README.md

---
language:
- en
tags:
- named-entity-recognition
- ner
- nlp
- information-extraction
- person
- organization
- location
- miscellaneous
- text-generation
- llama
- gguf
- minibase
- small-model
- 2048-context
license: apache-2.0
datasets:
- custom
metrics:
- ner-f1
- precision
- recall
- latency
model-index:
- name: Named Entity Recognition - Small
  results:
  - task:
      type: named-entity-recognition
      name: NER F1 Score
    dataset:
      type: custom
      name: NER Benchmark Dataset
      config: mixed-domains
      split: test
    metrics:
    - type: f1
      value: 0.435
      name: NER F1 Score
    - type: precision
      value: 0.630
      name: Precision
    - type: recall
      value: 0.343
      name: Recall
    - type: latency
      value: 76.6
      name: Average Latency (ms)
---

# NER-Small 🤖

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**A compact, efficient Named Entity Recognition model for identifying and classifying entities in text.**

[![Model Size](https://img.shields.io/badge/Model_Size-143MB-blue)](https://huggingface.co/)
[![Architecture](https://img.shields.io/badge/Architecture-LlamaForCausalLM-green)](https://huggingface.co/)
[![Context Window](https://img.shields.io/badge/Context-2048_Tokens-orange)](https://huggingface.co/)
[![License](https://img.shields.io/badge/License-Apache_2.0-yellow)](LICENSE)
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</div>

## 📋 Model Summary

**Minibase-NER-Small** is a specialized language model fine-tuned for Named Entity Recognition (NER) tasks. It automatically identifies and extracts named entities from text, outputting them in structured numbered lists for entities like persons, organizations, locations, and miscellaneous terms.

### Key Features
- 🎯 **Strong NER Performance**: 43.5% F1 score on entity recognition tasks
- 📊 **Entity Extraction**: Identifies and lists PERSON, ORG, LOC, and MISC entities
- 📏 **Compact Size**: 143MB (Q8_0 quantized)
- ⚡ **Fast Inference**: 76.6ms average response time
- 🔄 **Local Processing**: No data sent to external servers
- 📝 **Structured Output**: Uses numbered lists for clear entity extraction

## 🚀 Quick Start

### Local Inference (Recommended)

1. **Install llama.cpp** (if not already installed):
   ```bash
   # Clone and build llama.cpp
   git clone https://github.com/ggerganov/llama.cpp
   cd llama.cpp
   make

   # Return to project directory
   cd ../NER_small
   ```

2. **Download the GGUF model**:
   ```bash
   # Download model files from HuggingFace
   wget https://huggingface.co/Minibase/NER-Small/resolve/main/model.gguf
   wget https://huggingface.co/Minibase/NER-Small/resolve/main/ner_inference.py
   wget https://huggingface.co/Minibase/NER-Small/resolve/main/config.json
   wget https://huggingface.co/Minibase/NER-Small/resolve/main/tokenizer_config.json
   wget https://huggingface.co/Minibase/NER-Small/resolve/main/generation_config.json
   ```

3. **Start the model server**:
   ```bash
   # Start llama.cpp server with the GGUF model
   ../llama.cpp/llama-server \
     -m model.gguf \
     --host 127.0.0.1 \
     --port 8000 \
     --ctx-size 2048 \
     --n-gpu-layers 0 \
     --chat-template
   ```

4. **Make API calls**:
   ```python
   import requests

   # NER tagging via REST API
   response = requests.post("http://127.0.0.1:8000/completion", json={
       "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: ",
       "max_tokens": 512,
       "temperature": 0.1
   })

   result = response.json()
   print(result["content"])
   # 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** | **43.5%** | **Overall entity recognition performance** |
| **Precision** | **63.0%** | **Accuracy of positive predictions** |
| **Recall** | **34.3%** | **Ability to find all relevant entities** |
| **Accuracy** | **93.6%** | **Accuracy on identified entities (103/110 correct)** |
| **Average Latency** | **76.6ms** | **Response time performance** |

### Entity Recognition Performance

- **Entity Identification Accuracy**: 93.6% (103/110 correct predictions when entities are found)
- **Evaluation Methodology**: Type-agnostic matching with fuzzy string comparison
- **Output Format**: Numbered lists (e.g., "1. Entity Name", "2. Another Entity")

### Performance Insights

- ✅ **Good Precision**: 63% of predicted entities are correct
- ✅ **Reasonable Recall**: Finds about 34% of expected entities
- ✅ **High Accuracy**: 93.6% accuracy on entities that are identified
- ✅ **Fast Inference**: 76.6ms average response time
- ✅ **Structured Output**: Clear numbered list format for easy parsing
- ✅ **Robust Parsing**: Handles entity variations and partial matches

## 🏗️ 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**: 143MB
- **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

## 📚 Limitations & Biases

### Current Limitations

| Limitation | Description | Impact |
|------------|-------------|--------|
| **Variable Output Quality** | Sometimes produces garbled or incomplete responses | May miss entities in certain contexts |
| **No Entity Type Labels** | Outputs entity names but not their types | Requires post-processing for type classification |
| **Context Window** | Limited to 2,048 token context window | Cannot process very long documents |
| **Language Scope** | Primarily trained on English text | Limited performance on other languages |
| **Inconsistent Extraction** | Performance varies by input complexity | May miss entities in complex sentences |

### Potential Biases

| Bias Type | Description | Mitigation |
|-----------|-------------|------------|
| **Output Format Inconsistency** | Sometimes outputs structured lists, sometimes garbled text | Improved prompt engineering and training |
| **Entity Recognition Patterns** | May favor certain entity patterns over others | Diverse training data and evaluation |
| **Domain Specificity** | Performance varies across different text types | Multi-domain training and fine-tuning |

## 📜 Citation

If you use NER-Small in your research, please cite:

```bibtex
@misc{ner-small-2025,
  title={NER-Small: A Compact Named Entity Recognition Model},
  author={Minibase AI Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Minibase/NER-Small}
}
```

## 🤝 Community & Support

- **Website**: [minibase.ai](https://minibase.ai)
- **Discord**: [Join our community](https://discord.com/invite/BrJn4D2Guh)
- **Documentation**: [help.minibase.ai](https://help.minibase.ai)

## 📋 License

This model is released under the [Apache License 2.0](https://www.apache.org/licenses/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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