Home > AI Solutions > Artificial Intelligence > White Papers > MLPerf Inference v3.1: Results using Dell Servers with Qualcomm Cloud AI 100 Accelerators > DellNER
DellNER is a custom Named Entity Recognition (NER) model that helps identify pre-defined entities in text data, as shown in the following figure. Currently, DellNER helps identify the following entities:
2000-0141, amber white-off-of
DellNER was trained on a corpus of more than 1 million case logs generated by technical support agents during troubleshooting for the client business. The training data was bootstrapped using a combination of static term lists and regular expressions. For evaluating model performance, a sample of 1,000 case logs was manually labelled. The model performance is shown in the following table:
Entity | Precision | Recall | F1 |
HARDWARE | 70 | 75 | 72 |
SOFTWARE | 57 | 59 | 58 |
DIAGTEST | 88 | 77 | 82 |
OVERALL | 58 | 70 | 64 |
Note: DIAGCODE was not included in test results above since there was a very small sample of it in the test set (<10 occurrences).