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NER-Standard 🤖

Name: NER-Standard
Rating: 2.5 (75 reviews)

A high-performance Named Entity Recognition model with superior accuracy and structured JSON output.

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📋 Model Summary

Minibase-NER-Standard is a high-performance language model fine-tuned for Named Entity Recognition (NER) tasks. It automatically identifies and extracts named entities from text, outputting them in structured JSON format with proper entity type classification for persons, organizations, locations, and miscellaneous terms.

Key Features

🎯 Excellent NER Performance: 95.1% F1 score on entity recognition tasks
📊 Entity Classification: Accurately categorizes PERSON, ORG, LOC, and MISC entities
📏 Optimized Size: 369MB (Q8_0 quantized)
⚡ Efficient Inference: 323.3ms average response time
🔄 Local Processing: No data sent to external servers
🏗️ Structured JSON Output: Clean, parseable entity extraction results

🚀 Quick Start

Local Inference (Recommended)

Install llama.cpp (if not already installed):

bash
1# Clone and build llama.cpp 2git clone https://github.com/ggerganov/llama.cpp 3cd llama.cpp 4make 5 6# Return to project directory 7cd ../NER_small
Download the GGUF model:

bash
1# Download model files from HuggingFace 2wget https://huggingface.co/Minibase/NER-Small/resolve/main/model.gguf 3wget https://huggingface.co/Minibase/NER-Small/resolve/main/ner_inference.py 4wget https://huggingface.co/Minibase/NER-Small/resolve/main/config.json 5wget https://huggingface.co/Minibase/NER-Small/resolve/main/tokenizer_config.json 6wget https://huggingface.co/Minibase/NER-Small/resolve/main/generation_config.json
Start the model server:

bash
1# Start llama.cpp server with the GGUF model 2../llama.cpp/llama-server \ 3 -m model.gguf \ 4 --host 127.0.0.1 \ 5 --port 8000 \ 6 --ctx-size 2048 \ 7 --n-gpu-layers 0 \ 8 --chat-template
Make API calls:

python
1import requests 2 3# NER tagging via REST API 4response = requests.post("http://127.0.0.1:8000/completion", json={ 5 "prompt": "Instruction: Identify and tag all named entities in the following text. Use BIO format with entity types: PERSON, ORG, LOC, MISC.\n\nInput: John Smith works at Google in New York.\n\nResponse: ", 6 "max_tokens": 512, 7 "temperature": 0.1 8}) 9 10result = response.json() 11print(result["content"]) 12# Output: "John B-PERSON\nSmith I-PERSON\nworks O\nat O\nGoogle B-ORG\nin O\nNew York B-LOC\nI-LOC\n."

Python Client (Recommended)

python

# Download and use the provided Python client
from ner_inference import NERClient

# Initialize client (connects to local server)
client = NERClient()

# Tag entities in text
text = "Apple Inc. was founded by Steve Jobs in Cupertino, California."
entities = client.extract_entities(text)

print(entities)
# Output: [
#   {"text": "Apple Inc.", "type": "ORG", "start": 0, "end": 9},
#   {"text": "Steve Jobs", "type": "PERSON", "start": 24, "end": 34},
#   {"text": "Cupertino", "type": "LOC", "start": 38, "end": 47},
#   {"text": "California", "type": "LOC", "start": 49, "end": 59}
# ]

# Batch processing
texts = [
    "Microsoft announced a new CEO.",
    "Paris is the capital of France."
]
all_entities = client.extract_entities_batch(texts)
print(all_entities)

Direct llama.cpp Usage

python

# Alternative: Use llama.cpp directly without server
import subprocess
import json

def extract_entities_with_llama_cpp(text: str) -> str:
    prompt = f"Instruction: Identify and tag all named entities in the following text. Use BIO format with entity types: PERSON, ORG, LOC, MISC.\n\nInput: {text}\n\nResponse: "

    # Run llama.cpp directly
    cmd = [
        "../llama.cpp/llama-cli",
        "-m", "model.gguf",
        "--prompt", prompt,
        "--ctx-size", "2048",
        "--n-predict", "512",
        "--temp", "0.1",
        "--log-disable"
    ]

    result = subprocess.run(cmd, capture_output=True, text=True, cwd=".")
    return result.stdout.strip()

# Usage
result = extract_entities_with_llama_cpp("John Smith works at Google in New York.")
print(result)

📊 Benchmarks & Performance

Overall Performance (100 samples)

Metric	Score	Description
NER F1 Score	95.1%	Overall entity recognition performance
Precision	91.5%	Accuracy of entity predictions
Recall	100.0%	Perfect recall - finds all relevant entities
Average Latency	323.3ms	Response time performance

Entity Recognition Performance

Entity Identification Accuracy: 100% (100/100 correct predictions when entities are found)
Evaluation Methodology: JSON parsing with exact entity type matching
Output Format: Structured JSON (e.g., {"PER": ["John Smith"], "ORG": ["Google"]})

Performance Insights

✅ Excellent F1 Score: 95.1% overall performance
✅ Perfect Recall: 100% of expected entities are found
✅ High Precision: 91.5% accuracy on identified entities
✅ Structured Output: Clean JSON format with proper entity categorization
✅ High Reliability: Consistent performance across different entity types
✅ Production Ready: Excellent for real-world NER applications

💡 Examples

Here are real examples from the benchmark evaluation showing the structured JSON output:

🏢 Business Example

Input:

Microsoft Corporation announced that Satya Nadella will visit London next week.

Output:

{"PER": ["Satya Nadella"], "ORG": ["Microsoft Corporation"], "LOC": ["London"]}

Analysis: Perfect entity identification and categorization - 3/3 entities correctly identified and classified.

🎓 Academic Example

Input:

The University of Cambridge is located in the United Kingdom and was founded by King Henry III.

Output:

{"PER": ["King Henry III"], "ORG": ["University of Cambridge"], "LOC": ["United Kingdom"]}

Analysis: All 3 entities correctly identified and properly categorized by type.

💼 Professional Example

Input:

John Smith works at Google in New York and uses Python programming language.

Output:

{"PER": ["John Smith"], "ORG": ["Google"], "LOC": ["New York"], "MISC": ["Python"]}

Analysis: Complete entity extraction with accurate type classification for all 4 entities found.

🏗️ Technical Details

Model Architecture

Architecture: LlamaForCausalLM
Parameters: 135M (small capacity)
Context Window: 2,048 tokens
Max Position Embeddings: 2,048
Quantization: GGUF (Q8_0 quantization)
File Size: 369MB
Memory Requirements: 8GB RAM minimum, 16GB recommended

Training Details

Base Model: Custom-trained Llama architecture
Fine-tuning Dataset: Mixed-domain entity recognition data
Training Objective: Named entity extraction and listing
Optimization: Quantized for efficient inference
Model Scale: Small capacity optimized for speed

System Requirements

Component	Minimum	Recommended
Operating System	Linux, macOS, Windows	Linux or macOS
RAM	8GB	16GB
Storage	150MB free space	500MB free space
Python	3.8+	3.10+
Dependencies	llama.cpp	llama.cpp, requests

Notes:

✅ CPU-only inference supported but slower
✅ GPU acceleration provides significant speed improvements
✅ Apple Silicon users get Metal acceleration automatically

📜 Citation

If you use Named Entity Recognition - Standard in your research, please cite:

bibtex

@misc{ner-standard-2025,
  title={Named Entity Recognition - Standard: High-Performance Named Entity Recognition Model},
  author={Minibase AI Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Minibase/NER-Standard}
}

🤝 Community & Support

Website: minibase.ai
Discord: Join our community
Documentation: help.minibase.ai

📋 License

This model is released under the Apache License 2.0.

🙏 Acknowledgments

CoNLL-2003 Dataset: Used for training and evaluation
llama.cpp: For efficient local inference
Hugging Face: For model hosting and community
Our amazing community: For feedback and contributions

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