initial commit

README.md

@@ -1,3 +1,43 @@
 ---
+language: en
 license: other
+multilinguality: monolingual
+pretty_name: MedNLI
+paperswithcode_id: mednli
 ---
+
+
+# Dataset Card for MedNLI
+
+## Dataset Description
+
+- **Homepage:** https://physionet.org/content/mednli/1.0.0/
+- **Pubmed:** False
+- **Public:** False
+- **Tasks:** Textual Entailment
+
+
+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.
+
+
+## Citation Information
+
+```
+@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/}
+}
+```

bigbiohub.py

@@ -0,0 +1,153 @@
+from dataclasses import dataclass
+from enum import Enum
+import datasets
+from types import SimpleNamespace
+
+
+BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")
+
+
+@dataclass
+class BigBioConfig(datasets.BuilderConfig):
+    """BuilderConfig for BigBio."""
+
+    name: str = None
+    version: datasets.Version = None
+    description: str = None
+    schema: str = None
+    subset_id: str = None
+
+
+class Tasks(Enum):
+    NAMED_ENTITY_RECOGNITION = "NER"
+    NAMED_ENTITY_DISAMBIGUATION = "NED"
+    EVENT_EXTRACTION = "EE"
+    RELATION_EXTRACTION = "RE"
+    COREFERENCE_RESOLUTION = "COREF"
+    QUESTION_ANSWERING = "QA"
+    TEXTUAL_ENTAILMENT = "TE"
+    SEMANTIC_SIMILARITY = "STS"
+    TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
+    PARAPHRASING = "PARA"
+    TRANSLATION = "TRANSL"
+    SUMMARIZATION = "SUM"
+    TEXT_CLASSIFICATION = "TXTCLASS"
+
+
+entailment_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "premise": datasets.Value("string"),
+        "hypothesis": datasets.Value("string"),
+        "label": datasets.Value("string"),
+    }
+)
+
+pairs_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text_1": datasets.Value("string"),
+        "text_2": datasets.Value("string"),
+        "label": datasets.Value("string"),
+    }
+)
+
+qa_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "question_id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "question": datasets.Value("string"),
+        "type": datasets.Value("string"),
+        "choices": [datasets.Value("string")],
+        "context": datasets.Value("string"),
+        "answer": datasets.Sequence(datasets.Value("string")),
+    }
+)
+
+text_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text": datasets.Value("string"),
+        "labels": [datasets.Value("string")],
+    }
+)
+
+text2text_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "text_1": datasets.Value("string"),
+        "text_2": datasets.Value("string"),
+        "text_1_name": datasets.Value("string"),
+        "text_2_name": datasets.Value("string"),
+    }
+)
+
+kb_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "document_id": datasets.Value("string"),
+        "passages": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "text": datasets.Sequence(datasets.Value("string")),
+                "offsets": datasets.Sequence([datasets.Value("int32")]),
+            }
+        ],
+        "entities": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "text": datasets.Sequence(datasets.Value("string")),
+                "offsets": datasets.Sequence([datasets.Value("int32")]),
+                "normalized": [
+                    {
+                        "db_name": datasets.Value("string"),
+                        "db_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+        "events": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                # refers to the text_bound_annotation of the trigger
+                "trigger": {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "offsets": datasets.Sequence([datasets.Value("int32")]),
+                },
+                "arguments": [
+                    {
+                        "role": datasets.Value("string"),
+                        "ref_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+        "coreferences": [
+            {
+                "id": datasets.Value("string"),
+                "entity_ids": datasets.Sequence(datasets.Value("string")),
+            }
+        ],
+        "relations": [
+            {
+                "id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "arg1_id": datasets.Value("string"),
+                "arg2_id": datasets.Value("string"),
+                "normalized": [
+                    {
+                        "db_name": datasets.Value("string"),
+                        "db_id": datasets.Value("string"),
+                    }
+                ],
+            }
+        ],
+    }
+)

mednli.py

@@ -0,0 +1,201 @@
+# 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.
+
+"""
+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