upload hubscripts/mediqa_nli_hub.py to hub from bigbio repo

mediqa_nli.py

@@ -0,0 +1,200 @@
+# 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.
+
+"""
+Natural Language Inference (NLI) is the task of determining whether a given hypothesis can be
+inferred from a given premise. Also known as Recognizing Textual Entailment (RTE), this task has
+enjoyed popularity among researchers for some time. However, almost all datasets for this task
+focused on open domain data such as as news texts, blogs, and so on. To address this gap, the MedNLI
+dataset was created for language inference in the medical domain. MedNLI is a derived dataset with
+data sourced from MIMIC-III v1.4. In order to stimulate research for this problem, a shared task on
+Medical Inference and Question Answering (MEDIQA) was organized at the workshop for biomedical
+natural language processing (BioNLP) 2019. The dataset provided herein is a test set of 405 premise
+hypothesis pairs for the NLI challenge in the MEDIQA shared task. Participants of the shared task
+are expected to use the MedNLI data for development of their models and this dataset was used as an
+unseen dataset for scoring each participant submission.
+
+The files comprising this dataset must be on the users local machine in a single directory that is
+passed to `datasets.load_datset` via the `data_dir` kwarg. This loader script will read the archive
+files directly (i.e. the user should not uncompress, untar or unzip any of the files). For example,
+if `data_dir` is `"mediqa_nli"` it should contain the following files:
+
+mediqa_nli
+├── mednli-for-shared-task-at-acl-bionlp-2019-1.0.1.zip
+"""
+
+import json
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import entailment_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = True
+_CITATION = """\
+@misc{https://doi.org/10.13026/gtv4-g455,
+    title        = {MedNLI for Shared Task at ACL BioNLP 2019},
+    author       = {Shivade,  Chaitanya},
+    year         = 2019,
+    publisher    = {physionet.org},
+    doi          = {10.13026/GTV4-G455},
+    url          = {https://physionet.org/content/mednli-bionlp19/}
+}
+
+"""
+
+_DATASETNAME = "mediqa_nli"
+_DISPLAYNAME = "MEDIQA NLI"
+
+_DESCRIPTION = """\
+Natural Language Inference (NLI) is the task of determining whether a given hypothesis can be
+inferred from a given premise. Also known as Recognizing Textual Entailment (RTE), this task has
+enjoyed popularity among researchers for some time. However, almost all datasets for this task
+focused on open domain data such as as news texts, blogs, and so on. To address this gap, the MedNLI
+dataset was created for language inference in the medical domain. MedNLI is a derived dataset with
+data sourced from MIMIC-III v1.4. In order to stimulate research for this problem, a shared task on
+Medical Inference and Question Answering (MEDIQA) was organized at the workshop for biomedical
+natural language processing (BioNLP) 2019. The dataset provided herein is a test set of 405 premise
+hypothesis pairs for the NLI challenge in the MEDIQA shared task. Participants of the shared task
+are expected to use the MedNLI data for development of their models and this dataset was used as an
+unseen dataset for scoring each participant submission.
+"""
+
+
+_HOMEPAGE = "https://physionet.org/content/mednli-bionlp19/1.0.1/"
+
+_LICENSE = 'PhysioNet Credentialed Health Data License'
+
+_URLS = {}
+
+_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
+
+_SOURCE_VERSION = "1.0.1"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MEDIQANLIDataset(datasets.GeneratorBasedBuilder):
+    """MEDIQA NLI"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="mediqa_nli_source",
+            version=SOURCE_VERSION,
+            description="MEDIQA NLI source schema",
+            schema="source",
+            subset_id="mediqa_nli",
+        ),
+        BigBioConfig(
+            name="mediqa_nli_bigbio_te",
+            version=BIGBIO_VERSION,
+            description="MEDIQA NLI BigBio schema",
+            schema="bigbio_te",
+            subset_id="mediqa_nli",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mediqa_nli_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pairID": datasets.Value("string"),
+                    "gold_label": datasets.Value("string"),
+                    "sentence1": datasets.Value("string"),
+                    "sentence2": datasets.Value("string"),
+                    "sentence1_parse": datasets.Value("string"),
+                    "sentence2_parse": datasets.Value("string"),
+                    "sentence1_binary_parse": datasets.Value("string"),
+                    "sentence2_binary_parse": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_te":
+            features = entailment_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        if self.config.data_dir is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
+            )
+        else:
+            extract_dir = dl_manager.extract(
+                os.path.join(
+                    self.config.data_dir,
+                    "mednli-for-shared-task-at-acl-bionlp-2019-1.0.1.zip",
+                )
+            )
+            data_dir = os.path.join(
+                extract_dir, "mednli-for-shared-task-at-acl-bionlp-2019-1.0.1"
+            )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "examples_filepath": os.path.join(
+                        data_dir, "mednli_bionlp19_shared_task.jsonl"
+                    ),
+                    "ground_truth_filepath": os.path.join(
+                        data_dir, "mednli_bionlp19_shared_task_ground_truth.csv"
+                    ),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, examples_filepath: str, ground_truth_filepath: str, split: str
+    ) -> Tuple[int, Dict]:
+
+        ground_truth = pd.read_csv(
+            ground_truth_filepath, index_col=0, squeeze=True
+        ).to_dict()
+        with open(examples_filepath, "r") as f:
+            if self.config.schema == "source":
+                for line in f:
+                    json_line = json.loads(line)
+                    json_line["gold_label"] = ground_truth[json_line["pairID"]]
+                    yield json_line["pairID"], json_line
+
+            elif self.config.schema == "bigbio_te":
+                for line in f:
+                    json_line = json.loads(line)
+                    entailment_example = {
+                        "id": json_line["pairID"],
+                        "premise": json_line["sentence1"],
+                        "hypothesis": json_line["sentence2"],
+                        "label": ground_truth[json_line["pairID"]],
+                    }
+                    yield json_line["pairID"], entailment_example