| # 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 | |