Create sd-nlp-non-tokenized.py

sd-nlp-non-tokenized.py

@@ -0,0 +1,229 @@
+# coding=utf-8
+# Copyright 2020 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.
+
+
+# template from : https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import pdb
+import datasets
+import os
+import logger
+
+_BASE_URL = "https://huggingface.co/datasets/EMBO/sd-nlp-non-tokenized/resolve/main/"
+
+class SourceDataNLP(datasets.GeneratorBasedBuilder):
+    """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology."""
+
+    _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_GENEPROD_ROLES_LABEL_NAMES =  ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]
+    _SEMANTIC_SMALL_MOL_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+    @Unpublished{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-non-tokenized"
+
+    _LICENSE = "CC-BY 4.0"
+
+    VERSION = datasets.Version("0.0.1")
+
+    _URLS = {
+        "NER": f"{_BASE_URL}sd_panels_general_tokenization.zip",
+        "ROLES": f"{_BASE_URL}sd_panels_general_tokenization.zip",
+        "BORING": f"{_BASE_URL}sd_panels_general_tokenization.zip",
+        "PANELIZATION": f"{_BASE_URL}sd_fig_general_tokenization.zip",
+    }
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="NER", version="0.0.1", description="Dataset for entity recognition"),
+        datasets.BuilderConfig(name="GENEPROD_ROLES", version="0.0.1", description="Dataset for semantic roles."),
+        datasets.BuilderConfig(name="SMALL_MOL_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(
+                {
+                    "words": datasets.Sequence(feature=datasets.Value("string")),
+                    "label_ids": datasets.Sequence(
+                        feature=datasets.ClassLabel(num_classes=len(self._NER_LABEL_NAMES), names=self._NER_LABEL_NAMES)
+                    ),
+                }
+            )
+        elif self.config.name == "GENEPROD_ROLES":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(
+                            num_classes=len(self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES), names=self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES
+                        )
+                    ),
+
+                }
+            )
+        elif self.config.name == "SMALL_MOL_ROLES":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(
+                            num_classes=len(self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES), names=self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES
+                        )
+                    ),
+                }
+            )
+        elif self.config.name == "BORING":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(num_classes=len(self._BORING_LABEL_NAMES), names=self._BORING_LABEL_NAMES)
+                    ),
+                }
+            )
+        elif self.config.name == "PANELIZATION":
+            features = datasets.Features(
+                {
+                    "input_ids": datasets.Sequence(feature=datasets.Value("string")),
+                    "labels": datasets.Sequence(
+                        feature=datasets.ClassLabel(num_classes=len(self._PANEL_START_NAMES), names=self._PANEL_START_NAMES)
+                    ),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=self._DESCRIPTION,
+            features=features,
+            supervised_keys=("input_ids", "labels"),
+            homepage=self._HOMEPAGE,
+            license=self._LICENSE,
+            citation=self._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."""
+        url = self._URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(url)
+        if self.config.name in ["NER", "GENEPROD_ROLES", "SMALL_MOL_ROLES", "BORING"]:
+            data_dir += "/sd_panels_general_tokenization"
+        elif self.config.name == "PANELIZATION":
+            data_dir += "/sd_fig_general_tokenization"
+        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"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir + "/test.jsonl"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir + "/eval.jsonl"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """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:
+            # logger.info("⏳ Generating examples from = %s", filepath)
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "NER":
+                    labels = data["label_ids"]["entity_types"]
+                    tag_mask = [0 if tag == "O" else 1 for tag in labels]
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": labels,
+                        "tag_mask": tag_mask,
+                    }
+                elif self.config.name == "GENEPROD_ROLES":
+                    labels = 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]
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": data["label_ids"]["geneprod_roles"],
+                        "tag_mask": tag_mask,
+                    }
+                elif self.config.name == "SMALL_MOL_ROLES":
+                    labels = data["label_ids"]["entity_types"]
+                    small_mol = ["B-SMALL_MOLECULE", "I-SMALL_MOLECULE"]
+                    tag_mask = [1 if t in small_mol else 0 for t in labels]
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": data["label_ids"]["small_mol_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":
+                    labels = data["label_ids"]["panel_start"]
+                    tag_mask = [1 if t == "B-PANEL_START" else 0 for t in labels]
+                    yield id_, {
+                        "input_ids": data["input_ids"],
+                        "labels": data["label_ids"]["panel_start"],
+                        "tag_mask": tag_mask,
+                    }