benchmarks.txt

# NER Benchmark Results
**Model:** Minibase-NER-Standard
**Dataset:** ner_benchmark_dataset.jsonl
**Sample Size:** 100
**Date:** 2025-10-07T13:41:36.866891

## Overall Performance

| Metric | Score | Description |
|--------|-------|-------------|
| F1 Score | 0.951 | Overall NER performance (harmonic mean of precision and recall) |
| Precision | 0.915 | Accuracy of entity predictions |
| Recall | 1.000 | Ability to find all entities |
| Average Latency | 323.3ms | Response time performance |

## Entity Type Performance

| Entity Type | Accuracy | Correct/Total |
|-------------|----------|---------------|
| PERSON | 1.000 | 100/100 |
| ORG | 1.000 | 100/100 |
| LOC | 0.660 | 66/100 |
| MISC | 1.000 | 34/34 |

## 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:** { "PER": ["John Smith"], "ORG": ["Google"], "LOC": ["New York"], "MISC": ["Python"] }...
**F1 Score:** 0.857

### Example 2
**Input:** Microsoft Corporation announced that Satya Nadella will visit London next week....
**Predicted:** { "PER": ["Satya Nadella"], "ORG": ["Microsoft Corporation"], "LOC": ["London"], "MISC": [] }...
**F1 Score:** 1.000

### Example 3
**Input:** The University of Cambridge is located in the United Kingdom and was founded by King Henry III....
**Predicted:** { "PER": ["King Henry III"], "ORG": ["University of Cambridge"], "LOC": ["United Kingdom"], "MISC": [] }...
**F1 Score:** 1.000