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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+{"plain_text": {"description": "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.\n", "citation": "@article{2016arXiv160605250R,\n       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n                 Konstantin and {Liang}, Percy},\n        title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n      journal = {arXiv e-prints},\n         year = 2016,\n          eid = {arXiv:1606.05250},\n        pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n       eprint = {1606.05250},\n}\n", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "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": "squad", "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": 79426386, "num_examples": 87599, "dataset_name": "squad"}, "validation": {"name": "validation", "num_bytes": 10491883, "num_examples": 10570, "dataset_name": "squad"}}, "download_checksums": {"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json": {"num_bytes": 30288272, "checksum": "3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955"}, "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json": {"num_bytes": 4854279, "checksum": "95aa6a52d5d6a735563366753ca50492a658031da74f301ac5238b03966972c9"}}, "download_size": 35142551, "dataset_size": 89918269, "size_in_bytes": 125060820}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5248bc997c6de0a053ebd1f73cc2fc94900bd7ddcb0483ebfabf4785ab938ef1
+size 1502

dummy/plain_text/1.0.0/dummy_data/dev

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+{
+	"data": [	
+		{ "title": "dev test",
+			"paragraphs": [
+				{ "context": "This is a test context.",
+					"qas": [
+						{ "question": "Is this a test?",
+							"id": "2",
+							"answers": [
+								{ "answer_start": 6,
+								  "text": "This is a test text"
+								}
+							]
+						}
+					]
+		   	}
+			]
+		}
+	]
+}
+
+
+
+
+

dummy/plain_text/1.0.0/dummy_data/train

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+{
+	"data": [	
+		{ "title": "train test",
+			"paragraphs": [
+				{ "context": "This is a test context.",
+					"qas": [
+						{ "question": "Is this a test?",
+							"id": "1",
+							"answers": [
+								{ "answer_start": 1,
+								  "text": "This is a test text"
+								}
+							]
+						}
+					]
+		   	}
+			]
+		}
+	]
+}
+
+
+
+
+

squad.py

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+# 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
+"""SQUAD: The Stanford Question Answering Dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import logging
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+"""
+
+_DESCRIPTION = """\
+Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
+dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
+articles, where the answer to every question is a segment of text, or span, \
+from the corresponding reading passage, or the question might be unanswerable.
+"""
+
+
+class SquadConfig(datasets.BuilderConfig):
+    """BuilderConfig for SQUAD."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQUAD.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadConfig, self).__init__(**kwargs)
+
+
+class Squad(datasets.GeneratorBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+    _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
+    _DEV_FILE = "dev-v1.1.json"
+    _TRAINING_FILE = "train-v1.1.json"
+
+    BUILDER_CONFIGS = [
+        SquadConfig(
+            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://rajpurkar.github.io/SQuAD-explorer/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = {
+            "train": os.path.join(self._URL, self._TRAINING_FILE),
+            "dev": os.path.join(self._URL, self._DEV_FILE),
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+        ]
+
+    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:
+            squad = json.load(f)
+            for article in squad["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,
+                            },
+                        }