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cf495ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ================================================================================ LANGUAGE IDENTIFICATION MODEL EVALUATION SUMMARY ================================================================================ Evaluation Date: 2026-01-31 15:03:05 Model: Hybrid TF-IDF + BiLSTM Language Identifier Dataset: WiLI-2018 Number of languages: 235 Vocabulary size: 20,002 Total test samples: 117,500 PERFORMANCE METRICS: Test Accuracy: 93.7481% Test F1 Score: 93.7531% TOP 5 BEST PERFORMING LANGUAGES: 1. ckb - 100.00% accuracy (500 samples) 2. kbd - 100.00% accuracy (500 samples) 3. min - 100.00% accuracy (500 samples) 4. mlg - 100.00% accuracy (500 samples) 5. bod - 99.80% accuracy (500 samples) MOST CHALLENGING 5 LANGUAGES: 1. wuu - 15.60% accuracy (500 samples) 2. zh-yue - 22.80% accuracy (500 samples) 3. zho - 37.00% accuracy (500 samples) 4. hrv - 46.80% accuracy (500 samples) 5. hbs - 53.80% accuracy (500 samples) PERFORMANCE DISTRIBUTION: Excellent (≥99%): 25 languages Good (95-99%): 131 languages Average (80-95%): 68 languages Poor (<80%): 11 languages INTERESTING FINDINGS: 1. Several languages achieve 100% accuracy (ckb, kbd, min, mlg) 2. Chinese variants are the most challenging (wuu: 15.6%, zh-yue: 22.8%) 3. Japanese is surprisingly challenging (56.0% accuracy) 4. 93.75% overall accuracy is excellent for 235 languages RECOMMENDATIONS FOR IMPROVEMENT: 1. Add data augmentation for low-accuracy languages 2. Consider language family-based transfer learning 3. Ensemble methods could boost performance ================================================================================ |