Upload bc2gm.py

bc2gm.py

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+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{smith2008overview,
+        title={Overview of BioCreative II gene mention recognition},
+        author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and others},
+        journal={Genome biology},
+        volume={9},
+        number={S2},
+        pages={S2},
+        year={2008},
+        publisher={Springer}
+}"""
+
+_DESCRIPTION = """\
+Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
+In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
+A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
+Here we present brief descriptions of all the methods used and a statistical analysis of the results.
+We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible,
+and furthermore that the best result makes use of the lowest scoring submissions.
+For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/
+The original dataset can be downloaded from: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-ii-corpus/
+This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/"
+_URL = "https://github.com/spyysalo/bc2gm-corpus/raw/master/conll/"
+_TRAINING_FILE = "train.tsv"
+_DEV_FILE = "devel.tsv"
+_TEST_FILE = "test.tsv"
+
+
+class Bc2gmCorpusConfig(datasets.BuilderConfig):
+    """BuilderConfig for Bc2gmCorpus"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Bc2gmCorpus.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Bc2gmCorpusConfig, self).__init__(**kwargs)
+
+class Bc2gmCorpus(datasets.GeneratorBasedBuilder):
+    """Bc2gmCorpus dataset."""
+
+    BUILDER_CONFIGS = [
+        Bc2gmCorpusConfig(name="bc2gm_corpus", version=datasets.Version("1.0.0"), description="bc2gm corpus"),
+    ]
+
+    def _info(self):
+
+        custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
+                'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE', 
+                'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES']    
+
+        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=custom_names
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        shift = 0
+        logger.info("⏳ Generating examples from = %s", 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(str(int(splits[1].rstrip()+shift)))
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }
+
+
+Bc2gmCorpus()