---
language: en
license: cc-by-nc-4.0
tags:
- token-classification
- ner
- northeast-india
- low-resource
- xlm-roberta
metrics:
- f1
- precision
- recall
model-index:
- name: MWirelabs/NortheastNER
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Custom Northeast India Gazetteers + News Corpus
      type: custom
      split: dev
    metrics:
    - name: Overall F1
      type: f1
      value: 0.964
    - name: Precision
      type: precision
      value: 0.962
    - name: Recall
      type: recall
      value: 0.967
---

# MWirelabs/NortheastNER

**NortheastNER** is a Named Entity Recognition (NER) model fine-tuned by [MWirelabs](https://huggingface.co/MWirelabs) to recognize entities specific to **Northeast India**.
It is based on [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) and trained on a mix of gazetteers, curated news, and domain-specific data (tribes, villages, flora, fauna, festivals, tourist places).

---

## What it can recognize

* **PLACES** → States, districts, villages, regions (e.g., *Shillong*, *Tura*, *Ri-Bhoi*)
* **TRIBES** → Indigenous tribes & sub-tribes (e.g., *Khasi*, *Nyishi*, *Wancho*)
* **FESTIVALS** → Local festivals (e.g., *Wangala*, *Losar*, *Nyokum Yullo*)
* **TOURIST** → Landmarks & tourist spots (e.g., *Tawang Monastery*, *Umiam Lake*)
* **FLORA** → Plants & crops of the Himalayan / NE region
* **FAUNA** → Animals, birds, wildlife from NE region

---

## Evaluation

Evaluated on a 5k-sentence dev set:

| Entity      | Precision                     | Recall    | F1        |
| ----------- | ----------------------------- | --------- | --------- |
| PLACES      | 0.963                         | 0.969     | 0.966     |
| TRIBES      | 0.927                         | 0.927     | 0.927     |
| FESTIVALS   | (coming soon, fewer examples) |           |           |
| TOURIST     | 0.167                         | 0.125     | 0.143     |
| FLORA       | 1.000                         | 0.800     | 0.889     |
| FAUNA       | 0.000                         | 0.000     | 0.000     |
| **Overall** | **0.962**                     | **0.967** | **0.964** |

⚠️ Low scores for **TOURIST / FAUNA** due to very few training examples — performance will improve with more labeled data.
Note: The current evaluation set does not include enough examples of **NAMES**, so that category is not reported in the table. Training data did include a small gazetteer of Khasi and regional names (~81 entries), but more labeled examples are needed for meaningful evaluation.

---

## Training Setup

* **Base model**: `xlm-roberta-base`
* **Max sequence length**: 256
* **Batch size**: 16
* **Learning rate**: 3e-5
* **Epochs**: 3
* **Weight decay**: 0.01
* **Optimizer**: AdamW
* **Framework**: HuggingFace Transformers Trainer API

### Dataset Size
- Train set: ~20,000 sentences  
- Dev set: ~5,000 sentences  
- Sources: Gazetteers (districts, tribes, flora/fauna, festivals, tourist sites, names), news articles, tourism/cultural descriptions  


### Environment

* **Transformers**: 4.44.2
* **Datasets**: 2.20.0
* **Evaluate**: 0.4.2
* **PyTorch**: 2.3.0+cu121
* **Python**: 3.11
* **Hardware**: Single NVIDIA A4500 GPU (20 GB VRAM), 62 GB RAM, 12 vCPU

---

## Usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "MWirelabs/NortheastNER"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Wangala festival is celebrated in Garo Hills near Tura."
print(ner(text))
```

Output:

```python
[{'entity_group': 'FESTIVALS', 'word': 'Wangala', 'score': 0.99},
 {'entity_group': 'PLACES', 'word': 'Garo Hills', 'score': 0.98},
 {'entity_group': 'PLACES', 'word': 'Tura', 'score': 0.97}]
```

---

## License

This model is licensed under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.

You are free to use, share, and adapt the model for non-commercial purposes with attribution.

---

### Data Licenses
- Gazetteers of villages and tribes: compiled by MWirelabs (open reference use).  
- Festivals, tourist sites, and names: curated by MWirelabs team.  
Please ensure attribution when reusing any derived dataset.

---

## Citation

If you use this model in your research, please cite:

```bibtex
@inproceedings{nyalang-2026-stereotyped,
    title     = {Stereotyped by Silence: How LLMs Erase Northeast Indian Languages Through Omission and Orthographic Corruption},
    author    = {Nyalang, Badal},
    booktitle = {Proceedings of the 1st Workshop on Stereotypes Across Cultures in Language Technologies (StereACuLT 2026)},
    year      = {2026},
    publisher = {Association for Computational Linguistics},
    url       = {https://aclanthology.org/2026.stereacult-1.6/},
    pages     = {62--68}
}
```
---

## Limitations
- Low support for **TOURIST** and **FAUNA** classes (few examples).  
- **NAMES** entity class trained but not evaluated due to lack of dev set coverage.  
- Possible confusion between **TRIBES** and **PLACES** where names overlap (e.g., Garo).  
- Model optimized for Northeast India texts; performance outside this domain may degrade.

## Future Work
- Add more gold-labeled examples for underrepresented classes (Names, Fauna, Tourist).  
- Explore active learning to identify low-confidence predictions for manual annotation.  
- Expand coverage of festivals and indigenous knowledge domains.  

---

## About

This model is developed by **MWirelabs**, pioneering AI solutions for the rich cultural and linguistic diversity of **Northeast India**.
Contact: [MWirelabs](https://huggingface.co/MWirelabs)