add files

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* 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
+corpus.jsonl.gz filter=lfs diff=lfs merge=lfs -text

corpus.jsonl.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7b7529ffde338c3a921289678edbc6022beef3d505985edb346bfa78e0137c75
+size 2756565

scifact-corpus.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""SciFact Dataset (Retrieval Only)"""
+
+import json
+
+import datasets
+
+_CITATION = """
+@inproceedings{Wadden2020FactOF,
+  title={Fact or Fiction: Verifying Scientific Claims},
+  author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
+  booktitle={EMNLP},
+  year={2020},
+}
+"""
+
+_DESCRIPTION = "dataset load script for SciFact"
+
+_DATASET_URLS = {
+    'train': "https://huggingface.co/datasets/tevatron/scifact/resolve/main/corpus.jsonl.gz",
+}
+
+
+class ScifactCorpus(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(version=VERSION,
+                               description="SciFact Corpus"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
+             'title': datasets.Value('string')},
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="",
+            # License for the dataset if available
+            license="",
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
+        splits = [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                },
+            ),
+        ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                data = json.loads(line)
+                yield data['docid'], data