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Datasets:
phucdev
/
fabner

Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K<n<100K
Tags:
manufacturing
2000-2020
License:
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets: []
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: FabNER is a manufacturing text dataset for Named Entity Recognition.
tags:
  - manufacturing
  - 2000-2020
dataset_info:
  - config_name: fabner
    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': E-MATE
              '4': S-MATE
              '5': B-MANP
              '6': I-MANP
              '7': E-MANP
              '8': S-MANP
              '9': B-MACEQ
              '10': I-MACEQ
              '11': E-MACEQ
              '12': S-MACEQ
              '13': B-APPL
              '14': I-APPL
              '15': E-APPL
              '16': S-APPL
              '17': B-FEAT
              '18': I-FEAT
              '19': E-FEAT
              '20': S-FEAT
              '21': B-PRO
              '22': I-PRO
              '23': E-PRO
              '24': S-PRO
              '25': B-CHAR
              '26': I-CHAR
              '27': E-CHAR
              '28': S-CHAR
              '29': B-PARA
              '30': I-PARA
              '31': E-PARA
              '32': S-PARA
              '33': B-ENAT
              '34': I-ENAT
              '35': E-ENAT
              '36': S-ENAT
              '37': B-CONPRI
              '38': I-CONPRI
              '39': E-CONPRI
              '40': S-CONPRI
              '41': B-MANS
              '42': I-MANS
              '43': E-MANS
              '44': S-MANS
              '45': B-BIOP
              '46': I-BIOP
              '47': E-BIOP
              '48': S-BIOP
    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: 1265830
    dataset_size: 6268493
  - config_name: fabner_bio
    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': B-MANP
              '4': I-MANP
              '5': B-MACEQ
              '6': I-MACEQ
              '7': B-APPL
              '8': I-APPL
              '9': B-FEAT
              '10': I-FEAT
              '11': B-PRO
              '12': I-PRO
              '13': B-CHAR
              '14': I-CHAR
              '15': B-PARA
              '16': I-PARA
              '17': B-ENAT
              '18': I-ENAT
              '19': B-CONPRI
              '20': I-CONPRI
              '21': B-MANS
              '22': I-MANS
              '23': B-BIOP
              '24': I-BIOP
    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: 1258672
    dataset_size: 6268493
  - config_name: fabner_simple
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': MATE
              '2': MANP
              '3': MACEQ
              '4': APPL
              '5': FEAT
              '6': PRO
              '7': CHAR
              '8': PARA
              '9': ENAT
              '10': CONPRI
              '11': MANS
              '12': BIOP
    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: 1233960
    dataset_size: 6268493
  - config_name: text2tech
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': Technological System
              '2': Method
              '3': Material
              '4': Technical Field
    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: 1192966
    dataset_size: 6268493
configs:
  - config_name: fabner
    data_files:
      - split: train
        path: fabner/train-*
      - split: validation
        path: fabner/validation-*
      - split: test
        path: fabner/test-*
    default: true
  - config_name: fabner_bio
    data_files:
      - split: train
        path: fabner_bio/train-*
      - split: validation
        path: fabner_bio/validation-*
      - split: test
        path: fabner_bio/test-*

Dataset Card for FabNER

Table of Contents

Dataset Description

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

Supported Tasks and Leaderboards

More Information Needed

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:

{
  "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

fabner

  • 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.
{"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}

fabner_bio

  • 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.
{"O": 0, "B-MATE": 1, "I-MATE": 2, "B-MANP": 3, "I-MANP": 4, "B-MACEQ": 5, "I-MACEQ": 6, "B-APPL": 7, "I-APPL": 8, "B-FEAT": 9, "I-FEAT": 10, "B-PRO": 11, "I-PRO": 12, "B-CHAR": 13, "I-CHAR": 14, "B-PARA": 15, "I-PARA": 16, "B-ENAT": 17, "I-ENAT": 18, "B-CONPRI": 19, "I-CONPRI": 20, "B-MANS": 21, "I-MANS": 22, "B-BIOP": 23, "I-BIOP": 24}

fabner_simple

  • 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.
{"O": 0, "MATE": 1, "MANP": 2, "MACEQ": 3, "APPL": 4, "FEAT": 5, "PRO": 6, "CHAR": 7, "PARA": 8, "ENAT": 9, "CONPRI": 10, "MANS": 11, "BIOP": 12}

text2tech

  • 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.
{"O": 0, "Technological System": 1, "Method": 2, "Material": 3, "Technical Field": 4}

Data Splits

Train Dev Test
fabner 9435 2183 2064

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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 for adding this dataset.