
# 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

#### 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.

```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}
```

#### 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.

```json
{"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.

```json
{"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.

```json
{"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](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.
task_categories:token-classification task_ids:named-entity-recognition annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:10K<n<100K language:en license:other manufacturing 2000-2020 region:us
{"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": "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"}}}}], "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}, {"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": 3793613, "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": 3793613, "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": 3793613, "dataset_size": 6268493}]}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #manufacturing #2000-2020 #region-us 

Dataset Card for [Dataset Name]
===============================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ 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
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* 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:


### 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.


#### 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.


#### 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.


#### 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.


### Data Splits



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


### Contributions


Thanks to @phucdev for adding this dataset.



### 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: ### 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. #### 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. #### 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. #### 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. ### Data Splits



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 ### Contributions


Thanks to @phucdev for adding this dataset.
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #manufacturing #2000-2020 #region-us 
 ### 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: ### 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. #### 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. #### 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. #### 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. ### Data Splits



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 ### Contributions


Thanks to @phucdev for adding this dataset.
passage: TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #manufacturing #2000-2020 #region-us 
### 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:### 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.