---
title: 'NagaNLP: Toolkit for Nagamese NLP'
url: https://www.emergentmind.com/topics/naganlp-toolkit
type: topic
---

# NagaNLP: Toolkit for Nagamese NLP

NagaNLP is the first comprehensive open-source toolkit designed to advance Natural Language Processing (NLP) for Nagamese, an Assamese-lexified creole with minimal digital representation. Developed to address the acute data scarcity that impedes the creation of NLP models for low-resource languages, NagaNLP employs a synthetic-hybrid corpus generation method that combines large language model (LLM)-driven data synthesis with rigorous human validation. The release includes datasets, fine-tuned discriminative and generative models, and a full suite of code and annotation tools, constituting both a fundamental resource for Nagamese and a transferable paradigm for other severely under-resourced languages [2512.12537].

## 1. Motivation and Scope

Nagamese, a creole spoken throughout Nagaland, exemplifies languages that face the “digital cliff”—an absence of accessible corpora, annotated datasets, or preexisting models, creating a self-reinforcing barrier for technological development. Prior to NagaNLP, digital resources for Nagamese were limited to a single, manually constructed Part-of-Speech (POS) dataset. The NagaNLP toolkit directly targets this “chicken-and-egg” problem by enabling data and model creation via a scalable expert-guided, LLM-to-human synthetic data pipeline. This hybrid approach seeks to bootstrap foundational NLP resources for languages starting from a practical zero-resource baseline. All artifacts are made available under an open-source license, specifically to provide both infrastructure for Nagamese and a generalizable workflow for similar contexts [2512.12537].

## 2. Synthetic-Hybrid Data Generation Pipeline

At the core of NagaNLP lies a structured, multi-stage pipeline for generating and annotating data:

- **Stage 1 (Persona and Task Definition):** Gemini 2.5 Pro is configured as an “AI linguist” with explicit knowledge acquisition objectives, eliciting language structure by interacting with a human expert rather than performing generic text synthesis.
- **Stage 2 (Interactive Grammatical Elicitation):** The expert presents authentic Nagamese texts; Gemini actively queries, posits grammatical generalizations, and is iteratively corrected to enhance output authenticity.
- **Stage 3 (Knowledge Consolidation):** Extracted rules and structures are distilled by Gemini into a formalized grammar representation to guide subsequent synthetic generation.
- **Stage 4 (Scaled Data Generation):** Using few-shot prompting and iterative grammar rule reinforcement, Gemini yields two primary resources: a collection of declarative sentences (for annotation) and 10,018 conversational instruction-response pairs (for generative fine-tuning), each formatted as JSONL.

Human-in-the-loop validation is integral at all stages. Four annotators (three native, one fluent) review generated outputs. Declarative sentences pass through human correction, LLM-assisted preliminary POS and Named Entity Recognition (NER) annotation, then final human review, yielding sequences labeled with Universal Dependencies v2 POS tags (17 classes) and IOB2-style NER tags (PER, LOC, ORG, MISC). Inter-annotator agreement is high: Cohen’s κ = 0.92 (POS) and κ = 0.88 (NER), indicating near-perfect and substantial agreement, respectively [2512.12537].

## 3. Released Datasets

NagaNLP introduces two principal corpora:

| Dataset                       | Size           | Format          | Primary Use                 |
|-------------------------------|----------------|-----------------|-----------------------------|
| Conversational Corpus         | 10,018 pairs   | JSONL (inst-resp) | Generative modeling         |
| Annotated POS/NER Corpus      | 214 sentences (4,839 tokens) | CONLL-style   | Discriminative tasks (POS, NER) |

The conversational dataset encompasses 311,684 tokens and a unique vocabulary of 22,998, split 80/10/10 for train/dev/test. Each entry provides a Nagamese “instruction” and its corresponding “response,” post-edited for grammaticality and idiomaticity. The annotated corpus, split 171/21/22 for train/dev/test, provides gold-standard labels for POS and NER tasks, employing universal tag sets and guidelines for code-switching phenomena. Tag/entity distributions demonstrate linguistic coverage, e.g., 19% nouns, 16% verbs; for NER, 36% MISC, 32% LOC [2512.12537].

## 4. Model Architectures, Training, and Benchmarks

### Discriminative Models

NagaNLP leverages both classical and transformer-based models for its discriminative tasks:

- **Architectures:** bert-base-multilingual-cased and xlm-roberta-base are fine-tuned with AdamW (LR=2e-5, weight decay=0.01, batch size 16) for up to 20 epochs with macro-F1-based checkpointing.
- **Baselines:** Performance is contextualized against zero-shot xlm-roberta-large and CRF-based POS/NER systems, including prior work and replication on new data.
- **Results:**
  - **POS Tagging:** Fine-tuned xlm-roberta-base achieves 93.81% accuracy and 0.90 macro F1—establishing a new state-of-the-art for Nagamese POS tagging. The previous CRF system yielded 85.70%/0.86; retrained on the new data, CRF attains 93.84%/0.91.
  - **NER:** xlm-roberta-base reaches 95.13% strict accuracy and 0.75 macro F1 (first such benchmark for Nagamese), outperforming both multilingual transformer and CRF baselines.
  - Zero-shot transformer performance remains minimal (≈0% for NER), demonstrating the necessity of in-language training data.

### Generative Model: NagaLLaMA

NagaLLaMA, a Llama-3.2-3B-Instruct derivative, is adapted via LoRA (r=16, α=32, dropout=0.05) applied to key projection layers, trained for 3 epochs (LR=2e-4, effective batch size 16). Notable outcomes:

- **Perplexity:** NagaLLaMA attains a perplexity of 3.85, compared to 96.76 for the few-shot Llama baseline, signifying dramatically improved fluency.
- **ROUGE-L:** Increases from 11.28 (few-shot) to 20.77 (NagaLLaMA).
- **Machine Translation Benchmarks:** For Eng→Nag, BLEU = 14.25 (vs. 1.64 for NLLB-200), chrF++=41.83, COMET = 0.6668; for Nag→Eng, BLEU = 34.97, chrF++ = 53.17, COMET = 0.7338.
- **Data Scaling Effects:** Perplexity decreases monotonically as more synthetic-hybrid data is added (5.33 at 25% data, 3.85 at full size), indicating continual benefit from increased data volume.

Qualitative assessment confirms that NagaLLaMA is capable of context-aware generation in Nagamese, including code-switching and idiomatic usage, surpassing few-shot LLM outputs [2512.12537].

## 5. Toolkit Components and Usage

The public release of NagaNLP comprises:

- **Data:**
  - NagaNLP Annotated Corpus (POS + NER, 214 sentences, CONLL format)
  - Conversational Corpus (10,018 JSONL pairs)
- **Models:**
  - Fine-tuned checkpoints for bert-base-multilingual-cased, xlm-roberta-base, and CRF POS/NER models
  - NagaLLaMA (Llama-3.2-3B with LoRA adapters)
- **Code and Scripts:**
  - Complete synthetic data generation workflow (Stages 1–4)
  - Annotation tools/guidelines for POS and NER
  - Training scripts for discriminative and generative models (HuggingFace Transformers, LoRA fine-tuning)
  - Standardized evaluation scripts (seqeval for sequence labeling, ROUGE, BLEU, COMET)
- **Installation and Replication:**
  - Environment files (Conda/YAML, Python 3.10)
  - Standard install: `pip install naganlp` or git-based deployment
  - Configuration templates support rapid adaptation of the full pipeline to any zero-resource language

All assets are designed to be modular, facilitating extensibility and reproducibility for both Nagamese and other extremely low-resource linguistic contexts [2512.12537].

## 6. Implications and Generalizability

NagaNLP demonstrates that rapid NLP bootstrapping for creole and other typologically complex, digitally marginalized languages is viable through expert-guided, LLM-augmented synthetic data pipelines coupled with rigorous human validation. By establishing robust new benchmarks—POS accuracy (93.81%), NER macro F1 (0.75), NagaLLaMA perplexity (3.85)—it provides both empirical evidence and infrastructural foundation for further research in underrepresented language technologies. The systematic, adaptable workflow, supported by open-source release, constitutes a transferable blueprint for enabling NLP resources in languages with near-zero pre-existing digital presence [2512.12537].

## 7. Significance for the NLP Community

By bridging methodological advances in LLM prompting, synthetic data curation, and annotation best practices, NagaNLP directly addresses a major bottleneck in NLP: data scarcity in the global majority of languages. Its empirical achievements in Nagamese establish new state-of-the-art baselines and showcase the effectiveness of synthetic-hybrid strategies where conventional manual annotation is infeasible. The toolkit and its documentation enable both domain experts and applied practitioners to replicate and adapt the approach, setting a precedent for sustainable, community-informed NLP resource development for under-resourced and endangered languages [2512.12537].

Source: https://www.emergentmind.com/topics/naganlp-toolkit