upload hubscripts/medhop_hub.py to hub from bigbio repo

medhop.py

@@ -0,0 +1,212 @@
+# coding=utf-8
+# Copyright 2022 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 json
+import os
+
+import datasets
+
+from .bigbiohub import qa_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{welbl-etal-2018-constructing,
+title = Constructing Datasets for Multi-hop Reading Comprehension Across Documents,
+author = Welbl, Johannes and Stenetorp, Pontus and Riedel, Sebastian,
+journal = Transactions of the Association for Computational Linguistics,
+volume = 6,
+year = 2018,
+address = Cambridge, MA,
+publisher = MIT Press,
+url = https://aclanthology.org/Q18-1021,
+doi = 10.1162/tacl_a_00021,
+pages = 287--302,
+abstract = {
+    Most Reading Comprehension methods limit themselves to queries which
+    can be answered using a single sentence, paragraph, or document.
+    Enabling models to combine disjoint pieces of textual evidence would
+    extend the scope of machine comprehension methods, but currently no
+    resources exist to train and test this capability. We propose a novel
+    task to encourage the development of models for text understanding
+    across multiple documents and to investigate the limits of existing
+    methods. In our task, a model learns to seek and combine evidence
+    -- effectively performing multihop, alias multi-step, inference.
+    We devise a methodology to produce datasets for this task, given a
+    collection of query-answer pairs and thematically linked documents.
+    Two datasets from different domains are induced, and we identify
+    potential pitfalls and devise circumvention strategies. We evaluate
+    two previously proposed competitive models and find that one can
+    integrate information across documents. However, both models
+    struggle to select relevant information; and providing documents
+    guaranteed to be relevant greatly improves their performance. While
+    the models outperform several strong baselines, their best accuracy
+    reaches 54.5 % on an annotated test set, compared to human
+    performance at 85.0 %, leaving ample room for improvement.
+}
+"""
+
+_DESCRIPTION = """\
+With the same format as WikiHop, this dataset is based on research paper
+abstracts from PubMed, and the queries are about interactions between
+pairs of drugs. The correct answer has to be inferred by combining
+information from a chain of reactions of drugs and proteins.
+"""
+
+_DATASETNAME = "medhop"
+_DISPLAYNAME = "MedHop"
+
+_HOMEPAGE = "http://qangaroo.cs.ucl.ac.uk/"
+
+_LICENSE = 'Creative Commons Attribution Share Alike 3.0 Unported'
+
+_BASE_GDRIVE = "https://drive.google.com/uc?export=download&confirm=yTib&id="
+
+_URLs = {
+    "source": _BASE_GDRIVE + "1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA",
+    "bigbio_qa": _BASE_GDRIVE + "1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA",
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MedHopDataset(datasets.GeneratorBasedBuilder):
+    """MedHop"""
+
+    DEFAULT_CONFIG_NAME = "medhop_source"
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="medhop_source",
+            version=SOURCE_VERSION,
+            description="MedHop source schema",
+            schema="source",
+            subset_id="MedHop",
+        ),
+        BigBioConfig(
+            name="medhop_bigbio_qa",
+            version=BIGBIO_VERSION,
+            description="MedHop BigBio schema",
+            schema="bigbio_qa",
+            subset_id="MedHop",
+        ),
+    ]
+
+    def _info(self):
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "candidates": datasets.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                    "supports": datasets.Sequence(datasets.Value("string")),
+                    "query": datasets.Value("string"),
+                }
+            )
+
+        # simplified schema for QA tasks
+        elif self.config.schema == "bigbio_qa":
+
+            features = qa_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        my_urls = _URLs[self.config.schema]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        data_dir += "/qangaroo_v1.1/medhop/"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.json"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.json"),
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+
+            with open(filepath, encoding="utf-8") as file:
+
+                uid = 0
+
+                data = json.load(file)
+
+                for i, record in enumerate(data):
+
+                    yield i, {
+                        "id": record["id"],
+                        "candidates": record["candidates"],
+                        "answer": record["answer"],
+                        "supports": record["supports"],
+                        "query": record["query"],
+                    }
+
+                    uid += 1
+
+        elif self.config.schema == "bigbio_qa":
+
+            with open(filepath, encoding="utf-8") as file:
+
+                uid = 0
+
+                data = json.load(file)
+
+                for record in data:
+
+                    record["type"] = "multiple_choice"
+
+                    yield uid, {
+                        "id": record["id"],
+                        "document_id": record["id"],
+                        "question_id": record["id"],
+                        "question": record["query"],
+                        "type": record["type"],
+                        "context": " ".join(record["supports"]),
+                        "answer": [record["answer"]],
+                        "choices": record["candidates"],
+                    }
+
+                    uid += 1