grit-id/id_nergrit_corpus

---
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
---

# 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:[e-mail])

### 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.

原始信息汇总

数据集概述

数据集描述

• 数据集名称: Nergrit Corpus
• 语言: 印度尼西亚语
• 许可证: 其他
• 多语言性: 单语种
• 数据集大小: 10K<n<100K
• 源数据集: 原始数据
• 任务类别:
  • 命名实体识别 (Named Entity Recognition)

数据集结构

配置信息

配置名称: ner

• 特征:

  • id: 字符串类型
  • tokens: 字符串序列
  • ner_tags: 标签序列，包含以下类别:
    • 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

• 数据分割:

  • train: 12532个样本，5428411字节
  • test: 2399个样本，1135577字节
  • validation: 2521个样本，1086437字节

配置名称: sentiment

• 特征:

  • id: 字符串类型
  • tokens: 字符串序列
  • ner_tags: 标签序列，包含以下类别:
    • 0: B-NEG
    • 1: B-NET
    • 2: B-POS
    • 3: I-NEG
    • 4: I-NET
    • 5: I-POS
    • 6: O

• 数据分割:

  • train: 7485个样本，3167972字节
  • test: 2317个样本，1097517字节
  • validation: 782个样本，337679字节

配置名称: statement

• 特征:

  • id: 字符串类型
  • tokens: 字符串序列
  • ner_tags: 标签序列，包含以下类别:
    • 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

• 数据分割:

  • train: 2405个样本，1469081字节
  • test: 335个样本，182553字节
  • validation: 176个样本，105119字节

数据集创建

注释

• 注释创建者: 专家生成
• 注释者: 注释者列表可在Nergrit Corpus仓库中找到