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# 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 |