Upload species_800.py

species_800.py

@@ -0,0 +1,150 @@
+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text"""
+
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{pafilis2013species,
+         title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
+         author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christos and Jensen, Lars Juhl},
+         journal={PloS one},
+         volume={8},
+         number={6},
+         pages={e65390},
+         year={2013},
+         publisher={Public Library of Science}
+}
+"""
+
+_DESCRIPTION = """\
+We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition,
+which we here use to identifynames of species and other taxa in text. The tool, SPECIES, is more than an order of
+magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard
+corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus
+comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of
+organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database
+and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists.
+"""
+
+_HOMEPAGE = "https://species.jensenlab.org/"
+_URL = "https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/"
+_BIOBERT_NER_DATASET_DIRECTORY = "s800"
+_TRAINING_FILE = "train.tsv"
+_DEV_FILE = "devel.tsv"
+_TEST_FILE = "test.tsv"
+
+
+class Species800Config(datasets.BuilderConfig):
+    """BuilderConfig for Species800"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Species800.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Species800Config, self).__init__(**kwargs)
+
+
+class Species800(datasets.GeneratorBasedBuilder):
+    """Species800 dataset."""
+
+    BUILDER_CONFIGS = [
+        Species800Config(name="species_800", version=datasets.Version("1.0.0"), description="Species800 dataset"),
+    ]
+
+    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 = {
+            "biobert_ner_datasets": _URL,
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        dataset_directory = os.path.join(downloaded_files["biobert_ner_datasets"], _BIOBERT_NER_DATASET_DIRECTORY)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dataset_directory, _TRAINING_FILE)}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dataset_directory, _DEV_FILE)}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dataset_directory, _TEST_FILE)}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        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])
+                    if(splits[1].rstrip()=="B"):
+                        ner_tags.append("B-SPECIES")
+                    elif(splits[1].rstrip()=="I"):
+                        ner_tags.append("I-SPECIES")
+                    else:
+                        ner_tags.append(splits[1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }