
# Dataset Card for [Dataset Name]

## 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:** [PT Gria Inovasi Teknologi](https://grit.id/)
- **Repository:** [Nergrit Corpus](https://github.com/grit-id/nergrit-corpus)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Taufiqur Rohman](mailto:taufiq@grit.id)

### Dataset Summary

Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, 
and Sentiment Analysis developed by [PT Gria Inovasi Teknologi (GRIT)](https://grit.id/). 

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

Indonesian

## Dataset Structure

A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 
```
{'id': '0',
 'tokens': ['Gubernur', 'Bank', 'Indonesia', 'menggelar', 'konferensi', 'pers'],
 'ner_tags': [9, 28, 28, 38, 38, 38],
}
```
### Data Instances

[More Information Needed]

### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token

#### Named Entity Recognition
The ner_tags correspond to this list:
```
"B-CRD", "B-DAT", "B-EVT", "B-FAC", "B-GPE", "B-LAN", "B-LAW", "B-LOC", "B-MON", "B-NOR", 
"B-ORD", "B-ORG", "B-PER", "B-PRC", "B-PRD", "B-QTY", "B-REG", "B-TIM", "B-WOA",
"I-CRD", "I-DAT", "I-EVT", "I-FAC", "I-GPE", "I-LAN", "I-LAW", "I-LOC", "I-MON", "I-NOR",
"I-ORD", "I-ORG", "I-PER", "I-PRC", "I-PRD", "I-QTY", "I-REG", "I-TIM", "I-WOA", "O",
```
The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word. The dataset contains 19 following entities
```
    'CRD': Cardinal
    'DAT': Date
    'EVT': Event
    'FAC': Facility
    'GPE': Geopolitical Entity
    'LAW': Law Entity (such as Undang-Undang)
    'LOC': Location
    'MON': Money
    'NOR': Political Organization
    'ORD': Ordinal
    'ORG': Organization
    'PER': Person
    'PRC': Percent
    'PRD': Product
    'QTY': Quantity
    'REG': Religion
    'TIM': Time
    'WOA': Work of Art
    'LAN': Language
```
#### Sentiment Analysis
The ner_tags correspond to this list:
```
"B-NEG", "B-NET", "B-POS",
"I-NEG", "I-NET", "I-POS",
"O",
```

#### Statement Extraction
The ner_tags correspond to this list:
```
"B-BREL", "B-FREL", "B-STAT", "B-WHO",
"I-BREL", "I-FREL", "I-STAT", "I-WHO", 
"O"
```
The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word.

### Data Splits

The dataset is splitted in to train, validation and test sets.

## 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?
The annotators are listed in the
[Nergrit Corpus repository](https://github.com/grit-id/nergrit-corpus)

### 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

[More Information Needed]

### Contributions

Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
task_categories:token-classification task_ids:named-entity-recognition annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:id license:other region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["id"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "nergrit-corpus", "pretty_name": "Nergrit Corpus", "dataset_info": [{"config_name": "ner", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "B-CRD", "1": "B-DAT", "2": "B-EVT", "3": "B-FAC", "4": "B-GPE", "5": "B-LAN", "6": "B-LAW", "7": "B-LOC", "8": "B-MON", "9": "B-NOR", "10": "B-ORD", "11": "B-ORG", "12": "B-PER", "13": "B-PRC", "14": "B-PRD", "15": "B-QTY", "16": "B-REG", "17": "B-TIM", "18": "B-WOA", "19": "I-CRD", "20": "I-DAT", "21": "I-EVT", "22": "I-FAC", "23": "I-GPE", "24": "I-LAN", "25": "I-LAW", "26": "I-LOC", "27": "I-MON", "28": "I-NOR", "29": "I-ORD", "30": "I-ORG", "31": "I-PER", "32": "I-PRC", "33": "I-PRD", "34": "I-QTY", "35": "I-REG", "36": "I-TIM", "37": "I-WOA", "38": "O"}}}}], "splits": [{"name": "train", "num_bytes": 5428411, "num_examples": 12532}, {"name": "test", "num_bytes": 1135577, "num_examples": 2399}, {"name": "validation", "num_bytes": 1086437, "num_examples": 2521}], "download_size": 14988232, "dataset_size": 7650425}, {"config_name": "sentiment", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "B-NEG", "1": "B-NET", "2": "B-POS", "3": "I-NEG", "4": "I-NET", "5": "I-POS", "6": "O"}}}}], "splits": [{"name": "train", "num_bytes": 3167972, "num_examples": 7485}, {"name": "test", "num_bytes": 1097517, "num_examples": 2317}, {"name": "validation", "num_bytes": 337679, "num_examples": 782}], "download_size": 14988232, "dataset_size": 4603168}, {"config_name": "statement", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "B-BREL", "1": "B-FREL", "2": "B-STAT", "3": "B-WHO", "4": "I-BREL", "5": "I-FREL", "6": "I-STAT", "7": "I-WHO", "8": "O"}}}}], "splits": [{"name": "train", "num_bytes": 1469081, "num_examples": 2405}, {"name": "test", "num_bytes": 182553, "num_examples": 335}, {"name": "validation", "num_bytes": 105119, "num_examples": 176}], "download_size": 14988232, "dataset_size": 1756753}]}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #license-other #region-us 


# Dataset Card for [Dataset Name]

## 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: PT Gria Inovasi Teknologi
- Repository: Nergrit Corpus
- Paper:
- Leaderboard:
- Point of Contact: Taufiqur Rohman

### Dataset Summary

Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, 
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT). 

### Supported Tasks and Leaderboards



### Languages

Indonesian

## Dataset Structure

A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 

### Data Instances



### Data Fields
- 'id': id of the sample
- 'tokens': the tokens of the example text
- 'ner_tags': the NER tags of each token

#### Named Entity Recognition
The ner_tags correspond to this list:

The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word. The dataset contains 19 following entities

#### Sentiment Analysis
The ner_tags correspond to this list:


#### Statement Extraction
The ner_tags correspond to this list:

The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word.

### Data Splits

The dataset is splitted in to train, validation and test sets.

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?
The annotators are listed in the
Nergrit Corpus repository

### 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 @cahya-wirawan for adding this dataset.
# Dataset Card for [Dataset Name] ## 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: PT Gria Inovasi Teknologi
- Repository: Nergrit Corpus
- Paper:
- Leaderboard:
- Point of Contact: Taufiqur Rohman ### Dataset Summary

Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, 
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT). ### Supported Tasks and Leaderboards ### Languages

Indonesian ## Dataset Structure

A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. ### Data Instances ### Data Fields
- 'id': id of the sample
- 'tokens': the tokens of the example text
- 'ner_tags': the NER tags of each token #### Named Entity Recognition
The ner_tags correspond to this list:

The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word. The dataset contains 19 following entities #### Sentiment Analysis
The ner_tags correspond to this list: #### Statement Extraction
The ner_tags correspond to this list:

The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word. ### Data Splits

The dataset is splitted in to train, validation and test sets. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators?
The annotators are listed in the
Nergrit Corpus repository ### 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 @cahya-wirawan for adding this dataset.
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #license-other #region-us 
 # Dataset Card for [Dataset Name] ## 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: PT Gria Inovasi Teknologi
- Repository: Nergrit Corpus
- Paper:
- Leaderboard:
- Point of Contact: Taufiqur Rohman ### Dataset Summary

Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, 
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT). ### Supported Tasks and Leaderboards ### Languages

Indonesian ## Dataset Structure

A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. ### Data Instances ### Data Fields
- 'id': id of the sample
- 'tokens': the tokens of the example text
- 'ner_tags': the NER tags of each token #### Named Entity Recognition
The ner_tags correspond to this list:

The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word. The dataset contains 19 following entities #### Sentiment Analysis
The ner_tags correspond to this list: #### Statement Extraction
The ner_tags correspond to this list:

The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word. ### Data Splits

The dataset is splitted in to train, validation and test sets. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators?
The annotators are listed in the
Nergrit Corpus repository ### 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 @cahya-wirawan for adding this dataset.
passage: TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #license-other #region-us 
# Dataset Card for [Dataset Name]## 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: PT Gria Inovasi Teknologi
- Repository: Nergrit Corpus
- Paper:
- Leaderboard:
- Point of Contact: Taufiqur Rohman### Dataset Summary

Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, 
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).### Supported Tasks and Leaderboards### Languages

Indonesian## Dataset Structure

A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.### Data Instances### Data Fields
- 'id': id of the sample
- 'tokens': the tokens of the example text
- 'ner_tags': the NER tags of each token#### Named Entity Recognition
The ner_tags correspond to this list:

The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any 
non-initial word. The dataset contains 19 following entities#### Sentiment Analysis
The ner_tags correspond to this list: