Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

@@ -0,0 +1 @@
+{"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\u00c3\u00a9d\u00c3\u00a9ric  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"}}, "supervised_keys": null, "builder_name": "piaf", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3337705, "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, "dataset_size": 3337705, "size_in_bytes": 4708089}}

dummy/plain_text/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3c038bf8d5ce829fa28d4cdf2bbf63793427a1785b53ee234880d2be64fe2ae
+size 664

piaf.py

@@ -0,0 +1,134 @@
+# 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"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import logging
+import os
+
+import datasets
+
+
+_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.
+"""
+
+
+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."""
+
+    _URL = "https://github.com/etalab-ia/piaf-code/raw/master/"
+    _TRAINING_FILE = "piaf-v1.0.json"
+
+    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,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = {"train": os.path.join(self._URL, self._TRAINING_FILE)}
+        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."""
+        logging.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,
+                            },
+                        }