Delete scifact.py

scifact.py

@@ -1,110 +0,0 @@
-# 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/train.jsonl.gz",
-    'dev': "https://huggingface.co/datasets/Tevatron/scifact/resolve/main/dev.jsonl.gz",
-    'test': "https://huggingface.co/datasets/Tevatron/scifact/resolve/main/test.jsonl.gz",
-}
-
-
-class Scifact(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(version=VERSION,
-                               description="SciFact train/dev/test datasets"),
-    ]
-
-    def _info(self):
-        features = datasets.Features({
-            'query_id': datasets.Value('string'),
-            'query': datasets.Value('string'),
-            'positive_passages': [
-                {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
-                 'title': datasets.Value('string')}
-            ],
-            'negative_passages': [
-                {'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"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name='dev',
-                gen_kwargs={
-                    "filepath": downloaded_files["dev"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name='test',
-                gen_kwargs={
-                    "filepath": downloaded_files["test"],
-                },
-            ),
-        ]
-        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)
-                if data.get('negative_passages') is None:
-                    data['negative_passages'] = []
-                if data.get('positive_passages') is None:
-                    data['positive_passages'] = []
-                yield data['query_id'], data