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": "\nQASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice \nquestions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.\n", "citation": "@article{allenai:qasc,\n      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},\n      title     = {QASC: A Dataset for Question Answering via Sentence Composition},\n      journal   = {arXiv:1910.11473v2},\n      year      = {2020},\n}\n", "homepage": "https://allenai.org/data/qasc", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}, "fact1": {"dtype": "string", "id": null, "_type": "Value"}, "fact2": {"dtype": "string", "id": null, "_type": "Value"}, "combinedfact": {"dtype": "string", "id": null, "_type": "Value"}, "formatted_question": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "qasc", "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": 393683, "num_examples": 920, "dataset_name": "qasc"}, "train": {"name": "train", "num_bytes": 4919377, "num_examples": 8134, "dataset_name": "qasc"}, "validation": {"name": "validation", "num_bytes": 562352, "num_examples": 926, "dataset_name": "qasc"}}, "download_checksums": {"http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz": {"num_bytes": 1616514, "checksum": "a7b3f2244f768974c609fd621346c931a72715609f171cb5544fc1da2a2ad55c"}}, "download_size": 1616514, "dataset_size": 5875412, "size_in_bytes": 7491926}}

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:19282b8004914c942e33e4da11227b3010552cc931b383c5bc59751c262db1d7
+size 2427

qasc.py

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+"""TODO(qasc): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(qasc): BibTeX citation
+_CITATION = """\
+@article{allenai:qasc,
+      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
+      title     = {QASC: A Dataset for Question Answering via Sentence Composition},
+      journal   = {arXiv:1910.11473v2},
+      year      = {2020},
+}
+"""
+
+# TODO(qasc):
+_DESCRIPTION = """
+QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
+questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
+"""
+_URl = "http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz"
+
+
+class Qasc(datasets.GeneratorBasedBuilder):
+    """TODO(qasc): Short description of my dataset."""
+
+    # TODO(qasc): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(qasc): 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"),
+                    "question": datasets.Value("string"),
+                    "choices": datasets.features.Sequence(
+                        {"text": datasets.Value("string"), "label": datasets.Value("string")}
+                    ),
+                    "answerKey": datasets.Value("string"),
+                    "fact1": datasets.Value("string"),
+                    "fact2": datasets.Value("string"),
+                    "combinedfact": datasets.Value("string"),
+                    "formatted_question": 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="https://allenai.org/data/qasc",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(qasc): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URl)
+        data_dir = os.path.join(dl_dir, "QASC_Dataset")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "train.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "dev.jsonl")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(qasc): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            for row in f:
+                data = json.loads(row)
+                answerkey = data.get("answerKey", "")
+                id_ = data["id"]
+                question = data["question"]["stem"]
+                choices = data["question"]["choices"]
+                text_choices = [choice["text"] for choice in choices]
+                label_choices = [choice["label"] for choice in choices]
+                fact1 = data.get("fact1", "")
+                fact2 = data.get("fact2", "")
+                combined_fact = data.get("combinedfact", "")
+                formatted_question = data.get("formatted_question", "")
+                yield id_, {
+                    "id": id_,
+                    "answerKey": answerkey,
+                    "question": question,
+                    "choices": {"text": text_choices, "label": label_choices},
+                    "fact1": fact1,
+                    "fact2": fact2,
+                    "combinedfact": combined_fact,
+                    "formatted_question": formatted_question,
+                }