# NER Benchmark Results
**Model:** Minibase-NER-Small
**Dataset:** ner_benchmark_dataset.jsonl
**Sample Size:** 100
**Date:** 2025-10-07T13:20:42.785262

## Overall Performance

| Metric | Score | Description |
|--------|-------|-------------|
| F1 Score | 0.435 | Overall NER performance (harmonic mean of precision and recall) |
| Precision | 0.630 | Accuracy of entity predictions |
| Recall | 0.343 | Ability to find all entities |
| Average Latency | 76.6ms | Response time performance |

## Entity Type Performance

| Entity Type | Accuracy | Correct/Total |
|-------------|----------|---------------|
| ENTITY | 0.936 | 103/110 |

## Key Improvements

- **BIO Tagging**: Model outputs entities in BIO (Beginning-Inside-Outside) format
- **Multiple Entity Types**: Supports PERSON, ORG, LOC, and MISC entities
- **Entity-Level Evaluation**: Metrics calculated at entity level rather than token level
- **Comprehensive Coverage**: Evaluates across different text domains

## Example Results

### Example 1
**Input:** John Smith works at Google in New York and uses Python programming language....
**Predicted:** PERGON, ORG...
**F1 Score:** 0.000

### Example 2
**Input:** Microsoft Corporation announced that Satya Nadella will visit London next week....
**Predicted:** 1. Microsoft Corporation...
**F1 Score:** 0.500

### Example 3
**Input:** The University of Cambridge is located in the United Kingdom and was founded by King Henry III....
**Predicted:** 1. The University of Cambridge
2. King Henry III...
**F1 Score:** 0.800