ConflLlama-NER-TOI: Large-Scale Named Entity Recognition for Indian News

---

⚠️ Important: Read Before Using

This model requires exact prompt formatting to work correctly. Please read the Critical: Inference & Prompt Formatting section below before attempting to use this model. Using incorrect prompts will result in poor performance or hallucinations.

---

ConflLlama-NER-TOI is a large-scale named entity recognition model fine-tuned on 100,000 Indian news articles from the Times of India. Built upon Llama-3.1 8B Instruct, this model identifies and classifies four entity types across diverse news domains:

• Location: Geographic entities (cities, regions, countries)
• Organisation: Companies, government bodies, institutions, political parties
• Person: Named individuals, public figures, officials
• Temporal: Time expressions, dates, periods

This model represents a significant scale-up from the original ConflLlama-NER, trained on 1,300x more data for broader domain coverage and improved generalization.

---

Key Features

• Large-Scale Training: Fine-tuned on 100,000 news articles with high-confidence entity annotations
• Multi-Domain Coverage: Trained on diverse news topics including politics, sports, crime, economy, and entertainment
• ConfliBERT Annotations: Uses state-of-the-art entity recognition model for training data generation
• High-Quality Filtering: Only entities with ≥0.9 confidence score included in training
• JSON Output Format: Returns structured entity lists with text, type, position, and confidence scores
• Efficient Deployment: Available in multiple quantization formats (Q4_K_M, Q8_0, BF16)
• Instruction-Tuned: Built on Llama-3.1 Instruct for robust prompt following

---

Training Data

Dataset: Times of India Corpus with ConfliBERT Annotations

The model was trained on a large-scale dataset derived from Indian news articles:

• Total Articles: ~1.5 million Times of India articles
• Training Samples: 100,000 (randomly sampled)
• Training/Test Split: 95,000 train / 5,000 test (95/5 split)
• Text Processing: Articles truncated to 510 tokens (~400-500 words) to fit context limits
• Annotation Method: Automated using eventdata-utd/conflibert-named-entity-recognition
• Quality Filter: Only entities with confidence score ≥0.9 included
• Total Entities: Hundreds of thousands of high-confidence annotations

Entity Type Definitions

Data Coverage

The training data spans multiple news domains:

• Politics: Government actions, elections, policy announcements
• Crime: Police reports, legal proceedings, incidents
• Sports: Matches, players, tournaments
• Economy: Business news, market reports, financial decisions
• Entertainment: Celebrity news, film industry, cultural events
• Local News: City-level events, regional developments

---

Model Architecture

• Base Model: unsloth/llama-3-8b-Instruct-bnb-4bit
• Fine-tuning Method: QLoRA (Quantized Low-Rank Adaptation)
• Quantization: 4-bit with bitsandbytes
• Maximum Sequence Length: 2048 tokens
• LoRA Configuration:
  • Rank (r): 16
  • Alpha (lora_alpha): 16
  • Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
  • Dropout: 0
  • Gradient Checkpointing: Enabled (Unsloth optimization)

Training Configuration

• Optimizer: AdamW 8-bit (memory efficient)
• Learning Rate: 2e-4 with 3% warmup (150 steps)
• Batch Size: 2 per device with 4 gradient accumulation steps (effective batch size: 8)
• Training Steps: 5,000 steps (~42% of one epoch)
• Precision: BFloat16 (when supported)
• Hardware: NVIDIA A100-SXM4-40GB GPU on NCSA Delta
• Training Time: ~3-4 hours
• Memory Footprint: ~8 GB VRAM

Training Dynamics

• Initial Loss: ~2.5
• Final Loss: ~0.90 (plateau observed)
• Gradient Norm: Stable at 0.25-0.35 throughout training
• Learning Rate Schedule: Linear decay from 2e-4 to 0

---

Critical: Inference & Prompt Formatting

IMPORTANT: This model requires exact prompt formatting to function correctly. The model was instruction-tuned with a specific template. Deviating from this format will result in poor performance or hallucinations.

Option 1: Using LM Studio / Llama.cpp (Recommended)

System Prompt:

Extract all named entities from the following text. Return them as a JSON list with 'text', 'type', 'start', 'end', and 'score' fields.

User Message:

Text: [YOUR ARTICLE TEXT HERE]
Entities: 

Option 2: Direct API/Python Integration

If your platform requires a single concatenated prompt:

prompt = """<|start_header_id|>system<|end_header_id|>

Extract all named entities from the following text. Return them as a JSON list with 'text', 'type', 'start', 'end', and 'score' fields.<|eot_id|><|start_header_id|>user<|end_header_id|>

Text: {your_text_here}
Entities: <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""

Example Usage

Input:

Text: The Punjab government has placed under suspension two officials following the Bathinda incident. Chief Minister Amarinder Singh announced the decision in Chandigarh on Monday.
Entities:

Expected Output:

[
  {"text": "Punjab government", "type": "Organisation", "start": 4, "end": 21, "score": 0.995},
  {"text": "Bathinda", "type": "Location", "start": 72, "end": 80, "score": 0.980},
  {"text": "Amarinder Singh", "type": "Person", "start": 101, "end": 116, "score": 0.998},
  {"text": "Chandigarh", "type": "Location", "start": 145, "end": 155, "score": 0.992},
  {"text": "Monday", "type": "Temporal", "start": 159, "end": 165, "score": 0.975}
]

---

Installation & Usage

Using Llama.cpp (Recommended for Local Deployment)

# Download the Q4_K_M GGUF model
wget https://huggingface.co/shreyasmeher/ConflLlama-NER-TOI-GGUF/resolve/main/model-unsloth-Q4_K_M.gguf

# Run with llama.cpp
./llama-cli -m model-unsloth-Q4_K_M.gguf \
  --system "Extract all named entities from the following text. Return them as a JSON list with 'text', 'type', 'start', 'end', and 'score' fields." \
  --prompt "Text: [YOUR TEXT]\nEntities: " \
  --temp 0.3 \
  --n-predict 512

Using Transformers (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import json

# Load model and tokenizer
model_name = "shreyasmeher/ConflLlama-NER-TOI"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Prepare input with correct formatting
text = "The Reserve Bank of India announced new policies in Mumbai on Tuesday."
prompt = f"""<|start_header_id|>system<|end_header_id|>

Extract all named entities from the following text. Return them as a JSON list with 'text', 'type', 'start', 'end', and 'score' fields.<|eot_id|><|start_header_id|>user<|end_header_id|>

Text: {text}
Entities: <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.3,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id
)

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Parse JSON output
entities = json.loads(result.split("Entities: ")[1])
print(entities)

Using Unsloth (For Further Fine-tuning)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="shreyasmeher/ConflLlama-NER-TOI",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Generate
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3)
result = tokenizer.batch_decode(outputs)

---

Intended Use

This model is designed for research and analysis of news and information extraction:

1. News Analytics: Extract structured information from news articles at scale
2. Information Retrieval: Build searchable databases of entities from news corpora
3. Knowledge Graph Construction: Create entity relationship networks from news
4. Content Analysis: Identify key actors, locations, and organizations in news coverage
5. Media Monitoring: Track entity mentions across large news datasets
6. Social Science Research: Analyze patterns in news coverage and entity representation

Example Applications

• Building entity databases from news archives
• Tracking political figures and organizations in media
• Analyzing geographic focus of news coverage
• Preprocessing for downstream NLP tasks (summarization, Q&A, classification)
• Creating entity timelines and co-occurrence networks

---

Limitations

1. Training Data Scope:

   • Trained primarily on Indian news from Times of India
   • May show bias toward Indian entities, locations, and naming conventions
   • Performance on news from other countries/regions may vary

2. Entity Type Coverage:

   • Limited to 4 entity types (Location, Organisation, Person, Temporal)
   • Does not recognize: Events, Products, Diseases, Currencies, etc.
   • No nested or overlapping entity support

3. Training Completeness:

   • Model only saw ~42% of training data (5,000 steps vs ~11,875 for full epoch)
   • Loss plateaued at 0.90, suggesting potential for improvement with longer training
   • Early stopping may have left performance gains on the table

4. Annotation Quality:

   • Training labels generated by ConfliBERT model (not human-annotated)
   • Inherits any biases or errors from the annotation model
   • Confidence threshold (≥0.9) may have excluded valid but uncertain entities

5. Context Window:

   • Articles truncated to 510 tokens (~400-500 words)
   • Entities appearing later in long articles were not seen during training

6. Language: English-only, optimized for Indian English conventions

7. Output Parsing: Model outputs JSON, but may occasionally produce malformed JSON. Implement robust parsing with error handling.

8. Prompt Sensitivity: Critical limitation - requires exact prompt formatting. Deviations will significantly degrade performance.

---

Performance Considerations

Expected Behavior

• Strong Performance: Indian locations, political organizations, Hindi-origin names
• Moderate Performance: International entities, technical/specialized terms
• Potential Challenges:
  • Ambiguous entity boundaries in complex phrases
  • Entities with unconventional capitalization
  • Code-mixed text (Hindi-English)
  • Very long or very short articles

Recommended Use Cases

✅ Good Fit:

• Extracting entities from Indian news articles
• Processing Times of India or similar English-language Indian news
• Large-scale batch processing of news corpora
• Research requiring entities with confidence scores

❌ Poor Fit:

• Real-time critical applications (e.g., emergency response)
• Non-news text (scientific papers, social media, legal documents)
• Languages other than English
• Applications requiring 100% precision

---

Ethical Considerations

Responsible Use

1. Training Data Provenance:

   • Model trained on Times of India articles without explicit permission
   • Users should verify compliance with data usage policies for their applications

2. Bias and Representation:

   • Training data reflects editorial decisions and biases of Times of India
   • May overrepresent certain geographic regions, political perspectives, or demographics
   • Entity recognition accuracy may vary across different communities

3. Privacy Concerns:

   • Model extracts names of real individuals from text
   • Users must handle personal information in compliance with privacy laws (GDPR, etc.)
   • Do not use for surveillance or unauthorized profiling

4. Quality and Errors:

   • Automated annotations (ConfliBERT) may contain errors
   • Critical applications should validate entity extractions manually
   • Do not use for legal, medical, or safety-critical decisions

5. Dual-Use Potential: While designed for research, entity extraction could support:

   • ❌ Surveillance or profiling of individuals/groups
   • ❌ Manipulation of public discourse
   • ❌ Unauthorized data harvesting
   • ✅ Academic research and journalistic analysis (intended use)

Transparency Requirements

Users should:

• Disclose when findings are based on automated NER
• Report model limitations in publications
• Validate critical findings with manual review or alternative methods
• Cite both the model and the ConfliBERT annotation source

---

Comparison with Original ConflLlama-NER

When to use which:

• CAMEO version: Conflict analysis, event coding, semantic role extraction
• TOI version (this): General news analysis, entity databases, broad NER tasks

---

Citation

@article{meher2025confllama,
  title={ConflLlama: Domain-specific adaptation of large language models for conflict event classification},
  author={Meher, Shreyas and Brandt, Patrick T.},
  journal={Research \& Politics},
  volume={12},
  number={3},
  year={2025},
  publisher={SAGE Publications},
  doi={10.1177/20531680251356282}
}

---

Acknowledgments

• Funding: NSF Award 2311142
• Computing Resources: Delta system at NCSA (University of Illinois) through ACCESS allocation CIS220162
• Base Model: Unsloth team for Llama-3.1 8B Instruct optimizations
• Annotation Model: Event Data UTD team for ConfliBERT
• Data Source: Times of India corpus
• Infrastructure: Hugging Face for model hosting and transformers library

---

Model Versions

This repository contains the following model variants:

• 16-bit merged: Full precision, largest file size, highest quality
• 4-bit quantized: Smaller, faster inference with minimal quality loss
• GGUF (Q4_K_M, Q8_0): Optimized for llama.cpp deployment
• LoRA adapters: For efficient storage and further fine-tuning

---

Future Work

Potential improvements for future iterations:

1. Complete Training: Run for 2-3 full epochs to reduce loss below 0.90
2. Hyperparameter Tuning: Increase learning rate, warmup, and LoRA rank
3. Enable Evaluation: Add validation set monitoring for optimal checkpoint selection
4. Expand Entity Types: Include Events, Quantities, Miscellaneous categories
5. Multilingual Support: Add Hindi, other Indian languages
6. Human Validation: Sample-based quality assessment of ConfliBERT annotations
7. Cross-Domain Testing: Evaluate on non-Indian news sources

---

License

Apache 2.0 - See LICENSE for details.

---

Related Resources

• Original ConflLlama (Attack Classification): shreyasmeher/confllama
• ConflLlama-NER (CAMEO version): shreyasmeher/confllama-ner-sft
• ConfliBERT Annotation Model: eventdata-utd/conflibert-named-entity-recognition
• Research Paper: https://doi.org/10.1177/20531680251356282
• Unsloth Framework: https://github.com/unslothai/unsloth

---

Contact

For questions, issues, or collaboration inquiries:

• GitHub Issues: Repository Issues
• Hugging Face: @shreyasmeher
• Email: shreyas.meher@utdallas.edu

---