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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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README.md

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+---
+language:
+- en
+pretty_name: Bar Exam QA
+license: cc-by-sa-4.0
+---
+`Bar Exam QA` is a dataset for legal information retrieval and question answering. See the [original releasing paper](https://reglab.github.io/legal-rag-benchmarks/) for more information.
+
+This repo contains the public MBE subset of questions (historical exams), which we release with gold passage annotations. In our work, we also evaluate on a private test set of questions from a BarBri multistate bar exam test preparation textbook.
+
+The dataset was created by linking multistate bar exam multiple-choice questions with supporting gold passages from primary sources (caselaw) and secondary sources (online resources, textbooks, etc.).
+The gold passages were annotated by a law student, simulating a legal research workflow.
+
+## qa
+The dataset can be loaded with the `qa` configuration. Each example consists of a question, multiple-choice answers, an answer label, and a gold passage (passage ID) label.
+The dataset contains 1,195 examples.
+```python
+dataset = load_dataset("reglab/barexam_qa", name="qa")
+```
+where
+- `idx`: unique example ID (string)
+- `prompt`: the question prompt (context before the question, can be shared between questions)
+- `question`: the question
+- `choice_a`: answer choice A text
+- `choice_b`: answer choice B text
+- `choice_c`: answer choice C text
+- `choice_d`: answer choice D text
+- `answer`: A, B, C, or D
+- `gold_passge`: gold passage text
+- `gold_idx`: passage ID of gold passage (matches with passage ID in `passages`)
+
+## passages
+The passage pool can be loaded with `passages` configuration. Each passages consists of a passage ID, the passage text, and additional metadata if applicable (e.g, case ID for passages from Caselaw Access Project).
+The passage pool contains ~900K passages.
+```python
+dataset = load_dataset("reglab/barexam_qa", name="passages")
+```
+where
+- `idx`: unique passage ID (string)
+- `faiss_id`: unique Faiss ID (integer)
+- `case_id`: case ID (from Caselaw Access Project)
+- `opinion_id`: opinion ID (from Caselaw Access Project)
+- `absolute_paragraph_id`: paragraph ID in case across all opinions (from Caselaw Access Project)
+- `relative_paragraph_id`: paragraph ID in opinion (from Caselaw Access Project)
+- `text`: passage text

barexam_qa.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 datasets
+import pandas as pd
+
+
+_CITATION = """"""
+_DESCRIPTION = """"""
+_HOMEPAGE = ""
+_LICENSE = ""
+_URLS = {
+    "qa": {
+        "train": "data/qa/train.csv",
+        "validation": "data/qa/validation.csv",
+        "test": "data/qa/test.csv",
+        "all": "data/qa/qa.csv",
+    },
+    "passages": {
+        "train": "data/passages/train.tsv",
+        "validation": "data/passages/validation.tsv",
+        "test": "data/passages/test.tsv",
+        "all": "data/passages/passages.tsv"
+    },
+}
+
+_CONFIGS = {}
+
+_CONFIGS["qa"] = {
+    "description": "Answer bar exam questions",
+    "features": {
+        "idx": datasets.Value("string"),
+        "dataset": datasets.Value("string"),
+        "example_id": datasets.Value("string"),
+        "prompt_id": datasets.Value("string"),
+        "source": datasets.Value("string"),
+        "subject": datasets.Value("string"),
+        "question_number": datasets.Value("string"),
+        "prompt": datasets.Value("string"),
+        "question": datasets.Value("string"),
+        "choice_a": datasets.Value("string"),
+        "choice_b": datasets.Value("string"),
+        "choice_c": datasets.Value("string"),
+        "choice_d": datasets.Value("string"),
+        "answer": datasets.Value("string"),
+        "gold_passage": datasets.Value("string"),
+        "gold_idx": datasets.Value("string"),
+    },
+    "license": None,
+}
+
+_CONFIGS["passages"] = {
+    "description": "Passage corpus of bar exam question explanations, Wex definitions and primary sources, and caselaw",
+    "features": {
+        "idx": datasets.Value("string"),
+        "source": datasets.Value("string"),
+        "faiss_id": datasets.Value("string"),
+        "case_id": datasets.Value("string"),
+        "absolute_paragraph_id": datasets.Value("string"),
+        "opinion_id": datasets.Value("string"),
+        "relative_paragraph_id": datasets.Value("string"),
+        "text": datasets.Value("string"),
+    },
+    "license": None,
+}
+
+
+class BarExamQA(datasets.GeneratorBasedBuilder):
+    """Legal retrieval/QA dataset for the multistate bar exam"""
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=task, version=datasets.Version("1.0.0"), description=task,
+        )
+        for task in _CONFIGS
+    ]
+
+
+    def _info(self):
+        features = _CONFIGS[self.config.name]["features"]
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_CONFIGS[self.config.name]["license"],
+        )
+
+
+    def _split_generators(self, dl_manager):
+        downloaded_file_dir = dl_manager.download_and_extract(_URLS[self.config.name])
+        splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "fpath": downloaded_file_dir["train"],
+                    "name": self.config.name,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "fpath": downloaded_file_dir["validation"],
+                    "name": self.config.name,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "fpath": downloaded_file_dir["test"],
+                    "name": self.config.name,
+                },
+            ),
+        ]
+        return splits
+
+
+    def _generate_examples(self, fpath, name):
+        """Yields examples as (key, example) tuples."""
+        if name in ["qa"]:
+            data = pd.read_csv(fpath)
+            data = data.to_dict(orient="records")
+            for id_line, example in enumerate(data):
+                yield id_line, example
+
+        if name in ["passages"]:
+            data = pd.read_csv(fpath, sep='\t')
+            data = data.to_dict(orient="records")
+            for id_line, example in enumerate(data):
+                yield id_line, example

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