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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+{"corpus": {"description": "SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales\n", "citation": "@inproceedings{scifact2020\n  title={ Fact or Fiction: Verifying Scientific Claims},\n  author={David,  Wadden and Kyle, Lo and Lucy Lu, Wang and Shanchuan, Lin and Madeleine van, Zuylen and Arman, Cohan and  Hannaneh, Hajishirzi},\n  booktitle={2011 AAAI Spring Symposium Series},\n  year={2020},\n}\n", "homepage": "https://scifact.apps.allenai.org/", "license": "", "features": {"doc_id": {"dtype": "int32", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "structured": {"dtype": "bool", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scifact", "config_name": "corpus", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8002556, "num_examples": 5183, "dataset_name": "scifact"}}, "download_checksums": {"https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz": {"num_bytes": 2848693, "checksum": "108c19d1a6e2522f20055fe503450c6af8c9c12a095fd727f21894cc44eb47aa"}}, "download_size": 2848693, "dataset_size": 8002556, "size_in_bytes": 10851249}, "claims": {"description": "SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales\n", "citation": "@inproceedings{scifact2020\n  title={ Fact or Fiction: Verifying Scientific Claims},\n  author={David,  Wadden and Kyle, Lo and Lucy Lu, Wang and Shanchuan, Lin and Madeleine van, Zuylen and Arman, Cohan and  Hannaneh, Hajishirzi},\n  booktitle={2011 AAAI Spring Symposium Series},\n  year={2020},\n}\n", "homepage": "https://scifact.apps.allenai.org/", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_doc_id": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_label": {"dtype": "string", "id": null, "_type": "Value"}, "evidence_sentences": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "cited_doc_ids": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "scifact", "config_name": "claims", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 170070, "num_examples": 1261, "dataset_name": "scifact"}, "test": {"name": "test", "num_bytes": 33899, "num_examples": 300, "dataset_name": "scifact"}, "validation": {"name": "validation", "num_bytes": 60882, "num_examples": 450, "dataset_name": "scifact"}}, "download_checksums": {"https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz": {"num_bytes": 2848693, "checksum": "108c19d1a6e2522f20055fe503450c6af8c9c12a095fd727f21894cc44eb47aa"}}, "download_size": 2848693, "dataset_size": 264851, "size_in_bytes": 3113544}}

dummy/claims/1.0.0/dummy_data.zip

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+size 1659

dummy/corpus/1.0.0/dummy_data.zip

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+oid sha256:0854510f54a4589c062b8f02f2afc868fd310caaa622a452385792b2e940c52d
+size 2197

scifact.py

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+"""TODO(scifact): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(scifact): BibTeX citation
+_CITATION = """\
+@inproceedings{scifact2020
+  title={ Fact or Fiction: Verifying Scientific Claims},
+  author={David,  Wadden and Kyle, Lo and Lucy Lu, Wang and Shanchuan, Lin and Madeleine van, Zuylen and Arman, Cohan and  Hannaneh, Hajishirzi},
+  booktitle={2011 AAAI Spring Symposium Series},
+  year={2020},
+}
+"""
+
+# TODO(scifact):
+_DESCRIPTION = """\
+SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales
+"""
+
+_URL = "https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz"
+
+
+class ScifactConfig(datasets.BuilderConfig):
+    """BuilderConfig for Scifact"""
+
+    def __init__(self, **kwargs):
+        """
+
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(ScifactConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class Scifact(datasets.GeneratorBasedBuilder):
+    """TODO(scifact): Short description of my dataset."""
+
+    # TODO(scifact): Set up version.
+    VERSION = datasets.Version("0.1.0")
+    BUILDER_CONFIGS = [
+        ScifactConfig(name="corpus", description=" The corpus of evidence documents"),
+        ScifactConfig(name="claims", description=" The claims are split into train, test, dev"),
+    ]
+
+    def _info(self):
+        # TODO(scifact): Specifies the datasets.DatasetInfo object
+        if self.config.name == "corpus":
+            features = {
+                "doc_id": datasets.Value("int32"),  # The document's S2ORC ID.
+                "title": datasets.Value("string"),  # The title.
+                "abstract": datasets.features.Sequence(
+                    datasets.Value("string")
+                ),  # The abstract, written as a list of sentences.
+                "structured": datasets.Value("bool"),  # Indicator for whether this is a structured abstract.
+            }
+        else:
+            features = {
+                "id": datasets.Value("int32"),  # An integer claim ID.
+                "claim": datasets.Value("string"),  # The text of the claim.
+                "evidence_doc_id": datasets.Value("string"),
+                "evidence_label": datasets.Value("string"),  # Label for the rationale.
+                "evidence_sentences": datasets.features.Sequence(datasets.Value("int32")),  # Rationale sentences.
+                "cited_doc_ids": datasets.features.Sequence(datasets.Value("int32")),  # The claim's "cited documents".
+            }
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                features
+                # 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://scifact.apps.allenai.org/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(scifact): 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)
+
+        if self.config.name == "corpus":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"filepath": os.path.join(dl_dir, "data", "corpus.jsonl"), "split": "train"},
+                ),
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_train.jsonl"), "split": "train"},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_test.jsonl"), "split": "test"},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_dev.jsonl"), "split": "dev"},
+                ),
+            ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        # TODO(scifact): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "corpus":
+                    yield id_, {
+                        "doc_id": int(data["doc_id"]),
+                        "title": data["title"],
+                        "abstract": data["abstract"],
+                        "structured": data["structured"],
+                    }
+                else:
+                    if split == "test":
+                        yield id_, {
+                            "id": data["id"],
+                            "claim": data["claim"],
+                            "evidence_doc_id": "",
+                            "evidence_label": "",
+                            "evidence_sentences": [],
+                            "cited_doc_ids": [],
+                        }
+                    else:
+                        evidences = data["evidence"]
+                        if evidences:
+                            for id1, doc_id in enumerate(evidences):
+                                for id2, evidence in enumerate(evidences[doc_id]):
+                                    yield str(id_) + "_" + str(id1) + "_" + str(id2), {
+                                        "id": data["id"],
+                                        "claim": data["claim"],
+                                        "evidence_doc_id": doc_id,
+                                        "evidence_label": evidence["label"],
+                                        "evidence_sentences": evidence["sentences"],
+                                        "cited_doc_ids": data.get("cited_doc_ids", []),
+                                    }
+                        else:
+                            yield id_, {
+                                "id": data["id"],
+                                "claim": data["claim"],
+                                "evidence_doc_id": "",
+                                "evidence_label": "",
+                                "evidence_sentences": [],
+                                "cited_doc_ids": data.get("cited_doc_ids", []),
+                            }