upload script

T2Ranking.py

@@ -0,0 +1,116 @@
+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+
+import datasets
+import json
+from typing import List
+import pandas as pd
+
+_LICENSE = "http://www.apache.org/licenses/LICENSE-2.0"
+_HOMEPAGE='https://huggingface.co/datasets/THUIR/T2Ranking'
+
+_DESCRIPTION = 'T2Ranking: A large-scale Chinese benchmark for passage retrieval.'
+_CITATION = """
+@article{sigir2023,
+title={T2Ranking},
+author={Qian Dong},
+volume={2023},
+number={2},
+pages={99-110},
+year={2022}
+}
+"""
+
+_URLS_DICT = {
+    "collection": "data/collection.tsv",
+    "qrels.train": "data/qrels.train.tsv",
+    "queries.train": "data/queries.train.tsv",
+}
+
+_FEATURES_DICT = {
+    'collection': {
+        "pid": datasets.Value("int64"),
+        "text": datasets.Value("string"),
+    },
+    'qrels.train': {
+        "qid": datasets.Value("int64"),
+        "-": datasets.Value("int64"),
+        "pid": datasets.Value("int64"),
+        "rel": datasets.Value("int64"),
+    },
+    'queries.train': {
+        "qid": datasets.Value("int64"),
+        "text": datasets.Value("string"),
+    },
+}
+
+class T2RankingConfig(datasets.BuilderConfig):
+    """BuilderConfig for T2Ranking."""
+
+    def __init__(self, splits, **kwargs):
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.splits = splits
+
+
+class T2Ranking(datasets.GeneratorBasedBuilder):
+    """The T2Ranking benchmark."""
+
+    BUILDER_CONFIGS = [
+        T2RankingConfig(
+            name="collection",
+            splits=['train'],
+        ),
+        T2RankingConfig(
+            name="qrels.train",
+            splits=['train'],
+        ),
+        T2RankingConfig(
+            name="queries.train",
+            splits=['train'],
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(_FEATURES_DICT[self.config.name]),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        split_things = []
+        for split_name in self.config.splits:
+            # print('')
+            split_data_path = _URLS_DICT[self.config.name]
+            # print(split_data_path)
+            filepath = dl_manager.download(split_data_path)
+            # print(filepath)
+            # print('')
+            split_thing = datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                }
+            )
+            split_things.append(split_thing)
+        return split_things
+
+    def _generate_examples(self, filepath):
+        data = pd.read_csv(filepath, sep='\t')
+        keys = _FEATURES_DICT[self.config.name].keys()
+        for idx in range(data.shape[0]):
+            yield idx, {key: data[key][idx] for key in keys}