MWirelabs
/

NortheastNER

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 library_name: transformers
 ---
+# 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
+The model is trained to extract the following entity types:
+* **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.
+---
+## 🚀 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}]
+```
+---
+## 📌 Notes
+* Optimized for **Northeast India domain texts**: news, culture, tourism, indigenous knowledge.
+* Best performance on **PLACES** and **TRIBES**.
+* Model will be continuously improved with more curated datasets (flora, fauna, festivals).
+---
+## 🏢 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)