|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" |
|
State of the art models using deep neural networks have become very good in learning an accurate |
|
mapping from inputs to outputs. However, they still lack generalization capabilities in conditions |
|
that differ from the ones encountered during training. This is even more challenging in specialized, |
|
and knowledge intensive domains, where training data is limited. To address this gap, we introduce |
|
MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), |
|
grounded in the medical history of patients. As the source of premise sentences, we used the |
|
MIMIC-III. More specifically, to minimize the risks to patient privacy, we worked with clinical |
|
notes corresponding to the deceased patients. The clinicians in our team suggested the Past Medical |
|
History to be the most informative section of a clinical note, from which useful inferences can be |
|
drawn about the patient. |
|
|
|
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 `"mednli"` it should contain the following files: |
|
|
|
mednli |
|
├── mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0.zip |
|
""" |
|
|
|
import json |
|
import os |
|
from typing import Dict, List, Tuple |
|
|
|
import datasets |
|
|
|
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/c2rs98, |
|
title = {MedNLI — A Natural Language Inference Dataset For The Clinical Domain}, |
|
author = {Shivade, Chaitanya}, |
|
year = 2017, |
|
publisher = {physionet.org}, |
|
doi = {10.13026/C2RS98}, |
|
url = {https://physionet.org/content/mednli/} |
|
} |
|
""" |
|
|
|
|
|
_DATASETNAME = "mednli" |
|
_DISPLAYNAME = "MedNLI" |
|
|
|
_DESCRIPTION = """\ |
|
State of the art models using deep neural networks have become very good in learning an accurate |
|
mapping from inputs to outputs. However, they still lack generalization capabilities in conditions |
|
that differ from the ones encountered during training. This is even more challenging in specialized, |
|
and knowledge intensive domains, where training data is limited. To address this gap, we introduce |
|
MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), |
|
grounded in the medical history of patients. As the source of premise sentences, we used the |
|
MIMIC-III. More specifically, to minimize the risks to patient privacy, we worked with clinical |
|
notes corresponding to the deceased patients. The clinicians in our team suggested the Past Medical |
|
History to be the most informative section of a clinical note, from which useful inferences can be |
|
drawn about the patient. |
|
""" |
|
|
|
|
|
_HOMEPAGE = "https://physionet.org/content/mednli/1.0.0/" |
|
|
|
_LICENSE = "PHYSIONET_LICENSE_1p5" |
|
|
|
_URLS = {} |
|
|
|
_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT] |
|
|
|
_SOURCE_VERSION = "1.0.0" |
|
_BIGBIO_VERSION = "1.0.0" |
|
|
|
|
|
class MedNLIDataset(datasets.GeneratorBasedBuilder): |
|
"""MedNLI""" |
|
|
|
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
|
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
|
|
|
BUILDER_CONFIGS = [ |
|
BigBioConfig( |
|
name="mednli_source", |
|
version=SOURCE_VERSION, |
|
description="MedNLI source schema", |
|
schema="source", |
|
subset_id="mednli", |
|
), |
|
BigBioConfig( |
|
name="mednli_bigbio_te", |
|
version=BIGBIO_VERSION, |
|
description="MedNLI BigBio schema", |
|
schema="bigbio_te", |
|
subset_id="mednli", |
|
), |
|
] |
|
|
|
DEFAULT_CONFIG_NAME = "mednli_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-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0.zip", |
|
) |
|
) |
|
data_dir = os.path.join( |
|
extract_dir, |
|
"mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0", |
|
) |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": os.path.join(data_dir, "mli_train_v1.jsonl"), |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": os.path.join(data_dir, "mli_test_v1.jsonl"), |
|
"split": "test", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": os.path.join(data_dir, "mli_dev_v1.jsonl"), |
|
"split": "dev", |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: |
|
with open(filepath, "r") as f: |
|
if self.config.schema == "source": |
|
for line in f: |
|
json_line = json.loads(line) |
|
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": json_line["gold_label"], |
|
} |
|
yield json_line["pairID"], entailment_example |
|
|
