upload hubscripts/biomrc_hub.py to hub from bigbio repo

biomrc.py

@@ -0,0 +1,267 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+"""
+We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the
+previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the
+new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating
+that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is
+also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new
+BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or
+surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different
+sizes, also releasing our code, and providing a leaderboard.
+"""
+
+import itertools as it
+import json
+
+import datasets
+
+from .bigbiohub import qa_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@inproceedings{pappas-etal-2020-biomrc,
+    title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
+    author = "Pappas, Dimitris  and
+      Stavropoulos, Petros  and
+      Androutsopoulos, Ion  and
+      McDonald, Ryan",
+    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
+    pages = "140--149",
+}
+"""
+
+_DATASETNAME = "biomrc"
+_DISPLAYNAME = "BIOMRC"
+
+_DESCRIPTION = """\
+We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the
+previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the
+new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating
+that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is
+also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new
+BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or
+surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different
+sizes, also releasing our code, and providing a leaderboard.
+"""
+
+_HOMEPAGE = "https://github.com/PetrosStav/BioMRC_code"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    "large": {
+        "A": {
+            "train": "https://archive.org/download/biomrc_dataset/biomrc_large/dataset_train.json.gz",
+            "val": "https://archive.org/download/biomrc_dataset/biomrc_large/dataset_val.json.gz",
+            "test": "https://archive.org/download/biomrc_dataset/biomrc_large/dataset_test.json.gz",
+        },
+        "B": {
+            "train": "https://archive.org/download/biomrc_dataset/biomrc_large/dataset_train_B.json.gz",
+            "val": "https://archive.org/download/biomrc_dataset/biomrc_large/dataset_val_B.json.gz",
+            "test": "https://archive.org/download/biomrc_dataset/biomrc_large/dataset_test_B.json.gz",
+        },
+    },
+    "small": {
+        "A": {
+            "train": "https://archive.org/download/biomrc_dataset/biomrc_small/dataset_train_small.json.gz",
+            "val": "https://archive.org/download/biomrc_dataset/biomrc_small/dataset_val_small.json.gz",
+            "test": "https://archive.org/download/biomrc_dataset/biomrc_small/dataset_test_small.json.gz",
+        },
+        "B": {
+            "train": "https://archive.org/download/biomrc_dataset/biomrc_small/dataset_train_small_B.json.gz",
+            "val": "https://archive.org/download/biomrc_dataset/biomrc_small/dataset_val_small_B.json.gz",
+            "test": "https://archive.org/download/biomrc_dataset/biomrc_small/dataset_test_small_B.json.gz",
+        },
+    },
+    "tiny": {
+        "A": {
+            "test": "https://archive.org/download/biomrc_dataset/biomrc_tiny/dataset_tiny.json.gz"
+        },
+        "B": {
+            "test": "https://archive.org/download/biomrc_dataset/biomrc_tiny/dataset_tiny_B.json.gz"
+        },
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class BiomrcDataset(datasets.GeneratorBasedBuilder):
+    """BioMRC: A Dataset for Biomedical Machine Reading Comprehension"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = []
+
+    for biomrc_setting in ["A", "B"]:
+        for biomrc_version in ["large", "small", "tiny"]:
+            subset_id = f"biomrc_{biomrc_version}_{biomrc_setting}"
+            BUILDER_CONFIGS.append(
+                BigBioConfig(
+                    name=f"{subset_id}_source",
+                    version=SOURCE_VERSION,
+                    description=f"BioMRC Version {biomrc_version} Setting {biomrc_setting} source schema",
+                    schema="source",
+                    subset_id=subset_id,
+                )
+            )
+            BUILDER_CONFIGS.append(
+                BigBioConfig(
+                    name=f"{subset_id}_bigbio_qa",
+                    version=BIGBIO_VERSION,
+                    description=f"BioMRC Version {biomrc_version} Setting {biomrc_setting} BigBio schema",
+                    schema="bigbio_qa",
+                    subset_id=subset_id,
+                )
+            )
+
+    DEFAULT_CONFIG_NAME = "biomrc_large_B_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "abstract": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "entities_list": datasets.features.Sequence(
+                        {
+                            "pseudoidentifier": datasets.Value("string"),
+                            "identifier": datasets.Value("string"),
+                            "synonyms": datasets.Value("string"),
+                        }
+                    ),
+                    "answer": {
+                        "pseudoidentifier": datasets.Value("string"),
+                        "identifier": datasets.Value("string"),
+                        "synonyms": datasets.Value("string"),
+                    },
+                }
+            )
+        elif self.config.schema == "bigbio_qa":
+            features = qa_features
+        else:
+            raise NotImplementedError()
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        _, version, setting = self.config.subset_id.split("_")
+        downloaded_files = dl_manager.download_and_extract(_URLS[version][setting])
+
+        if version == "tiny":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": downloaded_files["test"]},
+                ),
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": downloaded_files["train"]},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": downloaded_files["val"]},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": downloaded_files["test"]},
+                ),
+            ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            with open(filepath, encoding="utf-8") as fp:
+                biomrc = json.load(fp)
+                for _id, (ab, ti, el, an) in enumerate(
+                    zip(
+                        biomrc["abstracts"],
+                        biomrc["titles"],
+                        biomrc["entities_list"],
+                        biomrc["answers"],
+                    )
+                ):
+                    el = [self._parse_dict_from_entity(entity) for entity in el]
+                    an = self._parse_dict_from_entity(an)
+                    yield _id, {
+                        "abstract": ab,
+                        "title": ti,
+                        "entities_list": el,
+                        "answer": an,
+                    }
+        elif self.config.schema == "bigbio_qa":
+            with open(filepath, encoding="utf-8") as fp:
+                uid = it.count(0)
+                biomrc = json.load(fp)
+                for _id, (ab, ti, el, an) in enumerate(
+                    zip(
+                        biomrc["abstracts"],
+                        biomrc["titles"],
+                        biomrc["entities_list"],
+                        biomrc["answers"],
+                    )
+                ):
+                    # remove info such as code, label, synonyms from answer and choices
+                    # f.e. @entity1 :: ('9606', 'Species') :: ['patients', 'patient']"
+                    example = {
+                        "id": next(uid),
+                        "question_id": next(uid),
+                        "document_id": next(uid),
+                        "question": ti,
+                        "type": "multiple_choice",
+                        "choices": [x.split(" :: ")[0] for x in el],
+                        "context": ab,
+                        "answer": [an.split(" :: ")[0]],
+                    }
+                    yield _id, example
+
+    def _parse_dict_from_entity(self, entity):
+        if "::" in entity:
+            pseudoidentifier, identifier, synonyms = entity.split(" :: ")
+            return {
+                "pseudoidentifier": pseudoidentifier,
+                "identifier": identifier,
+                "synonyms": synonyms,
+            }
+        else:
+            return {"pseudoidentifier": entity, "identifier": "", "synonyms": ""}