precision recall f1-score support ADJ 0.9040 0.8828 0.8933 128 ADJFP 0.9811 0.9585 0.9697 434 ADJFS 0.9606 0.9826 0.9715 918 ADJMP 0.9613 0.9357 0.9483 451 ADJMS 0.9561 0.9611 0.9586 952 ADV 0.9870 0.9948 0.9908 1524 AUX 0.9956 0.9964 0.9960 1124 CHIF 0.9798 0.9774 0.9786 1239 COCO 1.0000 0.9989 0.9994 884 COSUB 0.9939 0.9939 0.9939 328 DET 0.9972 0.9972 0.9972 2897 DETFS 0.9990 1.0000 0.9995 1007 DETMS 1.0000 0.9993 0.9996 1426 DINTFS 0.9967 0.9902 0.9934 306 DINTMS 0.9923 0.9948 0.9935 387 INTJ 0.8000 0.8000 0.8000 5 MOTINC 0.5049 0.5827 0.5410 266 NFP 0.9807 0.9675 0.9740 892 NFS 0.9778 0.9699 0.9738 2588 NMP 0.9687 0.9495 0.9590 1367 NMS 0.9759 0.9560 0.9659 3181 NOUN 0.6164 0.8673 0.7206 113 NUM 0.6250 0.8333 0.7143 6 PART 1.0000 0.9375 0.9677 16 PDEMFP 1.0000 1.0000 1.0000 3 PDEMFS 1.0000 1.0000 1.0000 89 PDEMMP 1.0000 1.0000 1.0000 20 PDEMMS 1.0000 1.0000 1.0000 222 PINDFP 1.0000 1.0000 1.0000 3 PINDFS 0.8571 1.0000 0.9231 12 PINDMP 0.9000 1.0000 0.9474 9 PINDMS 0.9286 0.9701 0.9489 67 PINTFS 0.0000 0.0000 0.0000 2 PPER1S 1.0000 1.0000 1.0000 62 PPER2S 0.7500 1.0000 0.8571 3 PPER3FP 1.0000 1.0000 1.0000 9 PPER3FS 1.0000 1.0000 1.0000 96 PPER3MP 1.0000 1.0000 1.0000 31 PPER3MS 1.0000 1.0000 1.0000 377 PPOBJFP 1.0000 0.7500 0.8571 4 PPOBJFS 0.9167 0.8919 0.9041 37 PPOBJMP 0.7500 0.7500 0.7500 12 PPOBJMS 0.9371 0.9640 0.9504 139 PREF 1.0000 1.0000 1.0000 332 PREFP 1.0000 1.0000 1.0000 64 PREFS 1.0000 1.0000 1.0000 13 PREL 0.9964 0.9964 0.9964 277 PRELFP 1.0000 1.0000 1.0000 5 PRELFS 0.8000 1.0000 0.8889 4 PRELMP 1.0000 1.0000 1.0000 3 PRELMS 1.0000 1.0000 1.0000 11 PREP 0.9971 0.9977 0.9974 6161 PRON 0.9836 0.9836 0.9836 61 PROPN 0.9468 0.9503 0.9486 4310 PUNCT 1.0000 1.0000 1.0000 4019 SYM 0.9394 0.8158 0.8732 76 VERB 0.9956 0.9921 0.9938 2273 VPPFP 0.9145 0.9469 0.9304 113 VPPFS 0.9562 0.9597 0.9580 273 VPPMP 0.8827 0.9728 0.9256 147 VPPMS 0.9778 0.9794 0.9786 630 VPPRE 0.0000 0.0000 0.0000 1 X 0.9604 0.9935 0.9766 1073 XFAMIL 0.9386 0.9113 0.9248 1342 YPFOR 1.0000 1.0000 1.0000 2750 accuracy 0.9778 47574 macro avg 0.9151 0.9285 0.9202 47574 weighted avg 0.9785 0.9778 0.9780 47574 DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'pos_tags'], num_rows: 14453 }) validation: Dataset({ features: ['id', 'tokens', 'pos_tags'], num_rows: 1477 }) test: Dataset({ features: ['id', 'tokens', 'pos_tags'], num_rows: 417 }) })