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

cmrc2018.py

@@ -0,0 +1,118 @@
+"""TODO(cmrc2018): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+# TODO(cmrc2018): BibTeX citation
+_CITATION = """\
+@inproceedings{cui-emnlp2019-cmrc2018,
+    title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension},
+    author = {Cui, Yiming  and
+      Liu, Ting  and
+      Che, Wanxiang  and
+      Xiao, Li  and
+      Chen, Zhipeng  and
+      Ma, Wentao  and
+      Wang, Shijin  and
+      Hu, Guoping},
+    booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
+    month = {nov},
+    year = {2019},
+    address = {Hong Kong, China},
+    publisher = {Association for Computational Linguistics},
+    url = {https://www.aclweb.org/anthology/D19-1600},
+    doi = {10.18653/v1/D19-1600},
+    pages = {5886--5891}}
+"""
+
+# TODO(cmrc2018):
+_DESCRIPTION = """\
+A Span-Extraction dataset for Chinese machine reading comprehension to add language
+diversities in this area. The dataset is composed by near 20,000 real questions annotated
+on Wikipedia paragraphs by human experts. We also annotated a challenge set which
+contains the questions that need comprehensive understanding and multi-sentence
+inference throughout the context.
+"""
+_URL = "https://github.com/ymcui/cmrc2018"
+_TRAIN_FILE = "https://worksheets.codalab.org/rest/bundles/0x15022f0c4d3944a599ab27256686b9ac/contents/blob/"
+_DEV_FILE = "https://worksheets.codalab.org/rest/bundles/0x72252619f67b4346a85e122049c3eabd/contents/blob/"
+_TEST_FILE = "https://worksheets.codalab.org/rest/bundles/0x182c2e71fac94fc2a45cc1a3376879f7/contents/blob/"
+
+
+class Cmrc2018(datasets.GeneratorBasedBuilder):
+    """TODO(cmrc2018): Short description of my dataset."""
+
+    # TODO(cmrc2018): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(cmrc2018): 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(
+                {
+                    "id": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": 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=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(cmrc2018): 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": _TRAIN_FILE, "dev": _DEV_FILE, "test": _TEST_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"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(cmrc2018): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for example in data["data"]:
+                for paragraph in example["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"]]
+
+                        yield id_, {
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }

dataset_infos.json

@@ -0,0 +1 @@
+{"default": {"description": "A Span-Extraction dataset for Chinese machine reading comprehension to add language\ndiversities in this area. The dataset is composed by near 20,000 real questions annotated\non Wikipedia paragraphs by human experts. We also annotated a challenge set which\ncontains the questions that need comprehensive understanding and multi-sentence\ninference throughout the context.\n", "citation": "@inproceedings{cui-emnlp2019-cmrc2018,\n    title = \"A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension\",\n    author = \"Cui, Yiming  and\n      Liu, Ting  and\n      Che, Wanxiang  and\n      Xiao, Li  and\n      Chen, Zhipeng  and\n      Ma, Wentao  and\n      Wang, Shijin  and\n      Hu, Guoping\",\n    booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)\",\n    month = nov,\n    year = \"2019\",\n    address = \"Hong Kong, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D19-1600\",\n    doi = \"10.18653/v1/D19-1600\",\n    pages = \"5886--5891\",\n}\n", "homepage": "https://github.com/ymcui/cmrc2018", "license": "", "features": {"id": {"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": "cmrc2018", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1608065, "num_examples": 1002, "dataset_name": "cmrc2018"}, "train": {"name": "train", "num_bytes": 15519498, "num_examples": 10142, "dataset_name": "cmrc2018"}, "validation": {"name": "validation", "num_bytes": 5189046, "num_examples": 3219, "dataset_name": "cmrc2018"}}, "download_checksums": {"https://worksheets.codalab.org/rest/bundles/0x15022f0c4d3944a599ab27256686b9ac/contents/blob/": {"num_bytes": 7408757, "checksum": "5497aa2f81908e31d6b0e27d99b1f90ab63a8f58fa92fffe5d17cf62eba0c212"}, "https://worksheets.codalab.org/rest/bundles/0x72252619f67b4346a85e122049c3eabd/contents/blob/": {"num_bytes": 3299139, "checksum": "e9ff74231f05c230c6fa88b84441ee334d97234cbb610991cd94b82db00c7f1f"}, "https://worksheets.codalab.org/rest/bundles/0x182c2e71fac94fc2a45cc1a3376879f7/contents/blob/": {"num_bytes": 800221, "checksum": "f3fae95b57da8e03afb2b57467dd221417060ef4d82db13bf22fc88589f3a6f3"}}, "download_size": 11508117, "dataset_size": 22316609, "size_in_bytes": 33824726}}

dummy/0.1.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22c8d3196864a039d66d93c314ce4581017ef357dc18682d15c7e270eaa04c95
+size 3944