Create BLURB.py

Add BC5CDR-chem-IOB dataset

BLURB.py

@@ -0,0 +1,189 @@
+"""Loading script for the BLURB (Biomedical Language Understanding and Reasoning Benchmark)
+benchmark for biomedical NLP."""
+
+import json
+from pathlib import Path
+import datasets
+import shutil
+
+
+_CITATION = """\
+@article{2022,
+   title={Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
+   volume={3},
+   ISSN={2637-8051},
+   url={http://dx.doi.org/10.1145/3458754},
+   DOI={10.1145/3458754},
+   number={1},
+   journal={ACM Transactions on Computing for Healthcare},
+   publisher={Association for Computing Machinery (ACM)},
+   author={Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
+   year={2022},
+   month={Jan},
+   pages={1–23} 
+   }
+"""
+
+_DESCRIPTION = """BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
+is a comprehensive benchmark for biomedical NLP, with 13 biomedical NLP datasets in 6 
+tasks (NER, PICO, Relation Extraction, Sentence similarity, document classification, question answering). 
+Our aim is to facilitate investigations of biomedical natural language processing 
+with a specific focus on language model pretraining and to help accelerate progress in universal Biomedical 
+NLP applications. The table below compares the datasets comprising BLURB versus the various datasets used in 
+previous Biomedical and Clinical BERT language models."""
+
+_HOMEPAGE = "https://microsoft.github.io/BLURB/index.html"
+
+_LICENSE = "TBD"
+
+
+_VERSION = "1.0.0"
+
+DATA_DIR = "blurb/"
+
+BASE_URL_NER = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/"
+
+logger = datasets.logging.get_logger(__name__)
+
+CITATION_BC5_CHEM = """@article{article,
+author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
+year = {2016},
+month = {05},
+pages = {baw068},
+title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
+volume = {2016},
+journal = {Database},
+doi = {10.1093/database/baw068}
+}
+"""
+
+class BlurbConfig(datasets.BuilderConfig):
+    """BuilderConfig for BLURB."""
+
+    def __init__(self, task, data_url, citation, url, label_classes=("False", "True"), **kwargs):
+        """BuilderConfig for BLURB.
+        Args:
+            task: `string` task the dataset is used for: 'ner', 'pico', 'rel-ext', 'sent-sim', 'doc-clas', 'qa'
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the data files from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        super(BlurbConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.task = task
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        if self.task == 'ner':
+            self.features = datasets.Features(
+                                              {"id": datasets.Value("string"),
+                                                  "tokens": datasets.Sequence(datasets.Value("string")),
+                                                  "ner_tags": datasets.Sequence(
+                                                      datasets.features.ClassLabel(names=self.label_classes)
+                                                  )}
+                                          )
+            self.base_url = f"{BASE_URL_NER}{self.name}/"
+            self.urls = {
+                        "train": f"{self.base_url}{'train.tsv'}",
+                        "validation": f"{self.base_url}{'devel.tsv'}",
+                        "test": f"{self.base_url}{'test.tsv'}"
+                        }
+
+
+class Blurb(datasets.GeneratorBasedBuilder):
+  """BLURB benchmark dataset for Biomedical Language Understanding and Reasoning Benchmark."""
+
+  VERSION = datasets.Version("1.0.0")
+
+  BUILDER_CONFIGS = [
+    BlurbConfig(name='BC5CDR-chem-IOB', task='ner', label_classes=['O', 'B-Chemical', 'I-Chemical'],
+                    version=datasets.Version(_VERSION),
+                    description='BC5-CHEM',
+                    citation=CITATION_BC5_CHEM)
+  ]
+
+
+  def _info(self):
+      return datasets.DatasetInfo(
+          description=_DESCRIPTION,
+          features=datasets.Features(
+              {
+                  "id": datasets.Value("string"),
+                  "tokens": datasets.Sequence(datasets.Value("string")),
+                  "ner_tags": datasets.Sequence(
+                      datasets.features.ClassLabel(
+                          names=[
+                              "O",
+                              "B-Chemical",
+                              "I-Chemical",
+                          ]
+                      )
+                  ),
+              }
+          ),
+          supervised_keys=None,
+          homepage=_HOMEPAGE,
+          citation=_CITATION,
+      )
+
+  def _split_generators(self, dl_manager):
+      """Returns SplitGenerators."""
+      
+      if self.config.task == 'ner':
+        downloaded_files = dl_manager.download_and_extract(self.config.urls)
+
+      return _self.ner_split_generator(downloaded_files)
+
+
+  def _generate_examples(self, filepath):
+    print("Before the download")
+    logger.info("⏳ Generating examples from = %s", filepath)
+
+    if self.config.task == 'ner':
+        self._ner_example_generator(filepath)
+
+
+  def _ner_split_generator(self, downloaded_files):
+      return [
+          datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                  gen_kwargs={"filepath": downloaded_files["train"]}),
+          datasets.SplitGenerator(name=datasets.Split.VALIDATION,
+                                  gen_kwargs={"filepath": downloaded_files["validation"]}),
+          datasets.SplitGenerator(name=datasets.Split.TEST,
+                                  gen_kwargs={"filepath": downloaded_files["test"]}),
+      ]
+
+
+  def _ner_example_generator(self, filepath):
+    with open(filepath, encoding="utf-8") as f:
+        guid = 0
+        tokens = []
+        ner_tags = []
+        for line in f:
+            if line == "" or line == "\n":
+                if tokens:
+                    yield guid, {
+                        "id": str(guid),
+                        "tokens": tokens,
+                        "ner_tags": ner_tags,
+                    }
+                    guid += 1
+                    tokens = []
+                    ner_tags = []
+            else:
+                # tokens are tab separated
+                splits = line.split("\t")
+                tokens.append(splits[0])
+                ner_tags.append(splits[1].rstrip())
+        # last example
+        yield guid, {
+            "id": str(guid),
+            "tokens": tokens,
+            "ner_tags": ner_tags,
+        }