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metadata
language:
  - si
license: cc-by-4.0
task_categories:
  - token-classification
tags:
  - named-entity-recognition
  - sinhala
  - ner
  - natural-language-processing
  - bio-format
pretty_name: Sinhala NER 85k Dataset
dataset_info:
  features:
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-PER
            '2': I-PER
            '3': B-LOC
            '4': I-LOC
            '5': B-ORG
            '6': I-ORG
            '7': B-Other
            '8': I-Other
size_categories:
  - 10K<n<100K

Sinhala Named Entity Recognition (NER) Dataset - 85,000 Annotations

Dataset Description

This is a high-quality Named Entity Recognition (NER) dataset for the Sinhala language, consisting of approximately 85,000 annotations. The dataset was manually curated and annotated by a team of three students to support NLP research for low-resource languages.

The data is sourced from diverse domains, including social media comments, news articles, and public domain texts, capturing the nuances of both formal and informal Sinhala usage.

Annotators & Contact

This dataset was manually annotated and curated by the following students. For inquiries regarding the dataset, please contact:

Dataset Structure

Format

The dataset follows the BIO (Beginning, Inside, Outside) format, commonly used for sequence labeling tasks. Each token is paired with a corresponding tag.

Label Statistics

The distribution of the entities in this dataset is as follows:

Label Count Description
B-PER 6,386 Beginning of a Person's name
I-PER 1,744 Inside of a Person's name
B-LOC 4,074 Beginning of a Location
I-LOC 253 Inside of a Location
B-ORG 3,762 Beginning of an Organization
I-ORG 707 Inside of an Organization
B-Other 51,048 Beginning of other entities
I-Other 190 Inside of other entities
O 18,489 Outside (Non-entity words)

Data Example

Each entry consists of a sentence split into tokens with their respective NER tags:

Tag Number (ID) Tag Label
0 O
1 B-PER
2 I-PER
3 B-LOC
4 I-LOC
5 B-ORG
6 I-ORG
7 B-Other
8 I-Other

Intended Use

  • Named Entity Recognition: Training models like BERT, XLM-R, or LSTM-CRF for Sinhala NER.
  • Language Modeling: Fine-tuning language models for Sinhala.
  • Academic Research: Studying the linguistic patterns of entities in South Asian languages.

Licensing Information

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Users are free to share and adapt the material as long as appropriate credit is given to the original creators.