Delete loading script

fabner.py

@@ -1,230 +0,0 @@
-# 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,
-                }