# 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. """FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.""" import datasets # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{DBLP:journals/jim/KumarS22, author = {Aman Kumar and Binil Starly}, title = {"FabNER": information extraction from manufacturing process science domain literature using named entity recognition}, journal = {J. Intell. Manuf.}, volume = {33}, number = {8}, pages = {2393--2407}, year = {2022}, url = {https://doi.org/10.1007/s10845-021-01807-x}, doi = {10.1007/s10845-021-01807-x}, timestamp = {Sun, 13 Nov 2022 17:52:57 +0100}, biburl = {https://dblp.org/rec/journals/jim/KumarS22.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ # You can copy an official description _DESCRIPTION = """\ FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition. It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process science research. For every word, there were categories/entity labels defined namely Material (MATE), Manufacturing Process (MANP), Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR), Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format: B=Beginning, I-Intermediate, O=Outside, E=End, S=Single. """ _HOMEPAGE = "https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "train": "https://figshare.com/ndownloader/files/28405854/S2-train.txt", "validation": "https://figshare.com/ndownloader/files/28405857/S3-val.txt", "test": "https://figshare.com/ndownloader/files/28405851/S1-test.txt", } def map_fabner_labels(string_tag): tag = string_tag[2:] # MATERIAL (FABNER) if tag == "MATE": return "Material" # MANUFACTURING PROCESS (FABNER) elif tag == "MANP": return "Method" # MACHINE/EQUIPMENT, MECHANICAL PROPERTIES, CHARACTERIZATION, ENABLING TECHNOLOGY (FABNER) elif tag in ["MACEQ", "PRO", "CHAR", "ENAT"]: return "Technological System" # APPLICATION (FABNER) elif tag == "APPL": return "Technical Field" # FEATURES, PARAMETERS, CONCEPT/PRINCIPLES, MANUFACTURING STANDARDS, BIOMEDICAL, O (FABNER) else: return "O" class FabNER(datasets.GeneratorBasedBuilder): """FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.""" VERSION = datasets.Version("1.2.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="fabner", version=VERSION, description="The FabNER dataset with the original BIOES tagging format"), datasets.BuilderConfig(name="fabner_bio", version=VERSION, description="The FabNER dataset with BIO tagging format"), datasets.BuilderConfig(name="fabner_simple", version=VERSION, description="The FabNER dataset with no tagging format"), datasets.BuilderConfig(name="text2tech", version=VERSION, description="The FabNER dataset mapped to the Text2Tech tag set"), ] DEFAULT_CONFIG_NAME = "fabner" def _info(self): entity_types = [ "MATE", # Material "MANP", # Manufacturing Process "MACEQ", # Machine/Equipment "APPL", # Application "FEAT", # Engineering Features "PRO", # Mechanical Properties "CHAR", # Process Characterization "PARA", # Process Parameters "ENAT", # Enabling Technology "CONPRI", # Concept/Principles "MANS", # Manufacturing Standards "BIOP", # BioMedical ] if self.config.name == "text2tech": class_labels = ["O", "Technological System", "Method", "Material", "Technical Field"] elif self.config.name == "fabner": class_labels = ["O"] for entity_type in entity_types: class_labels.extend( [ "B-" + entity_type, "I-" + entity_type, "E-" + entity_type, "S-" + entity_type, ] ) elif self.config.name == "fabner_bio": class_labels = ["O"] for entity_type in entity_types: class_labels.extend( [ "B-" + entity_type, "I-" + entity_type, ] ) else: class_labels = ["O"] + entity_types features = datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=class_labels ) ), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive downloaded_files = dl_manager.download_and_extract(_URLS) return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]}) for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath): # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. 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: splits = line.split(" ") tokens.append(splits[0]) ner_tag = splits[1].rstrip() if self.config.name == "fabner_simple": if ner_tag == "O": ner_tag = "O" else: ner_tag = ner_tag.split("-")[1] elif self.config.name == "fabner_bio": if ner_tag == "O": ner_tag = "O" else: ner_tag = ner_tag.replace("S-", "B-").replace("E-", "I-") elif self.config.name == "text2tech": ner_tag = map_fabner_labels(ner_tag) ner_tags.append(ner_tag) # last example if tokens: yield guid, { "id": str(guid), "tokens": tokens, "ner_tags": ner_tags, }