Table 8 The performance after removing the deep learning module.
From: Information extraction from green channel textual records on expressways using hybrid deep learning
Model | BERT-CRF | ALBERT-CRF | RoBERTa-CRF | ||||||
|---|---|---|---|---|---|---|---|---|---|
Precision | Recall | F1 (%) | Precision | Recall | F1 (%) | Precision | Recall | F1 (%) | |
Cheating | 82.56% | 93.42% | 87.65 | 37.62% | 50.00% | 42.94 | 96.10% | 97.37% | 96.73 |
Mixed | 98.50% | 99.85% | 99.17 | 95.38% | 97.26% | 96.31 | 100.00% | 100.00% | 100 |
Out-listed | 92.90% | 97.52% | 95.15 | 76.71% | 86.54% | 81.33 | 97.05% | 98.95% | 97.99 |
Not fresh | 80.28% | 90.48% | 85.07 | 64.00% | 76.19% | 69.57 | 89.23% | 92.06% | 90.62 |
Frozen | 84.80% | 80.30% | 82.49 | 64.75% | 68.18% | 66.42 | 82.11% | 76.52% | 79.22 |
Spoilage | 88.19% | 95.73% | 91.80 | 66.90% | 82.91% | 74.05 | 91.13% | 96.58% | 93.78 |
Over-loading | 97.67% | 98.88% | 98.27 | 87.03% | 91.13% | 89.04 | 99.89% | 100.00% | 99.94 |
Deep-processing | 75.48% | 79.60% | 77.48 | 55.56% | 66.09% | 60.37 | 83.38% | 85.06% | 84.21 |
F | – | – | 89.64 | – | – | 72.50 | – | – | 92.81 |
