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dev.jsonl.gz → default/msmarco-passage-dev.parquet

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train.jsonl.gz → default/msmarco-passage-train-00000-of-00010.parquet

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msmarco-passage.py

@@ -1,103 +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.Wikipedia
-
-# Lint as: python3
-"""MsMarco Passage dataset."""
-
-import json
-
-import datasets
-
-_CITATION = """
-@misc{bajaj2018ms,
-      title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, 
-      author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu 
-      and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song
-       and Alina Stoica and Saurabh Tiwary and Tong Wang},
-      year={2018},
-      eprint={1611.09268},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = "dataset load script for MSMARCO Passage"
-
-_DATASET_URLS = {
-    'train': "https://huggingface.co/datasets/Tevatron/msmarco-passage/resolve/main/train.jsonl.gz",
-    #'train': "https://www.dropbox.com/s/seqqbu90jopvtq5/msmarco_passage_train.json",
-    'dev': "https://huggingface.co/datasets/Tevatron/msmarco-passage/resolve/main/dev.jsonl.gz",
-}
-
-
-class MsMarcoPassage(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(version=VERSION,
-                               description="MS MARCO passage train/dev datasets"),
-    ]
-
-    def _info(self):
-        features = datasets.Features({
-            'query_id': datasets.Value('string'),
-            'query': datasets.Value('string'),
-            'positive_passages': [
-                {'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': datasets.Value('string')}
-            ],
-            'negative_passages': [
-                {'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': 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):
-        if self.config.data_files:
-            downloaded_files = self.config.data_files
-        else:
-            downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
-        splits = [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={
-                    "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
-                },
-            ) for split in downloaded_files
-        ]
-        return splits
-        
-    def _generate_examples(self, files):
-        """Yields examples."""
-        for filepath in files:
-            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