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.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data/passages.jsonl filter=lfs diff=lfs merge=lfs -text

data/passages.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c70370268ee068beb2b8f7b3eaab1a696124ed31a68faab7794d8fa399bd99e9
+size 33963958

legal-questions.py

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+# 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.
+
+
+import csv
+import json
+
+import datasets
+
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+Legal Questions is a dataset for evaluating passage retrievers. 
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+_FEATURES_PAIRS = datasets.Features(
+    {        
+        "question_id": datasets.Value("int32"),
+        "question": datasets.Value("string"),
+        "passage_id": datasets.Value("string"),
+        "answers": datasets.Value("string"),
+        "passage_title": datasets.Value("string"),
+        "passage_text": datasets.Value("string"),
+        "relevant": datasets.Value("bool"),
+    }
+)
+
+_FEATURES_PASSAGES = datasets.Features(
+    {        
+        "id": datasets.Value("string"),
+        "title": datasets.Value("string"),
+        "text": datasets.Value("string"),
+    }
+)
+
+_URLS = {
+    "pairs": {
+        "test": ["data/test.csv"],
+    },
+    "passages": {
+        "test": ["data/passages.jsonl"],
+    },
+}
+
+
+class LegalQuestions(datasets.GeneratorBasedBuilder):
+    """Legal Questions is a dataset for evaluating passage retrievers. """
+
+    BUILDER_CONFIGS = list(map(lambda x: datasets.BuilderConfig(name=x, version=datasets.Version("1.0.0")), _URLS.keys()))
+    DEFAULT_CONFIG_NAME = "pairs"
+
+    def _info(self):
+        if self.config.name == "pairs":
+            features = _FEATURES_PAIRS
+        else:
+            features = _FEATURES_PASSAGES
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": data_dir["test"],
+                },
+            ),
+        ]
+
+    @staticmethod
+    def _parse_bool(text):
+        if text == 'True':
+            return True
+        elif text == 'False':
+            return False
+        else:
+            raise ValueError
+
+    def _generate_examples(self, filepaths):
+        if self.config.name == "pairs":
+            boolean_features = [name for name, val in _FEATURES_PAIRS.items() if val.dtype == "bool"]
+
+            for filepath in filepaths:
+                with open(filepath, encoding="utf-8") as f:
+                    data = csv.DictReader(f)
+                    for i, row in enumerate(data):
+                        for boolean_feature in boolean_features:
+                            row[boolean_feature] = self._parse_bool(row[boolean_feature])
+                        yield i, row
+        else:
+            for filepath in filepaths:
+                with open(filepath, encoding="utf-8") as f:
+                    for i, row in enumerate(f):
+                        parsed_row = json.loads(row)
+                        yield i, parsed_row