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 to recognize entities specific to Northeast India. It is based on 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
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:
[{'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:
@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