| 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 | |
| }) | |
| }) | |