Public MBE subset of Bar Exam QA

.gitattributes

@@ -56,3 +56,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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mbe.py

@@ -0,0 +1,140 @@
+# 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