# 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