# 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 os import datasets _BASE_URL = "https://huggingface.co/datasets/EMBO/SourceData/resolve/main/" class SourceData(datasets.GeneratorBasedBuilder): """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology.""" _NER_LABEL_NAMES = [ "O", "B-SMALL_MOLECULE", "I-SMALL_MOLECULE", "B-GENEPROD", "I-GENEPROD", "B-SUBCELLULAR", "I-SUBCELLULAR", "B-CELL_TYPE", "I-CELL_TYPE", "B-TISSUE", "I-TISSUE", "B-ORGANISM", "I-ORGANISM", "B-EXP_ASSAY", "I-EXP_ASSAY", "B-DISEASE", "I-DISEASE", "B-CELL_LINE", "I-CELL_LINE", ] _SEMANTIC_ROLES = [ "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "B-MEASURED_VAR", "I-MEASURED_VAR", ] _PANEL_START_NAMES = ["O", "B-PANEL_START", "I-PANEL_START"] _ROLES_MULTI = ["O", "GENEPROD", "SMALL_MOLECULE"] _CITATION = """\ @article{abreu2023sourcedata, title={The SourceData-NLP dataset: integrating curation into scientific publishing for training large language models}, author={Abreu-Vicente, Jorge and Sonntag, Hannah and Eidens, Thomas and Lemberger, Thomas}, journal={arXiv preprint arXiv:2310.20440}, year={2023} } """ _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/SourceData" _LICENSE = "CC-BY 4.0" DEFAULT_CONFIG_NAME = "NER" _LATEST_VERSION = "2.0.3" # Should this be updated to 2.0.3 def _info(self): VERSION = ( self.config.version if self.config.version not in ["0.0.0", "latest"] else self._LATEST_VERSION ) self._URLS = { "NER": f"{_BASE_URL}token_classification/v_{VERSION}/ner/", "PANELIZATION": f"{_BASE_URL}token_classification/v_{VERSION}/panelization/", "ROLES_GP": f"{_BASE_URL}token_classification/v_{VERSION}/roles_gene/", "ROLES_SM": f"{_BASE_URL}token_classification/v_{VERSION}/roles_small_mol/", "ROLES_MULTI": f"{_BASE_URL}token_classification/v_{VERSION}/roles_multi/", "FULL": os.path.join( _BASE_URL, "bigbio", # f"v_{VERSION}", ), } self.BUILDER_CONFIGS = [ datasets.BuilderConfig( name="NER", version=VERSION, description="Dataset for named-entity recognition.", ), datasets.BuilderConfig( name="PANELIZATION", version=VERSION, description="Dataset to separate figure captions into panels.", ), datasets.BuilderConfig( name="ROLES_GP", version=VERSION, description="Dataset for semantic roles of gene products.", ), datasets.BuilderConfig( name="ROLES_SM", version=VERSION, description="Dataset for semantic roles of small molecules.", ), datasets.BuilderConfig( name="ROLES_MULTI", version=VERSION, description="Dataset to train roles. ROLES_GP and ROLES_SM at once.", ), datasets.BuilderConfig( name="FULL", version=VERSION, description="Full dataset including all NER + entity linking annotations, links to figure images, etc.", ), # datasets.BuilderConfig( # name="BIGBIO_KB", # version=VERSION, # description="Full dataset formatted according to BigBio KB schema (see https://huggingface.co/bigbio). Includes all NER + entity linking annotations.", # ), ] if self.config.name in ["NER", "default"]: features = datasets.Features( { "words": datasets.Sequence(feature=datasets.Value("string")), "labels": datasets.Sequence( feature=datasets.ClassLabel( num_classes=len(self._NER_LABEL_NAMES), names=self._NER_LABEL_NAMES, ) ), # "is_category": datasets.Sequence(feature=datasets.Value("int8")), "tag_mask": datasets.Sequence(feature=datasets.Value("int8")), "text": datasets.Value("string"), } ) elif self.config.name == "ROLES_GP": features = datasets.Features( { "words": datasets.Sequence(feature=datasets.Value("string")), "labels": datasets.Sequence( feature=datasets.ClassLabel( num_classes=len(self._SEMANTIC_ROLES), names=self._SEMANTIC_ROLES, ) ), # "is_category": datasets.Sequence(feature=datasets.Value("int8")), "tag_mask": datasets.Sequence(feature=datasets.Value("int8")), "text": datasets.Value("string"), } ) elif self.config.name == "ROLES_SM": features = datasets.Features( { "words": datasets.Sequence(feature=datasets.Value("string")), "labels": datasets.Sequence( feature=datasets.ClassLabel( num_classes=len(self._SEMANTIC_ROLES), names=self._SEMANTIC_ROLES, ) ), # "is_category": datasets.Sequence(feature=datasets.Value("int8")), "tag_mask": datasets.Sequence(feature=datasets.Value("int8")), "text": datasets.Value("string"), } ) elif self.config.name == "ROLES_MULTI": features = datasets.Features( { "words": datasets.Sequence(feature=datasets.Value("string")), "labels": datasets.Sequence( feature=datasets.ClassLabel( num_classes=len(self._SEMANTIC_ROLES), names=self._SEMANTIC_ROLES, ) ), "is_category": datasets.Sequence( feature=datasets.ClassLabel( num_classes=len(self._ROLES_MULTI), names=self._ROLES_MULTI ) ), "tag_mask": datasets.Sequence(feature=datasets.Value("int8")), "text": datasets.Value("string"), } ) elif self.config.name == "PANELIZATION": features = datasets.Features( { "words": datasets.Sequence(feature=datasets.Value("string")), "labels": datasets.Sequence( feature=datasets.ClassLabel( num_classes=len(self._PANEL_START_NAMES), names=self._PANEL_START_NAMES, ) ), "tag_mask": datasets.Sequence(feature=datasets.Value("int8")), } ) elif self.config.name == "FULL": features = datasets.Features( { "doi": datasets.Value("string"), "abstract": datasets.Value("string"), # "split": datasets.Value("string"), "figures": [ { "fig_id": datasets.Value("string"), "label": datasets.Value("string"), "fig_graphic_url": datasets.Value("string"), "panels": [ { "panel_id": datasets.Value("string"), "text": datasets.Value("string"), "panel_graphic_url": datasets.Value("string"), "entities": [ { "annotation_id": datasets.Value("string"), "source": datasets.Value("string"), "category": datasets.Value("string"), "entity_type": datasets.Value("string"), "role": datasets.Value("string"), "text": datasets.Value("string"), "ext_ids": datasets.Value("string"), "norm_text": datasets.Value("string"), "ext_dbs": datasets.Value("string"), "in_caption": datasets.Value("bool"), "ext_names": datasets.Value("string"), "ext_tax_ids": datasets.Value("string"), "ext_tax_names": datasets.Value("string"), "ext_urls": datasets.Value("string"), "offsets": [datasets.Value("int64")], } ], } ], } ], } ) return datasets.DatasetInfo( description=self._DESCRIPTION, features=features, supervised_keys=("words", "label_ids"), 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. """ try: config_name = self.config.name if self.config.name != "default" else "NER" if config_name == "FULL": url = os.path.join( self._URLS[config_name], # "source_data_full.zip" "source_data_json_splits_2.0.2.zip", ) data_dir = dl_manager.download_and_extract(url) data_files = [ os.path.join(data_dir, filename) for filename in ["train.jsonl", "test.jsonl", "validation.jsonl"] ] else: urls = [ os.path.join(self._URLS[config_name], "train.jsonl"), os.path.join(self._URLS[config_name], "test.jsonl"), os.path.join(self._URLS[config_name], "validation.jsonl"), ] data_files = dl_manager.download(urls) except: 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_files[0]}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": data_files[1]}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files[2]}, ), ] 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)""" no_panels = 0 no_entities = 0 has_panels = 0 has_entities = 0 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.strip()) if self.config.name in ["NER", "default"]: yield id_, { "words": data["words"], "labels": data["labels"], "tag_mask": data["is_category"], "text": data["text"], } elif self.config.name == "ROLES_GP": yield id_, { "words": data["words"], "labels": data["labels"], "tag_mask": data["is_category"], "text": data["text"], } elif self.config.name == "ROLES_MULTI": labels = data["labels"] tag_mask = [1 if t != 0 else 0 for t in labels] yield id_, { "words": data["words"], "labels": data["labels"], "tag_mask": tag_mask, "is_category": data["is_category"], "text": data["text"], } elif self.config.name == "ROLES_SM": yield id_, { "words": data["words"], "labels": data["labels"], "tag_mask": data["is_category"], "text": data["text"], } elif self.config.name == "PANELIZATION": labels = data["labels"] tag_mask = [1 if t == "B-PANEL_START" else 0 for t in labels] yield id_, { "words": data["words"], "labels": data["labels"], "tag_mask": tag_mask, } elif self.config.name == "FULL": doc_figs = data["figures"] all_figures = [] for fig in doc_figs: all_panels = [] figure = { "fig_id": fig["fig_id"], "label": fig["label"], "fig_graphic_url": fig["fig_graphic_url"], } for p in fig["panels"]: panel = { "panel_id": p["panel_id"], "text": p["text"].strip(), "panel_graphic_url": p["panel_graphic_url"], "entities": [ { "annotation_id": t["tag_id"], "source": t["source"], "category": t["category"], "entity_type": t["entity_type"], "role": t["role"], "text": t["text"], "ext_ids": t["ext_ids"], "norm_text": t["norm_text"], "ext_dbs": t["ext_dbs"], "in_caption": bool(t["in_caption"]), "ext_names": t["ext_names"], "ext_tax_ids": t["ext_tax_ids"], "ext_tax_names": t["ext_tax_names"], "ext_urls": t["ext_urls"], "offsets": t["local_offsets"], } for t in p["tags"] ], } for e in panel["entities"]: assert type(e["offsets"]) == list if len(panel["entities"]) == 0: no_entities += 1 continue else: has_entities += 1 all_panels.append(panel) figure["panels"] = all_panels # Pass on all figures that aren't split into panels if len(all_panels) == 0: no_panels += 1 continue else: has_panels += 1 all_figures.append(figure) output = { "doi": data["doi"], "abstract": data["abstract"], "figures": all_figures, } yield id_, output