Upload msmarco_passage.py with huggingface_hub

msmarco_passage.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.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",
+    # 'dl19': "https://huggingface.co/datasets/Tevatron/msmarco-passage/resolve/main/dl19.jsonl.gz",
+    # 'dl20': "https://huggingface.co/datasets/Tevatron/msmarco-passage/resolve/main/dl20.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