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:868b00642fd947182dd406becee1f7981c4ab69c2dc78ee5f0e6e7a42d98ebad
+size 650352