---
dataset_info:
  features:
  - name: tokens
    list: string
  - name: ner_tags
    list:
      class_label:
        names:
          '0': B-DATE
          '1': I-DATE
          '2': B-LOC
          '3': I-LOC
          '4': B-NUM
          '5': I-NUM
          '6': B-ORG
          '7': I-ORG
          '8': B-PER
          '9': I-PER
          '10': B-TIME
          '11': I-TIME
          '12': O
  splits:
  - name: train
    num_bytes: 6097148
    num_examples: 12825
  - name: test
    num_bytes: 1498925
    num_examples: 3207
  download_size: 627526
  dataset_size: 7596073
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
task_categories:
- token-classification
language:
- my
pretty_name: Myanmar Name Entity Recognition(NER) Dataset
size_categories:
- 10K<n<100K
---

*Please visit the [GitHub repository](https://github.com/chuuhtetnaing/myanmar-language-dataset-collection) for other Myanmar Language datasets.*

# Myanmar NER Dataset

A token classification dataset for Myanmar (Burmese) Named Entity Recognition (NER), formatted for sequence labeling tasks using BIO tagging scheme.

📓 **Data Preparation Notebook**: [dataset-preparation.ipynb](https://github.com/chuuhtetnaing/myanmar-language-dataset-collection/blob/main/myNER/dataset-preparation.ipynb)

📓 **Fine-Tuning Notebook**: [myanmar-ner-fine-tuning.ipynb](https://github.com/chuuhtetnaing/myanmar-language-dataset-collection/blob/main/myNER/myanmar-ner-fine-tuning.ipynb) (based on the [HuggingFace Token Classification Guide](https://huggingface.co/docs/transformers/en/tasks/token_classification))

## Dataset Description

This dataset contains Myanmar language text annotated with Named Entity Recognition (NER) tags. The dataset has been **cleaned, preprocessed, and formatted** to be ready for fine-tuning HuggingFace token classification models.

The dataset uses a **7-tag annotation scheme** (6 entity types + O tag) designed to support various NLP tasks including information extraction, question answering, and knowledge graph construction.

> **Note**: The original myNER corpus uses the **BIOES** (Begin, Inside, Outside, End, Single) tagging scheme. This preprocessed version has been converted to the **BIO** (Begin, Inside, Outside) format for easier fine-tuning with HuggingFace Token Classification models.

## Source Data

**Original Dataset**: [ye-kyaw-thu/myNER](https://github.com/ye-kyaw-thu/myNER)

**Original Corpus**: The myNER corpus containing 16,605 sentences was originally sourced from the [myPOS](https://github.com/ye-kyaw-thu/myPOS) version 3 corpus and manually annotated for NER.

This is a preprocessed and cleaned version of the original myNER dataset. Please cite the original dataset and authors if you use this preprocessed version in your research.

### Processing Pipeline

1. **Data Loading**: Loaded CoNLL format data from the original myNER-7tags corpus
2. **Syllable Segmentation**: Applied Myanmar syllable breaking for proper tokenization
3. **Tag Conversion & Expansion**: Converted BIOES to BIO and expanded tags for multi-syllable words (B/S → B+I..., I/E → I..., O → O...)
4. **Token-Label Alignment**: Verified proper alignment between syllables and their NER tags
5. **Deduplication**: Removal of duplicate entries
6. **Train-Test Split**: 80% training (12,825 examples) and 20% test (3,207 examples)
7. **Format Conversion**: Conversion to HuggingFace token classification format

### Tagging Scheme Conversion

The original corpus uses word-level BIOES tagging. After syllable segmentation, each word may become multiple syllables, so tags are expanded accordingly:

| BIOES (Original) | BIO (After Syllable Segmentation) | Description |
|------------------|-----------------------------------|-------------|
| B- | B-, I-, I-, ... | Begin → B for first syllable, I for remaining syllables |
| I- | I-, I-, I-, ... | Inside → all syllables remain I |
| O | O, O, O, ... | Outside → all syllables remain O |
| E- | I-, I-, I-, ... | End → all syllables become I |
| S- | B-, I-, I-, ... | Single → B for first syllable, I for remaining syllables |

## Dataset Statistics

| Split | Examples |
|-------|----------|
| Train | 12,825   |
| Test  | 3,207    |

## Data Format

```python
{
    "tokens": [ "ရွာ", "နေ", "ရာ", "ကုတ်", "မှာ", "၁", "၇", "၆", "၉", "၂", "၄", "ဖြစ်", "သည်", "။" ],
    "ner_tags": [ 12, 12, 12, 12, 12, 4, 5, 5, 5, 5, 5, 12, 12, 12 ]
}
```

## Features

- `tokens`: `Sequence[string]` - Input tokens (Myanmar syllables)
- `ner_tags`: `Sequence[ClassLabel]` - Named Entity Recognition labels

## NER Tags

The dataset includes 6 entity types using BIO tagging scheme:

| Entity Type | Description |
|-------------|-------------|
| DATE | Date expressions |
| LOC | Location |
| NUM | Numerical expressions |
| ORG | Organization |
| PER | Person |
| TIME | Time expressions |

### Tag Labels

The full tag list in BIO format:

| Tag | Index |
|-----|-------|
| B-DATE | 0 |
| I-DATE | 1 |
| B-LOC | 2 |
| I-LOC | 3 |
| B-NUM | 4 |
| I-NUM | 5 |
| B-ORG | 6 |
| I-ORG | 7 |
| B-PER | 8 |
| I-PER | 9 |
| B-TIME | 10 |
| I-TIME | 11 |
| O | 12 |

## Usage

```python
from datasets import load_dataset
ds = load_dataset("chuuhtetnaing/myanmar-ner-dataset")

ds["train"].features["ner_tags"].feature.names
# ['B-DATE', 'I-DATE', 'B-LOC', 'I-LOC', 'B-NUM', 'I-NUM', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER', 'B-TIME', 'I-TIME', 'O']
```

Example from the dataset:

```python
ds["train"][0]
# {
#     'tokens': [ "1", "bit", "ပုံ", "ရိပ်", "တစ်", "ခု", "သည်", "monochrome", "ဖြစ်", "သည်", "။" ],
#     'ner_tags': [ 4, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12 ]
# }

id2label = {i: label for i, label in enumerate(ds["train"].features["ner_tags"].feature.names)}
label_ner_tags = [id2label[tag_id] for tag_id in ds["train"][0]["ner_tags"]]
```

## Intended Use

- Training NER models for Myanmar NLP
- Token classification / sequence labeling experiments ([HuggingFace Token Classification Training Example](https://huggingface.co/docs/transformers/en/tasks/token_classification))
- Myanmar language processing research
- Information extraction and knowledge graph construction
- Question answering systems
- Machine translation preprocessing

## Citation

**arXiv Preprint**: [arXiv:2504.04038](https://arxiv.org/abs/2504.04038)

For more information about the original dataset, please visit:

- [GitHub Repository](https://github.com/ye-kyaw-thu/myNER)
- [arXiv Paper](https://arxiv.org/abs/2504.04038)

## Acknowledgments

Special thanks to:

- **Professor Ye Kyaw Thu** and the Language Understanding Lab (LU Lab) team for creating the original myNER dataset
- The Myanmar NLP community for supporting language technology development