Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K<n<100K
License:
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
Dataset Card for FabNER
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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
- Paper: "FabNER": information extraction from manufacturing process science domain literature using named entity recognition
- 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
Supported Tasks and Leaderboards
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, astringfeature.tokens: the list of tokens of this sentence, alistofstringfeatures.ner_tags: the list of entity tags, alistof 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, astringfeature.tokens: the list of tokens of this sentence, alistofstringfeatures.ner_tags: the list of entity tags, alistof 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, astringfeature.tokens: the list of tokens of this sentence, alistofstringfeatures.ner_tags: the list of entity tags, alistof 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, astringfeature.tokens: the list of tokens of this sentence, alistofstringfeatures.ner_tags: the list of entity tags, alistof 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
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
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.