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

cosmos_qa.py

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+"""TODO(cosmos_qa): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO(cosmos_qa): BibTeX citation
+_CITATION = """\
+@inproceedings{cosmos,
+    title={COSMOS QA: Machine Reading Comprehension
+    with Contextual Commonsense Reasoning},
+    author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
+    booktitle ={arXiv:1909.00277v2},
+    year={2019}
+}
+"""
+
+# TODO(cosmos_qa):
+_DESCRIPTION = """\
+Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context
+"""
+_URL = "https://github.com/wilburOne/cosmosqa/raw/master/data/"
+_TEST_FILE = "test.jsonl"
+_TRAIN_FILE = "train.csv"
+_DEV_FILE = "valid.csv"
+
+
+class CosmosQa(datasets.GeneratorBasedBuilder):
+    """TODO(cosmos_qa): Short description of my dataset."""
+
+    # TODO(cosmos_qa): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(cosmos_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(
+                {
+                    "id": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer0": datasets.Value("string"),
+                    "answer1": datasets.Value("string"),
+                    "answer2": datasets.Value("string"),
+                    "answer3": datasets.Value("string"),
+                    "label": 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://wilburone.github.io/cosmos/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(cosmos_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),
+            "test": os.path.join(_URL, _TEST_FILE),
+            "dev": os.path.join(_URL, _DEV_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(cosmos_qa): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            if split == "test":
+                for id_, row in enumerate(f):
+                    data = json.loads(row)
+                    yield id_, {
+                        "id": data["id"],
+                        "context": data["context"],
+                        "question": data["question"],
+                        "answer0": data["answer0"],
+                        "answer1": data["answer1"],
+                        "answer2": data["answer2"],
+                        "answer3": data["answer3"],
+                        "label": int(data.get("label", -1)),
+                    }
+            else:
+                data = csv.DictReader(f)
+                for id_, row in enumerate(data):
+                    yield id_, {
+                        "id": row["id"],
+                        "context": row["context"],
+                        "question": row["question"],
+                        "answer0": row["answer0"],
+                        "answer1": row["answer1"],
+                        "answer2": row["answer2"],
+                        "answer3": row["answer3"],
+                        "label": int(row.get("label", -1)),
+                    }

dataset_infos.json

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+{"default": {"description": "Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context\n", "citation": "@inproceedings{cosmos,\n    title={COSMOS QA: Machine Reading Comprehension\n    with Contextual Commonsense Reasoning},\n    author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},\n    booktitle ={arXiv:1909.00277v2},\n    year={2019}\n}\n", "homepage": "https://wilburone.github.io/cosmos/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer0": {"dtype": "string", "id": null, "_type": "Value"}, "answer1": {"dtype": "string", "id": null, "_type": "Value"}, "answer2": {"dtype": "string", "id": null, "_type": "Value"}, "answer3": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "cosmos_qa", "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": 5128447, "num_examples": 6963, "dataset_name": "cosmos_qa"}, "train": {"name": "train", "num_bytes": 17185126, "num_examples": 25262, "dataset_name": "cosmos_qa"}, "validation": {"name": "validation", "num_bytes": 2189979, "num_examples": 2985, "dataset_name": "cosmos_qa"}}, "download_checksums": {"https://github.com/wilburOne/cosmosqa/raw/master/data/train.csv": {"num_bytes": 16660449, "checksum": "d8d5ca1f9f6534b6530550718591af89372d976a8fc419360fab4158dee4d0b2"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/test.jsonl": {"num_bytes": 5610681, "checksum": "70005196dc2588b95de34f1657b25e2c1a4810cfe55b5bb0c0e15580c37b3ed0"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/valid.csv": {"num_bytes": 2128345, "checksum": "a6a94fc1463ca82bb10f98ef68ed535405e6f5c36e044ff8e136b5c19dea63f3"}}, "download_size": 24399475, "dataset_size": 24503552, "size_in_bytes": 48903027}}

dummy/0.1.0/dummy_data.zip

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+oid sha256:c83fe07c5e4cc1381a999258f8e787c735a0d763b10b9436ed0f0bafc0393f00
+size 6688