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sd-nlp.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and Thomas Lemberger.
+#
+# 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.
+"""SourceDataNLP dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+from pathlib import Path
+
+import datasets
+
+
+_NER_LABEL_NAMES = [
+    "O",
+    "I-SMALL_MOLECULE",
+    "B-SMALL_MOLECULE",
+    "I-GENEPROD",
+    "B-GENEPROD",
+    "I-SUBCELLULAR",
+    "B-SUBCELLULAR",
+    "I-CELL",
+    "B-CELL",
+    "I-TISSUE",
+    "B-TISSUE",
+    "I-ORGANISM",
+    "B-ORGANISM",
+    "I-EXP_ASSAY",
+    "B-EXP_ASSAY",
+]
+_SEMANTIC_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
+_BORING_LABEL_NAMES = ["O", "I-BORING", "B-BORING"]
+_PANEL_START_NAMES = ["O", "B-PANEL_START"]
+
+_CITATION = """\
+@Unpublished{
+    huggingface: dataset,
+    title = {SourceData NLP},
+    authors={Thomas Lemberger, EMBO},
+    year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/EMBO/sd-nlp"
+
+_LICENSE = "CC-BY 4.0"
+
+_URLS = {
+    "NER": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
+    "ROLES": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
+    "BORING": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip",
+    "PANELIZATION": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_figs.zip",
+}
+
+class SourceDataNLP(datasets.GeneratorBasedBuilder):
+    """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="NER", version="0.0.1", description="Dataset for entity recognition"),
+        datasets.BuilderConfig(name="ROLES", version="0.0.1", description="Dataset for semantic roles."),
+        datasets.BuilderConfig(name="BORING", version="0.0.1", description="Dataset for semantic roles."),
+        datasets.BuilderConfig(
+            name="PANELIZATION",
+            version="0.0.1",
+            description="Dataset for figure legend segmentation into panel-specific legends.",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "NER"
+
+    def _info(self):
+        if self.config.name == "NER":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(num_classes=len(_NER_LABEL_NAMES), names=_NER_LABEL_NAMES)
+                    ),
+                    "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
+                }
+            )
+        elif self.config.name == "ROLES":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(
+                            num_classes=len(_SEMANTIC_ROLES_LABEL_NAMES), names=_SEMANTIC_ROLES_LABEL_NAMES
+                        )
+                    ),
+                    "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
+                }
+            )
+        elif self.config.name == "BORING":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(num_classes=len(_BORING_LABEL_NAMES), names=_BORING_LABEL_NAMES)
+                    ),
+                }
+            )
+        elif self.config.name == "PANELIZATION":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("int32")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(num_classes=len(_PANEL_START_NAMES), names=_PANEL_START_NAMES)
+                    ),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("input_ids", "labels"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """Returns SplitGenerators.
+        Uses local files if a data_dir is specified. Otherwise downloads the files from their official url."""
+        if self.config.data_dir:
+            data_dir = self.config.data_dir
+        else:
+            url = _URLS[self.config.name]
+            data_dir = dl_manager.download_and_extract(url)
+            if self.config.name in ["NER", "ROLES", "BORING"]:
+                data_dir += "/sd_panels"
+            elif self.config.name == "PANELIZATION":
+                data_dir += "/sd_figs"
+            else:
+                raise ValueError(f"unkonwn config name: {self.config.name}")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir + "/train.jsonl",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir + "/test.jsonl",
+                    "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir + "/eval.jsonl",
+                    "split": "eval",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples. This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        The key is not important, it's more here for legacy reason (legacy from tfds)"""
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "NER":
+                    labels_type = data["label_ids"]["entity_types"]
+                    tag_mask = [0 if tag == "O" else 1 for tag in labels_type]
+                    yield id_, {"input_ids": data["input_ids"], "labels": labels_type, "tag_mask": tag_mask}
+                elif self.config.name == "ROLES":
+                    labels_type = data["label_ids"]["entity_types"]
+                    geneprod = ["B-GENEPROD", "I-GENEPROD", "B-PROTEIN", "I-PROTEIN", "B-GENE", "I-GENE"]
+                    tag_mask = [1 if t in geneprod else 0 for t in labels_type]
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": data["label_ids"]["geneprod_roles"],
+                        "tag_mask": tag_mask,
+                    }
+                elif self.config.name == "BORING":
+                    yield id_, {"input_ids": data["input_ids"], "labels": data["label_ids"]["boring"]}
+                elif self.config.name == "PANELIZATION":
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": data["label_ids"]["panel_start"],
+                    }