Update files from the datasets library (from 1.0.0)

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

.gitattributes

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+*.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

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+{"default": {"description": "FQuAD: French Question Answering Dataset\nWe introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.\nFinetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.\n\n", "citation": "@ARTICLE{2020arXiv200206071\n       author = {Martin, d'Hoffschmidt and Maxime, Vidal and\n         Wacim, Belblidia and Tom, Brendl\u00e9},\n        title = \"{FQuAD: French Question Answering Dataset}\",\n      journal = {arXiv e-prints},\n     keywords = {Computer Science - Computation and Language},\n         year = \"2020\",\n        month = \"Feb\",\n          eid = {arXiv:2002.06071},\n        pages = {arXiv:2002.06071},\narchivePrefix = {arXiv},\n       eprint = {2002.06071},\n primaryClass = {cs.CL}\n}\n", "homepage": "https://fquad.illuin.tech/", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answers": {"feature": {"texts": {"dtype": "string", "id": null, "_type": "Value"}, "answers_starts": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "fquad", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5910248, "num_examples": 4921, "dataset_name": "fquad"}, "validation": {"name": "validation", "num_bytes": 1033253, "num_examples": 768, "dataset_name": "fquad"}}, "download_checksums": {"https://storage.googleapis.com/illuin/fquad/train.json.zip": {"num_bytes": 2813123, "checksum": "64f0aea68bacee6ffca7f2f7d56a97e504b2ad2abce057ab6c14768a72c09b47"}, "https://storage.googleapis.com/illuin/fquad/valid.json.zip": {"num_bytes": 479113, "checksum": "53eb7f33573f619f6d56f9e656c6ea6030639ebd663bb445b86b999123be1ef3"}}, "download_size": 3292236, "dataset_size": 6943501, "size_in_bytes": 10235737}}

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3cbda7105c329d43952fea07a739e9d27b739bb0f26867e792e9bcd6f26d7fb
+size 6764

fquad.py

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+"""TODO(fquad): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(fquad): BibTeX citation
+_CITATION = """\
+@ARTICLE{2020arXiv200206071
+       author = {Martin, d'Hoffschmidt and Maxime, Vidal and
+         Wacim, Belblidia and Tom, Brendlé},
+        title = "{FQuAD: French Question Answering Dataset}",
+      journal = {arXiv e-prints},
+     keywords = {Computer Science - Computation and Language},
+         year = "2020",
+        month = "Feb",
+          eid = {arXiv:2002.06071},
+        pages = {arXiv:2002.06071},
+archivePrefix = {arXiv},
+       eprint = {2002.06071},
+ primaryClass = {cs.CL}
+}
+"""
+
+# TODO(fquad):
+_DESCRIPTION = """\
+FQuAD: French Question Answering Dataset
+We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.
+Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.
+
+"""
+_URL = "https://storage.googleapis.com/illuin/fquad"
+_TRAIN_DATA = "train.json.zip"
+_VALID_DATA = "valid.json.zip"
+
+
+class Fquad(datasets.GeneratorBasedBuilder):
+    """TODO(fquad): Short description of my dataset."""
+
+    # TODO(fquad): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(fquad): Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "context": datasets.Value("string"),
+                    "questions": datasets.features.Sequence(datasets.Value("string")),
+                    "answers": datasets.features.Sequence(
+                        {"texts": datasets.Value("string"), "answers_starts": datasets.Value("int32")}
+                    ),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+            # 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="https://fquad.illuin.tech/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(fquad): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        download_urls = {"train": os.path.join(_URL, _TRAIN_DATA), "valid": os.path.join(_URL, _VALID_DATA)}
+        dl_dir = dl_manager.download_and_extract(download_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(dl_dir["train"], "train.json")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(dl_dir["valid"], "valid.json")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+
+        """Yields examples."""
+        # TODO(fquad): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for id1, examples in enumerate(data["data"]):
+                for id2, example in enumerate(examples["paragraphs"]):
+                    questions = [question["question"] for question in example["qas"]]
+                    answers = [answer["answers"] for answer in example["qas"]]
+                    texts = [answer[0]["text"] for answer in answers]
+                    answers_starts = [answer[0]["answer_start"] for answer in answers]
+
+                    yield str(id1) + "_" + str(id2), {
+                        "context": example["context"],
+                        "questions": questions,
+                        "answers": {"texts": texts, "answers_starts": answers_starts},
+                    }