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

com_qa.py

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+"""TODO(com_qa): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(com_qa): BibTeX citation
+_CITATION = """\
+@inproceedings{abujabal-etal-2019-comqa,
+    title = "{ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters",
+    author = {Abujabal, Abdalghani  and
+      Saha Roy, Rishiraj  and
+      Yahya, Mohamed  and
+      Weikum, Gerhard},
+    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)},
+    month = {jun},
+    year = {2019},
+    address = {Minneapolis, Minnesota},
+    publisher = {Association for Computational Linguistics},
+    url = {https://www.aclweb.org/anthology/N19-1027},
+    doi = {10.18653/v1/N19-1027{,
+    pages = {307--317},
+    }
+"""
+
+# TODO(com_qa):
+_DESCRIPTION = """\
+ComQA is a dataset of 11,214 questions, which were collected from WikiAnswers, a community question answering website.
+By collecting questions from such a site we ensure that the information needs are ones of interest to actual users.
+Moreover, questions posed there are often cannot be answered by commercial search engines or QA technology, making them
+more interesting for driving future research compared to those collected from an engine's query log. The dataset contains
+questions with various challenging phenomena such as the need for temporal reasoning, comparison (e.g., comparatives,
+superlatives, ordinals), compositionality (multiple, possibly nested, subquestions with multiple entities), and
+unanswerable questions (e.g., Who was the first human being on Mars?). Through a large crowdsourcing effort, questions
+in ComQA are grouped into 4,834 paraphrase clusters that express the same information need. Each cluster is annotated
+with its answer(s). ComQA answers come in the form of Wikipedia entities wherever possible. Wherever the answers are
+temporal or measurable quantities, TIMEX3 and the International System of Units (SI) are used for normalization.
+"""
+_URL = "https://qa.mpi-inf.mpg.de/comqa"
+_TRAIN_FILE = "comqa_train.json"
+_DEV_FILE = "comqa_dev.json"
+_TEST_FILE = "comqa_test.json"
+
+
+class ComQa(datasets.GeneratorBasedBuilder):
+    """TODO(com_qa): Short description of my dataset."""
+
+    # TODO(com_qa): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(com_qa): 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(
+                {
+                    "cluster_id": datasets.Value("string"),
+                    "questions": datasets.features.Sequence(datasets.Value("string")),
+                    "answers": datasets.features.Sequence(datasets.Value("string")),
+                    # 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="http://qa.mpi-inf.mpg.de/comqa/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(com_qa): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        urls_to_download = {
+            "train": os.path.join(_URL, _TRAIN_FILE),
+            "dev": os.path.join(_URL, _DEV_FILE),
+            "test": os.path.join(_URL, _TEST_FILE),
+        }
+        dl_dir = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        # TODO(com_qa): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for id_, example in enumerate(data):
+                questions = []
+                if split == "test":
+                    cluster_id = str(example["id"])
+                    questions.append(example["question"])
+                else:
+                    cluster_id = example["cluster_id"]
+                    questions = example["questions"]
+                answers = example["answers"]
+                yield id_, {
+                    "cluster_id": cluster_id,
+                    "questions": questions,
+                    "answers": answers,
+                }

dataset_infos.json

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+{"default": {"description": "ComQA is a dataset of 11,214 questions, which were collected from WikiAnswers, a community question answering website. \nBy collecting questions from such a site we ensure that the information needs are ones of interest to actual users. \nMoreover, questions posed there are often cannot be answered by commercial search engines or QA technology, making them \nmore interesting for driving future research compared to those collected from an engine's query log. The dataset contains \nquestions with various challenging phenomena such as the need for temporal reasoning, comparison (e.g., comparatives, \nsuperlatives, ordinals), compositionality (multiple, possibly nested, subquestions with multiple entities), and \nunanswerable questions (e.g., Who was the first human being on Mars?). Through a large crowdsourcing effort, questions \nin ComQA are grouped into 4,834 paraphrase clusters that express the same information need. Each cluster is annotated \nwith its answer(s). ComQA answers come in the form of Wikipedia entities wherever possible. Wherever the answers are \ntemporal or measurable quantities, TIMEX3 and the International System of Units (SI) are used for normalization.\n", "citation": "@inproceedings{abujabal-etal-2019-comqa,\n    title = \"{ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters\",\n    author = {Abujabal, Abdalghani  and\n      Saha Roy, Rishiraj  and\n      Yahya, Mohamed  and\n      Weikum, Gerhard},\n    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)},\n    month = {jun},\n    year = {2019},\n    address = {Minneapolis, Minnesota},\n    publisher = {Association for Computational Linguistics},\n    url = {https://www.aclweb.org/anthology/N19-1027},\n    doi = {10.18653/v1/N19-1027{,\n    pages = {307--317},\n    }\n", "homepage": "http://qa.mpi-inf.mpg.de/comqa/", "license": "", "features": {"cluster_id": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "com_qa", "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": 696645, "num_examples": 3966, "dataset_name": "com_qa"}, "test": {"name": "test", "num_bytes": 273384, "num_examples": 2243, "dataset_name": "com_qa"}, "validation": {"name": "validation", "num_bytes": 131945, "num_examples": 966, "dataset_name": "com_qa"}}, "download_checksums": {"https://qa.mpi-inf.mpg.de/comqa/comqa_train.json": {"num_bytes": 1054334, "checksum": "aa25b2747221a6147737066fa7f8509414a6333e6eebb20d7970fc50166eba9c"}, "https://qa.mpi-inf.mpg.de/comqa/comqa_dev.json": {"num_bytes": 213246, "checksum": "937f898f996e9a7317d311553cf5b43c4a53e0bb841ae8eed4d8cf15936fbc84"}, "https://qa.mpi-inf.mpg.de/comqa/comqa_test.json": {"num_bytes": 404104, "checksum": "b81e19c4108198781167ddb30d26172571e76cc8e684aabb181537a45e816867"}}, "download_size": 1671684, "dataset_size": 1101974, "size_in_bytes": 2773658}}

dummy/0.1.0/dummy_data.zip

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+size 1300