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README.md

@@ -1,273 +0,0 @@
----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- found
-license:
-- other
-multilinguality:
-- monolingual
-pretty_name: FabNER is a manufacturing text dataset for Named Entity Recognition.
-size_categories:
-- 10K<n<100K
-source_datasets: []
-tags:
-- manufacturing
-- 2000-2020
-task_categories:
-- token-classification
-task_ids:
-- named-entity-recognition
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-MATE
-          '2': I-MATE
-          '3': O-MATE
-          '4': E-MATE
-          '5': S-MATE
-          '6': B-MANP
-          '7': I-MANP
-          '8': O-MANP
-          '9': E-MANP
-          '10': S-MANP
-          '11': B-MACEQ
-          '12': I-MACEQ
-          '13': O-MACEQ
-          '14': E-MACEQ
-          '15': S-MACEQ
-          '16': B-APPL
-          '17': I-APPL
-          '18': O-APPL
-          '19': E-APPL
-          '20': S-APPL
-          '21': B-FEAT
-          '22': I-FEAT
-          '23': O-FEAT
-          '24': E-FEAT
-          '25': S-FEAT
-          '26': B-PRO
-          '27': I-PRO
-          '28': O-PRO
-          '29': E-PRO
-          '30': S-PRO
-          '31': B-CHAR
-          '32': I-CHAR
-          '33': O-CHAR
-          '34': E-CHAR
-          '35': S-CHAR
-          '36': B-PARA
-          '37': I-PARA
-          '38': O-PARA
-          '39': E-PARA
-          '40': S-PARA
-          '41': B-ENAT
-          '42': I-ENAT
-          '43': O-ENAT
-          '44': E-ENAT
-          '45': S-ENAT
-          '46': B-CONPRI
-          '47': I-CONPRI
-          '48': O-CONPRI
-          '49': E-CONPRI
-          '50': S-CONPRI
-          '51': B-MANS
-          '52': I-MANS
-          '53': O-MANS
-          '54': E-MANS
-          '55': S-MANS
-          '56': B-BIOP
-          '57': I-BIOP
-          '58': O-BIOP
-          '59': E-BIOP
-          '60': S-BIOP
-  config_name: fabner
-  splits:
-  - name: train
-    num_bytes: 4394010
-    num_examples: 9435
-  - name: validation
-    num_bytes: 934347
-    num_examples: 2183
-  - name: test
-    num_bytes: 940136
-    num_examples: 2064
-  download_size: 3793613
-  dataset_size: 6268493
----
-
-# Dataset Card for [Dataset Name]
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407](https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407)
-- **Paper:** ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
-- **Size of downloaded dataset files:** 3.79 MB
-- **Size of the generated dataset:** 6.27 MB
-
-### Dataset Summary
-
-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.
-
-For details about the dataset, please refer to the paper: ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-The language in the dataset is English.
-
-## Dataset Structure
-
-### Data Instances
-
-- **Size of downloaded dataset files:** 3.79 MB
-- **Size of the generated dataset:** 6.27 MB
-  
-An example of 'train' looks as follows:
-```json
-{
-  "id": "0", 
-  "tokens": ["Revealed", "the", "location-specific", "flow", "patterns", "and", "quantified", "the", "speeds", "of", "various", "types", "of", "flow", "."], 
-  "ner_tags": [0, 0, 0, 46, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-}
-```
-
-### Data Fields
-
-- `id`: the instance id of this sentence, a `string` feature.
-- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
-- `ner_tags`: the list of entity tags, a `list` of classification labels.
-
-```json
-{"O": 0, "B-MATE": 1, "I-MATE": 2, "O-MATE": 3, "E-MATE": 4, "S-MATE": 5, "B-MANP": 6, "I-MANP": 7, "O-MANP": 8, "E-MANP": 9, "S-MANP": 10, "B-MACEQ": 11, "I-MACEQ": 12, "O-MACEQ": 13, "E-MACEQ": 14, "S-MACEQ": 15, "B-APPL": 16, "I-APPL": 17, "O-APPL": 18, "E-APPL": 19, "S-APPL": 20, "B-FEAT": 21, "I-FEAT": 22, "O-FEAT": 23, "E-FEAT": 24, "S-FEAT": 25, "B-PRO": 26, "I-PRO": 27, "O-PRO": 28, "E-PRO": 29, "S-PRO": 30, "B-CHAR": 31, "I-CHAR": 32, "O-CHAR": 33, "E-CHAR": 34, "S-CHAR": 35, "B-PARA": 36, "I-PARA": 37, "O-PARA": 38, "E-PARA": 39, "S-PARA": 40, "B-ENAT": 41, "I-ENAT": 42, "O-ENAT": 43, "E-ENAT": 44, "S-ENAT": 45, "B-CONPRI": 46, "I-CONPRI": 47, "O-CONPRI": 48, "E-CONPRI": 49, "S-CONPRI": 50, "B-MANS": 51, "I-MANS": 52, "O-MANS": 53, "E-MANS": 54, "S-MANS": 55, "B-BIOP": 56, "I-BIOP": 57, "O-BIOP": 58, "E-BIOP": 59, "S-BIOP": 60}
-```
-
-### Data Splits
-
-|        | Train | Dev  | Test |
-|--------|-------|------|------|
-| fabner | 9435  | 2183 | 2064 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@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}
-}
-```
-
-### Contributions
-
-Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

fabner.py

@@ -1,213 +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",
-}
-
-class FabNER(datasets.GeneratorBasedBuilder):
-    """FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # 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"),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=[
-                            "O",
-                            "B-MATE",   # Material
-                            "I-MATE",
-                            "O-MATE",
-                            "E-MATE",
-                            "S-MATE",
-                            "B-MANP",   # Manufacturing Process
-                            "I-MANP",
-                            "O-MANP",
-                            "E-MANP",
-                            "S-MANP",
-                            "B-MACEQ",  # Machine/Equipment
-                            "I-MACEQ",
-                            "O-MACEQ",
-                            "E-MACEQ",
-                            "S-MACEQ",
-                            "B-APPL",   # Application
-                            "I-APPL",
-                            "O-APPL",
-                            "E-APPL",
-                            "S-APPL",
-                            "B-FEAT",   # Engineering Features
-                            "I-FEAT",
-                            "O-FEAT",
-                            "E-FEAT",
-                            "S-FEAT",
-                            "B-PRO",    # Mechanical Properties
-                            "I-PRO",
-                            "O-PRO",
-                            "E-PRO",
-                            "S-PRO",
-                            "B-CHAR",   # Process Characterization
-                            "I-CHAR",
-                            "O-CHAR",
-                            "E-CHAR",
-                            "S-CHAR",
-                            "B-PARA",   # Process Parameters
-                            "I-PARA",
-                            "O-PARA",
-                            "E-PARA",
-                            "S-PARA",
-                            "B-ENAT",   # Enabling Technology
-                            "I-ENAT",
-                            "O-ENAT",
-                            "E-ENAT",
-                            "S-ENAT",
-                            "B-CONPRI",     # Concept/Principles
-                            "I-CONPRI",
-                            "O-CONPRI",
-                            "E-CONPRI",
-                            "S-CONPRI",
-                            "B-MANS",   # Manufacturing Standards
-                            "I-MANS",
-                            "O-MANS",
-                            "E-MANS",
-                            "S-MANS",
-                            "B-BIOP",   # BioMedical
-                            "I-BIOP",
-                            "O-BIOP",
-                            "E-BIOP",
-                            "S-BIOP",
-                        ]
-                    )
-                ),
-            }
-        )
-        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_tags.append(splits[1].rstrip())
-            # last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "ner_tags": ner_tags,
-                }

fabner/fabner-test.parquet

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fabner/fabner-train.parquet

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fabner/fabner-validation.parquet

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