upload hubscripts/swedish_medical_ner_hub.py to hub from bigbio repo

swedish_medical_ner.py

@@ -0,0 +1,302 @@
+# 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.
+
+
+"""
+swedish_medical_ner is Named Entity Recognition dataset on medical text in Swedish. 
+
+It consists three subsets which are in turn derived from three different sources respectively: 
+
+* the Swedish Wikipedia (a.k.a. wiki): Wiki_annotated_60.txt
+* Läkartidningen (a.k.a. lt): LT_annotated_60.txt
+* 1177 Vårdguiden (a.k.a. 1177): 1177_annotated_sentences.txt
+
+Texts from both Swedish Wikipedia and Läkartidningen were automatically annotated using a 
+list of medical seed terms. Sentences from 1177 Vårdguiden were manuually annotated.
+
+It can be found in Hugging Face Datasets: https://huggingface.co/datasets/swedish_medical_ner.
+
+"""
+
+import os
+import re
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_DATASETNAME = "swedish_medical_ner"
+_DISPLAYNAME = "Swedish Medical NER"
+
+_LANGUAGES = ['Swedish']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@inproceedings{almgren-etal-2016-named,
+    author = {
+        Almgren, Simon and
+        Pavlov, Sean and
+        Mogren, Olof
+    },
+    title     = {Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs},
+    booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016)},
+    publisher = {The COLING 2016 Organizing Committee},
+    pages     = {30-39},
+    year      = {2016},
+    month     = {12},
+    url       = {https://aclanthology.org/W16-5104},
+    eprint    = {https://aclanthology.org/W16-5104.pdf}
+}
+"""
+
+_DESCRIPTION = """\
+swedish_medical_ner is Named Entity Recognition dataset on medical text in Swedish. 
+It consists three subsets which are in turn derived from three different sources 
+respectively: the Swedish Wikipedia (a.k.a. wiki), Läkartidningen (a.k.a. lt), 
+and 1177 Vårdguiden (a.k.a. 1177). While the Swedish Wikipedia and Läkartidningen 
+subsets in total contains over 790000 sequences with 60 characters each, 
+the 1177 Vårdguiden subset is manually annotated and contains 927 sentences, 
+2740 annotations, out of which 1574 are disorder and findings, 546 are 
+pharmaceutical drug, and 620 are body structure.
+
+Texts from both Swedish Wikipedia and Läkartidningen were automatically annotated 
+using a list of medical seed terms. Sentences from 1177 Vårdguiden were manuually 
+annotated.
+"""
+
+_HOMEPAGE = "https://github.com/olofmogren/biomedical-ner-data-swedish/"
+
+_LICENSE = 'Creative Commons Attribution Share Alike 4.0 International'
+
+_URLS = {
+    "swedish_medical_ner_wiki": "https://raw.githubusercontent.com/olofmogren/biomedical-ner-data-swedish/master/Wiki_annotated_60.txt",
+    "swedish_medical_ner_lt": "https://raw.githubusercontent.com/olofmogren/biomedical-ner-data-swedish/master/LT_annotated_60.txt",
+    "swedish_medical_ner_1177": "https://raw.githubusercontent.com/olofmogren/biomedical-ner-data-swedish/master/1177_annotated_sentences.txt",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class SwedishMedicalNerDataset(datasets.GeneratorBasedBuilder):
+    """
+    Swedish medical named entity recognition
+
+    The dataset contains three subsets, namely "wiki", "lt" and "1177".
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in ["wiki", "lt", "1177"]:
+        BUILDER_CONFIGS.append(
+            BigBioConfig(
+                name=f"swedish_medical_ner_{subset}_source",
+                version=SOURCE_VERSION,
+                description="swedish_medical_ner source schema",
+                schema="source",
+                subset_id=f"swedish_medical_ner_{subset}",
+            )
+        )
+        BUILDER_CONFIGS.append(
+            BigBioConfig(
+                name=f"swedish_medical_ner_{subset}_bigbio_kb",
+                version=BIGBIO_VERSION,
+                description="swedish_medical_ner BigBio schema",
+                schema="bigbio_kb",
+                subset_id=f"swedish_medical_ner_{subset}",
+            )
+        )
+
+    DEFAULT_CONFIG_NAME = "swedish_medical_ner_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "sid": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "start": datasets.Value("int32"),
+                            "end": datasets.Value("int32"),
+                            "text": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS
+        filepath = dl_manager.download_and_extract(urls[self.config.subset_id])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    @staticmethod
+    def get_type(text):
+        """
+        Tagging format per the dataset authors
+        - Prenthesis, (): Disorder and Finding
+        - Brackets, []: Pharmaceutical Drug
+        - Curly brackets, {}: Body Structure
+
+        """
+        if text[0] == "(":
+            return "disorder_finding"
+        elif text[1] == "[":
+            return "pharma_drug"
+        return "body_structure"
+
+    @staticmethod
+    def get_source_example(uid, tagged):
+
+        ents, text = zip(*tagged)
+        text = list(text)
+
+        # build offsets
+        offsets = []
+        curr = 0
+        for span in text:
+            offsets.append((curr, curr + len(span)))
+            curr = curr + len(span)
+
+        text = "".join(text)
+        doc = {"sid": "s" + str(uid), "sentence": text, "entities": []}
+
+        # Create entities
+        for i, (start, end) in enumerate(offsets):
+            if ents[i] is not None:
+                doc["entities"].append(
+                    {
+                        "start": start,
+                        "end": end,
+                        "text": text[start:end],
+                        "type": ents[i],
+                    }
+                )
+
+        return uid, doc
+
+    @staticmethod
+    def get_bigbio_example(uid, tagged, remove_markup=True):
+        doc = {
+            "id": str(uid),
+            "document_id": "s" + str(uid),
+            "passages": [],
+            "entities": [],
+            "events": [],
+            "coreferences": [],
+            "relations": [],
+        }
+
+        ents, text = zip(*tagged)
+        text = list(text)
+        if remove_markup:
+            for i in range(len(ents)):
+                if ents[i] is not None:
+                    text[i] = re.sub(r"[(){}\[\]]", "", text[i]).strip()
+
+        # build offsets
+        offsets = []
+        curr = 0
+        for span in text:
+            offsets.append((curr, curr + len(span)))
+            curr = curr + len(span)
+
+        # Create passage
+        passage = "".join(text)
+        doc["passages"].append(
+            {
+                "id": str(uid) + "-passage-0",
+                "type": "sentence",
+                "text": [passage],
+                "offsets": [[0, len(passage)]],
+            }
+        )
+
+        # Create entities
+        ii = 0
+        for i, (start, end) in enumerate(offsets):
+            if ents[i] is not None:
+                doc["entities"].append(
+                    {
+                        "id": str(uid) + "-entity-" + str(ii),
+                        "type": ents[i],
+                        "text": [passage[start:end]],
+                        "offsets": [[start, end]],
+                        "normalized": [],
+                    }
+                )
+                ii += 1
+
+        return uid, doc
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        entity_rgx = re.compile(r"[(].+?[)]|[\[].+?[\]]|[{].+?[}]")
+
+        uid = 0
+        with open(filepath, "rt", encoding="utf-8") as file:
+            for i, row in enumerate(file):
+                row = row.replace("\n", "")
+                if row:
+                    curr = 0
+                    stack = []
+                    # match entities and build spans for sentence string
+                    for m in entity_rgx.finditer(row):
+                        span = m.group()
+                        if m.start() != 0:
+                            stack.append([None, row[curr : m.start()]])
+                        stack.append((self.get_type(span), span))
+                        curr = m.start() + len(span)
+                    stack.append([None, row[curr:]])
+
+                    if self.config.schema == "source":
+                        yield self.get_source_example(uid, stack)
+                    elif self.config.schema == "bigbio_kb":
+                        yield self.get_bigbio_example(uid, stack)
+                    uid += 1