Sample part-of-speech tagger in ML․NET

When using AveragedPerceptron as the trainer, the testset metrics are as follows.

• Accuracy (micro-avg): 0.9154 # 0..1, higher is better
• Accuracy (macro): 0.6963 # 0..1, higher is better
• Top-K accuracy: [0.9154, 0.9636, 0.9785, 0.9846, 0.9879, 0.9898, 0.9907, 0.9915, 0.9921, 0.9926, 0.9929, 0.9933, 0.9938, 0.9941, 0.9946, 0.9949, 0.9953, 0.9958, 0.9959, 0.9961, 0.9964, 0.9966, 0.9969, 0.9971, 0.9972, 0.9974, 0.9975, 0.9976, 0.9977, 0.9980, 0.9981, 0.9983, 0.9985, 0.9986, 0.9986, 0.9987, 0.9988, 0.9988, 0.9989, 0.9989, 0.9990, 0.9991, 0.9993, 0.9993, 0.9995, 0.9995, 0.9996, 0.9997, 0.9997, 0.9997, 0.9998, 0.9999, 0.9999, 1.0000, 1.0000] # 0..1, higher is better
• Log-loss reduction: 0.8497 # -Inf..1, higher is better
• Log-loss: 0.4515 # 0..Inf, lower is better

Dataset: CoNLL-2000