MWirelabs
/

nagamesebert

@@ -1,199 +1,240 @@
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 ---
+language:
+- nag
+license: cc-by-4.0
+tags:
+- bert
+- roberta
+- nagamese
+- low-resource
+- creole
+- northeast-india
+- token-classification
+- fill-mask
+datasets:
+- agnivamaiti/naganlp-ner-annotated-corpus
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: NagameseBERT
+  results:
+  - task:
+      type: token-classification
+      name: Part-of-Speech Tagging
+    dataset:
+      name: NagaNLP Annotated Corpus
+      type: agnivamaiti/naganlp-ner-annotated-corpus
+    metrics:
+    - type: accuracy
+      value: 88.35
+      name: Accuracy
+    - type: f1
+      value: 80.72
+      name: F1 (macro)
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: NagaNLP Annotated Corpus
+      type: agnivamaiti/naganlp-ner-annotated-corpus
+    metrics:
+    - type: accuracy
+      value: 91.74
+      name: Accuracy
+    - type: f1
+      value: 56.51
+      name: F1 (macro)
 ---
+# NagameseBERT
+[![HuggingFace Model](https://img.shields.io/badge/🤗%20HuggingFace-Model-yellow)](https://huggingface.co/MWirelabs/nagamesebert)
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
+[![Language](https://img.shields.io/badge/Language-Nagamese-green)](https://en.wikipedia.org/wiki/Nagamese_Creole)
+**A Foundational BERT model for Nagamese Creole** - A compact, efficient language model for a low resource Northeast Indian language.
+---
+## Overview
+NagameseBERT is a 7M parameter RoBERTa-style BERT model pre-trained on 42,552 Nagamese sentences. Despite being 15× smaller than multilingual models like mBERT (110M) and XLM-RoBERTa (125M), it achieves competitive performance on downstream NLP tasks while offering significant efficiency advantages.
+**Key Features:**
+- **Compact**: 6.9M parameters (15× smaller than mBERT)
+- **Efficient**: Pre-trained in 35 minutes on single A40 GPU
+- **Custom tokenizer**: 8K BPE vocabulary optimized for Nagamese
+- **Rigorous evaluation**: Multi-seed testing (n=3) with reproducible results
+- **Open**: Model, code, and data splits publicly available
+---
+## Performance
+Multi-seed evaluation results (mean ± std, n=3):
+| Model | Parameters | POS Accuracy | POS F1 | NER Accuracy | NER F1 |
+|-------|-----------|--------------|--------|--------------|--------|
+| **NagameseBERT** | **7M** | **88.35 ± 0.71%** | **0.807 ± 0.013** | **91.74 ± 0.68%** | **0.565 ± 0.054** |
+| mBERT | 110M | 95.14 ± 0.47% | 0.916 ± 0.008 | 96.11 ± 0.72% | 0.750 ± 0.064 |
+| XLM-RoBERTa | 125M | 95.64 ± 0.56% | 0.919 ± 0.008 | 96.38 ± 0.26% | 0.819 ± 0.066 |
+**Trade-off**: 6-7 percentage points lower accuracy with 15× parameter reduction, enabling resource-constrained deployment.
+---
+## Model Details
+### Architecture
+- **Type**: RoBERTa-style BERT (no token type embeddings)
+- **Hidden size**: 256
+- **Layers**: 6 transformer blocks
+- **Attention heads**: 4 per layer
+- **Intermediate size**: 1,024
+- **Max sequence length**: 64 tokens
+- **Total parameters**: 6,878,528
+### Tokenizer
+- **Type**: Byte-Pair Encoding (BPE)
+- **Vocabulary size**: 8,000 tokens
+- **Special tokens**: `[PAD]`, `[UNK]`, `[CLS]`, `[SEP]`, `[MASK]`
+- **Normalization**: NFD Unicode + accent stripping
+- **Case**: Preserved (for proper nouns and code-switched English)
 ### Training Data
+- **Corpus size**: 42,552 Nagamese sentences
+- **Average length**: 11.82 tokens/sentence
+- **Split**: 90% train (38,296) / 10% validation (4,256)
+- **Sources**: Web, social media, community contributions (deduplicated)
+### Pre-training
+- **Objective**: Masked Language Modeling (15% masking)
+- **Optimizer**: AdamW (lr=5e-4, weight_decay=0.01)
+- **Batch size**: 64
+- **Epochs**: 50
+- **Training time**: ~35 minutes
+- **Hardware**: NVIDIA A40 (48GB)
+- **Final validation loss**: 2.79
+---
+## Usage
+### Load Model and Tokenizer
+```python
+from transformers import AutoTokenizer, AutoModel
+model_name = "MWirelabs/nagamesebert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+# Example usage
+text = "Toi moi laga sathi hobo pare?"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+```
+### Fine-tuning for Token Classification
+```python
+from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
+# Load model with classification head
+model = AutoModelForTokenClassification.from_pretrained(
+    "MWirelabs/nagamesebert",
+    num_labels=num_labels
+)
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=100,
+    per_device_train_batch_size=8,
+    learning_rate=3e-5,
+    weight_decay=0.01
+)
+# Train
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset
+)
+trainer.train()
+```
+---
 ## Evaluation
+### Dataset
+- **Source**: [NagaNLP Annotated Corpus](https://huggingface.co/datasets/agnivamaiti/naganlp-ner-annotated-corpus)
+- **Total**: 214 sentences
+- **Split** (seed=42): 171 train / 21 dev / 22 test (80/10/10)
+- **POS tags**: 13 Universal Dependencies tags
+- **NER tags**: 4 entity types (PER, LOC, ORG, MISC) in IOB2 format
+### Experimental Setup
+- **Seeds**: 42, 123, 456 (n=3 for variance estimation)
+- **Batch size**: 32
+- **Learning rate**: 3e-5
+- **Epochs**: 100
+- **Optimization**: AdamW with 100 warmup steps
+- **Hardware**: NVIDIA A40
+- **Metrics**: Token-level accuracy and macro-averaged F1
+**Data Leakage Statement**: All splits created with fixed seed (42) with no sentence overlap between train/dev/test sets.
+---
+## Limitations
+- **Corpus size**: 42K sentences is modest; expansion to 100K+ could improve performance
+- **Evaluation scale**: Small test set (22 sentences) limits statistical power
+- **Task scope**: Only evaluated on token classification; needs broader task assessment
+- **Efficiency metrics**: No quantitative inference benchmarks (latency, memory) yet provided
+- **Data documentation**: Complete data provenance and licenses to be formalized
+---
+## Citation
+If you use NagameseBERT in your research, please cite:
+```bibtex
+@misc{nagamesebert2025,
+  title={Bootstrapping BERT for Nagamese: A Low-Resource Creole Language},
+  author={MWire Labs},
+  year={2025},
+  url={https://huggingface.co/MWirelabs/nagamesebert}
+}
+```
+---
+## Contact
+**MWire Labs**
+Shillong, Meghalaya, India
+Website: [MWire Labs](https://mwirelabs.com)
+---
+## License
+This model is released under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
+You are free to:
+- **Share** — copy and redistribute the material
+- **Adapt** — remix, transform, and build upon the material
+Under the following terms:
+- **Attribution** — You must give appropriate credit to MWire Labs
+---
+## Acknowledgments
+We thank the Nagamese-speaking community for their contributions to corpus development and validation.