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2022-08-24 10:57:35,091 ----------------------------------------------------------------------------------------------------
2022-08-24 10:57:35,092 Model: "SequenceTagger(
(embeddings): StackedEmbeddings(
(list_embedding_0): WordEmbeddings(
'hindi'
(embedding): Embedding(1000000, 300)
)
(list_embedding_1): FlairEmbeddings(
(lm): LanguageModel(
(drop): Dropout(p=0.1, inplace=False)
(encoder): Embedding(3520, 100)
(rnn): LSTM(100, 2048)
(decoder): Linear(in_features=2048, out_features=3520, bias=True)
)
)
(list_embedding_2): FlairEmbeddings(
(lm): LanguageModel(
(drop): Dropout(p=0.1, inplace=False)
(encoder): Embedding(3520, 100)
(rnn): LSTM(100, 2048)
(decoder): Linear(in_features=2048, out_features=3520, bias=True)
)
)
)
(word_dropout): WordDropout(p=0.05)
(locked_dropout): LockedDropout(p=0.5)
(embedding2nn): Linear(in_features=4396, out_features=4396, bias=True)
(rnn): LSTM(4396, 256, batch_first=True, bidirectional=True)
(linear): Linear(in_features=512, out_features=34, bias=True)
(loss_function): ViterbiLoss()
(crf): CRF()
)"
2022-08-24 10:57:35,092 ----------------------------------------------------------------------------------------------------
2022-08-24 10:57:35,093 Corpus: "Corpus: 13304 train + 1659 dev + 1684 test sentences"
2022-08-24 10:57:35,093 ----------------------------------------------------------------------------------------------------
2022-08-24 10:57:35,093 Parameters:
2022-08-24 10:57:35,094 - learning_rate: "0.100000"
2022-08-24 10:57:35,094 - mini_batch_size: "32"
2022-08-24 10:57:35,094 - patience: "3"
2022-08-24 10:57:35,095 - anneal_factor: "0.5"
2022-08-24 10:57:35,095 - max_epochs: "10"
2022-08-24 10:57:35,095 - shuffle: "True"
2022-08-24 10:57:35,096 - train_with_dev: "False"
2022-08-24 10:57:35,096 - batch_growth_annealing: "False"
2022-08-24 10:57:35,097 ----------------------------------------------------------------------------------------------------
2022-08-24 10:57:35,097 Model training base path: "resources/taggers"
2022-08-24 10:57:35,097 ----------------------------------------------------------------------------------------------------
2022-08-24 10:57:35,130 Device: cuda:0
2022-08-24 10:57:35,131 ----------------------------------------------------------------------------------------------------
2022-08-24 10:57:35,131 Embeddings storage mode: cpu
2022-08-24 10:57:35,131 ----------------------------------------------------------------------------------------------------
2022-08-24 10:57:43,221 epoch 1 - iter 41/416 - loss 1.75374784 - samples/sec: 163.39 - lr: 0.100000
2022-08-24 10:57:48,774 epoch 1 - iter 82/416 - loss 1.21144958 - samples/sec: 236.39 - lr: 0.100000
2022-08-24 10:57:54,449 epoch 1 - iter 123/416 - loss 0.96022365 - samples/sec: 231.30 - lr: 0.100000
2022-08-24 10:58:00,063 epoch 1 - iter 164/416 - loss 0.81474244 - samples/sec: 233.84 - lr: 0.100000
2022-08-24 10:58:06,071 epoch 1 - iter 205/416 - loss 0.72188458 - samples/sec: 218.46 - lr: 0.100000
2022-08-24 10:58:12,045 epoch 1 - iter 246/416 - loss 0.65566787 - samples/sec: 222.79 - lr: 0.100000
2022-08-24 10:58:17,908 epoch 1 - iter 287/416 - loss 0.60358770 - samples/sec: 223.89 - lr: 0.100000
2022-08-24 10:58:24,130 epoch 1 - iter 328/416 - loss 0.56015346 - samples/sec: 210.95 - lr: 0.100000
2022-08-24 10:58:30,431 epoch 1 - iter 369/416 - loss 0.52768132 - samples/sec: 208.36 - lr: 0.100000
2022-08-24 10:58:36,388 epoch 1 - iter 410/416 - loss 0.50102804 - samples/sec: 220.39 - lr: 0.100000
2022-08-24 10:58:37,307 ----------------------------------------------------------------------------------------------------
2022-08-24 10:58:37,308 EPOCH 1 done: loss 0.4973 - lr 0.100000
2022-08-24 10:58:47,137 Evaluating as a multi-label problem: False
2022-08-24 10:58:47,698 DEV : loss 0.16205480694770813 - f1-score (micro avg) 0.9458
2022-08-24 10:58:47,863 BAD EPOCHS (no improvement): 0
2022-08-24 10:58:47,865 saving best model
2022-08-24 10:58:51,789 ----------------------------------------------------------------------------------------------------
2022-08-24 10:58:55,679 epoch 2 - iter 41/416 - loss 0.24514879 - samples/sec: 343.22 - lr: 0.100000
2022-08-24 10:58:59,592 epoch 2 - iter 82/416 - loss 0.24050334 - samples/sec: 335.61 - lr: 0.100000
2022-08-24 10:59:03,458 epoch 2 - iter 123/416 - loss 0.23347290 - samples/sec: 339.70 - lr: 0.100000
2022-08-24 10:59:07,545 epoch 2 - iter 164/416 - loss 0.23151347 - samples/sec: 322.37 - lr: 0.100000
2022-08-24 10:59:11,970 epoch 2 - iter 205/416 - loss 0.22632911 - samples/sec: 299.49 - lr: 0.100000
2022-08-24 10:59:15,668 epoch 2 - iter 246/416 - loss 0.22429595 - samples/sec: 355.20 - lr: 0.100000
2022-08-24 10:59:19,397 epoch 2 - iter 287/416 - loss 0.22215739 - samples/sec: 352.18 - lr: 0.100000
2022-08-24 10:59:23,375 epoch 2 - iter 328/416 - loss 0.21962025 - samples/sec: 330.16 - lr: 0.100000
2022-08-24 10:59:27,423 epoch 2 - iter 369/416 - loss 0.21729297 - samples/sec: 324.45 - lr: 0.100000
2022-08-24 10:59:31,761 epoch 2 - iter 410/416 - loss 0.21444959 - samples/sec: 302.89 - lr: 0.100000
2022-08-24 10:59:32,350 ----------------------------------------------------------------------------------------------------
2022-08-24 10:59:32,350 EPOCH 2 done: loss 0.2144 - lr 0.100000
2022-08-24 10:59:39,506 Evaluating as a multi-label problem: False
2022-08-24 10:59:40,286 DEV : loss 0.11246180534362793 - f1-score (micro avg) 0.9578
2022-08-24 10:59:40,475 BAD EPOCHS (no improvement): 0
2022-08-24 10:59:40,477 saving best model
2022-08-24 10:59:44,103 ----------------------------------------------------------------------------------------------------
2022-08-24 10:59:48,468 epoch 3 - iter 41/416 - loss 0.18469601 - samples/sec: 301.11 - lr: 0.100000
2022-08-24 10:59:52,504 epoch 3 - iter 82/416 - loss 0.18261678 - samples/sec: 325.54 - lr: 0.100000
2022-08-24 10:59:56,236 epoch 3 - iter 123/416 - loss 0.18163090 - samples/sec: 352.04 - lr: 0.100000
2022-08-24 11:00:00,041 epoch 3 - iter 164/416 - loss 0.17863873 - samples/sec: 345.20 - lr: 0.100000
2022-08-24 11:00:03,939 epoch 3 - iter 205/416 - loss 0.17689939 - samples/sec: 336.91 - lr: 0.100000
2022-08-24 11:00:07,952 epoch 3 - iter 246/416 - loss 0.17651204 - samples/sec: 327.26 - lr: 0.100000
2022-08-24 11:00:11,880 epoch 3 - iter 287/416 - loss 0.17610330 - samples/sec: 334.48 - lr: 0.100000
2022-08-24 11:00:15,861 epoch 3 - iter 328/416 - loss 0.17480990 - samples/sec: 329.95 - lr: 0.100000
2022-08-24 11:00:19,654 epoch 3 - iter 369/416 - loss 0.17340667 - samples/sec: 346.31 - lr: 0.100000
2022-08-24 11:00:23,590 epoch 3 - iter 410/416 - loss 0.17286643 - samples/sec: 333.65 - lr: 0.100000
2022-08-24 11:00:24,178 ----------------------------------------------------------------------------------------------------
2022-08-24 11:00:24,179 EPOCH 3 done: loss 0.1730 - lr 0.100000
2022-08-24 11:00:31,344 Evaluating as a multi-label problem: False
2022-08-24 11:00:31,586 DEV : loss 0.09120035171508789 - f1-score (micro avg) 0.9647
2022-08-24 11:00:31,765 BAD EPOCHS (no improvement): 0
2022-08-24 11:00:31,767 saving best model
2022-08-24 11:00:35,385 ----------------------------------------------------------------------------------------------------
2022-08-24 11:00:39,594 epoch 4 - iter 41/416 - loss 0.16103536 - samples/sec: 312.15 - lr: 0.100000
2022-08-24 11:00:43,611 epoch 4 - iter 82/416 - loss 0.15827466 - samples/sec: 327.05 - lr: 0.100000
2022-08-24 11:00:47,595 epoch 4 - iter 123/416 - loss 0.15368275 - samples/sec: 329.72 - lr: 0.100000
2022-08-24 11:00:51,430 epoch 4 - iter 164/416 - loss 0.15380442 - samples/sec: 342.46 - lr: 0.100000
2022-08-24 11:00:55,373 epoch 4 - iter 205/416 - loss 0.15360279 - samples/sec: 333.13 - lr: 0.100000
2022-08-24 11:00:59,206 epoch 4 - iter 246/416 - loss 0.15416655 - samples/sec: 345.39 - lr: 0.100000
2022-08-24 11:01:03,077 epoch 4 - iter 287/416 - loss 0.15297728 - samples/sec: 346.54 - lr: 0.100000
2022-08-24 11:01:07,189 epoch 4 - iter 328/416 - loss 0.15273117 - samples/sec: 319.42 - lr: 0.100000
2022-08-24 11:01:11,111 epoch 4 - iter 369/416 - loss 0.15136905 - samples/sec: 334.89 - lr: 0.100000
2022-08-24 11:01:14,964 epoch 4 - iter 410/416 - loss 0.15052223 - samples/sec: 340.79 - lr: 0.100000
2022-08-24 11:01:15,525 ----------------------------------------------------------------------------------------------------
2022-08-24 11:01:15,526 EPOCH 4 done: loss 0.1503 - lr 0.100000
2022-08-24 11:01:24,855 Evaluating as a multi-label problem: False
2022-08-24 11:01:25,108 DEV : loss 0.08160468190908432 - f1-score (micro avg) 0.9671
2022-08-24 11:01:25,259 BAD EPOCHS (no improvement): 0
2022-08-24 11:01:25,261 saving best model
2022-08-24 11:01:28,924 ----------------------------------------------------------------------------------------------------
2022-08-24 11:01:33,139 epoch 5 - iter 41/416 - loss 0.13452886 - samples/sec: 313.17 - lr: 0.100000
2022-08-24 11:01:36,957 epoch 5 - iter 82/416 - loss 0.13710874 - samples/sec: 344.08 - lr: 0.100000
2022-08-24 11:01:40,703 epoch 5 - iter 123/416 - loss 0.13645113 - samples/sec: 350.66 - lr: 0.100000
2022-08-24 11:01:44,909 epoch 5 - iter 164/416 - loss 0.13713570 - samples/sec: 312.19 - lr: 0.100000
2022-08-24 11:01:48,859 epoch 5 - iter 205/416 - loss 0.13547128 - samples/sec: 332.51 - lr: 0.100000
2022-08-24 11:01:52,795 epoch 5 - iter 246/416 - loss 0.13505763 - samples/sec: 333.65 - lr: 0.100000
2022-08-24 11:01:56,601 epoch 5 - iter 287/416 - loss 0.13405671 - samples/sec: 345.07 - lr: 0.100000
2022-08-24 11:02:00,371 epoch 5 - iter 328/416 - loss 0.13335547 - samples/sec: 348.32 - lr: 0.100000
2022-08-24 11:02:04,122 epoch 5 - iter 369/416 - loss 0.13393736 - samples/sec: 350.13 - lr: 0.100000
2022-08-24 11:02:07,887 epoch 5 - iter 410/416 - loss 0.13423791 - samples/sec: 348.83 - lr: 0.100000
2022-08-24 11:02:08,432 ----------------------------------------------------------------------------------------------------
2022-08-24 11:02:08,433 EPOCH 5 done: loss 0.1341 - lr 0.100000
2022-08-24 11:02:22,185 Evaluating as a multi-label problem: False
2022-08-24 11:02:22,641 DEV : loss 0.07825736701488495 - f1-score (micro avg) 0.9683
2022-08-24 11:02:22,794 BAD EPOCHS (no improvement): 0
2022-08-24 11:02:22,796 saving best model
2022-08-24 11:02:26,511 ----------------------------------------------------------------------------------------------------
2022-08-24 11:02:30,210 epoch 6 - iter 41/416 - loss 0.12471213 - samples/sec: 355.02 - lr: 0.100000
2022-08-24 11:02:34,090 epoch 6 - iter 82/416 - loss 0.12531338 - samples/sec: 338.40 - lr: 0.100000
2022-08-24 11:02:37,977 epoch 6 - iter 123/416 - loss 0.12700505 - samples/sec: 337.77 - lr: 0.100000
2022-08-24 11:02:41,958 epoch 6 - iter 164/416 - loss 0.12652385 - samples/sec: 329.91 - lr: 0.100000
2022-08-24 11:02:46,847 epoch 6 - iter 205/416 - loss 0.12700222 - samples/sec: 268.61 - lr: 0.100000
2022-08-24 11:02:50,839 epoch 6 - iter 246/416 - loss 0.12583029 - samples/sec: 329.02 - lr: 0.100000
2022-08-24 11:02:54,884 epoch 6 - iter 287/416 - loss 0.12590751 - samples/sec: 324.67 - lr: 0.100000
2022-08-24 11:02:58,955 epoch 6 - iter 328/416 - loss 0.12594140 - samples/sec: 328.72 - lr: 0.100000
2022-08-24 11:03:02,798 epoch 6 - iter 369/416 - loss 0.12619028 - samples/sec: 341.85 - lr: 0.100000
2022-08-24 11:03:06,816 epoch 6 - iter 410/416 - loss 0.12617017 - samples/sec: 326.85 - lr: 0.100000
2022-08-24 11:03:07,433 ----------------------------------------------------------------------------------------------------
2022-08-24 11:03:07,434 EPOCH 6 done: loss 0.1262 - lr 0.100000
2022-08-24 11:03:14,657 Evaluating as a multi-label problem: False
2022-08-24 11:03:14,881 DEV : loss 0.0752514973282814 - f1-score (micro avg) 0.97
2022-08-24 11:03:15,034 BAD EPOCHS (no improvement): 0
2022-08-24 11:03:15,036 saving best model
2022-08-24 11:03:18,770 ----------------------------------------------------------------------------------------------------
2022-08-24 11:03:22,706 epoch 7 - iter 41/416 - loss 0.11739473 - samples/sec: 333.76 - lr: 0.100000
2022-08-24 11:03:26,636 epoch 7 - iter 82/416 - loss 0.11762875 - samples/sec: 334.19 - lr: 0.100000
2022-08-24 11:03:30,530 epoch 7 - iter 123/416 - loss 0.11795678 - samples/sec: 337.32 - lr: 0.100000
2022-08-24 11:03:34,541 epoch 7 - iter 164/416 - loss 0.11731247 - samples/sec: 327.37 - lr: 0.100000
2022-08-24 11:03:39,870 epoch 7 - iter 205/416 - loss 0.11730825 - samples/sec: 247.45 - lr: 0.100000
2022-08-24 11:03:43,818 epoch 7 - iter 246/416 - loss 0.11685275 - samples/sec: 332.60 - lr: 0.100000
2022-08-24 11:03:47,733 epoch 7 - iter 287/416 - loss 0.11654411 - samples/sec: 335.53 - lr: 0.100000
2022-08-24 11:03:51,557 epoch 7 - iter 328/416 - loss 0.11687989 - samples/sec: 343.41 - lr: 0.100000
2022-08-24 11:03:55,312 epoch 7 - iter 369/416 - loss 0.11710931 - samples/sec: 349.86 - lr: 0.100000
2022-08-24 11:03:59,149 epoch 7 - iter 410/416 - loss 0.11723170 - samples/sec: 342.29 - lr: 0.100000
2022-08-24 11:03:59,718 ----------------------------------------------------------------------------------------------------
2022-08-24 11:03:59,719 EPOCH 7 done: loss 0.1172 - lr 0.100000
2022-08-24 11:04:06,739 Evaluating as a multi-label problem: False
2022-08-24 11:04:06,960 DEV : loss 0.07426313310861588 - f1-score (micro avg) 0.9696
2022-08-24 11:04:07,109 BAD EPOCHS (no improvement): 1
2022-08-24 11:04:07,110 ----------------------------------------------------------------------------------------------------
2022-08-24 11:04:10,972 epoch 8 - iter 41/416 - loss 0.11078633 - samples/sec: 340.11 - lr: 0.100000
2022-08-24 11:04:14,939 epoch 8 - iter 82/416 - loss 0.10893638 - samples/sec: 331.00 - lr: 0.100000
2022-08-24 11:04:18,833 epoch 8 - iter 123/416 - loss 0.10944998 - samples/sec: 337.22 - lr: 0.100000
2022-08-24 11:04:22,638 epoch 8 - iter 164/416 - loss 0.10903293 - samples/sec: 345.14 - lr: 0.100000
2022-08-24 11:04:26,635 epoch 8 - iter 205/416 - loss 0.10899615 - samples/sec: 328.58 - lr: 0.100000
2022-08-24 11:04:30,526 epoch 8 - iter 246/416 - loss 0.10934547 - samples/sec: 337.61 - lr: 0.100000
2022-08-24 11:04:34,391 epoch 8 - iter 287/416 - loss 0.10995397 - samples/sec: 339.83 - lr: 0.100000
2022-08-24 11:04:38,189 epoch 8 - iter 328/416 - loss 0.10996701 - samples/sec: 345.79 - lr: 0.100000
2022-08-24 11:04:41,970 epoch 8 - iter 369/416 - loss 0.11080413 - samples/sec: 347.42 - lr: 0.100000
2022-08-24 11:04:45,949 epoch 8 - iter 410/416 - loss 0.11086062 - samples/sec: 330.00 - lr: 0.100000
2022-08-24 11:04:46,488 ----------------------------------------------------------------------------------------------------
2022-08-24 11:04:46,489 EPOCH 8 done: loss 0.1109 - lr 0.100000
2022-08-24 11:04:53,723 Evaluating as a multi-label problem: False
2022-08-24 11:04:53,952 DEV : loss 0.07048413157463074 - f1-score (micro avg) 0.9713
2022-08-24 11:04:54,108 BAD EPOCHS (no improvement): 0
2022-08-24 11:04:54,109 saving best model
2022-08-24 11:04:57,751 ----------------------------------------------------------------------------------------------------
2022-08-24 11:05:01,731 epoch 9 - iter 41/416 - loss 0.10321695 - samples/sec: 330.13 - lr: 0.100000
2022-08-24 11:05:05,885 epoch 9 - iter 82/416 - loss 0.10395837 - samples/sec: 316.14 - lr: 0.100000
2022-08-24 11:05:09,765 epoch 9 - iter 123/416 - loss 0.10476506 - samples/sec: 339.68 - lr: 0.100000
2022-08-24 11:05:13,707 epoch 9 - iter 164/416 - loss 0.10424004 - samples/sec: 340.40 - lr: 0.100000
2022-08-24 11:05:17,615 epoch 9 - iter 205/416 - loss 0.10460388 - samples/sec: 336.08 - lr: 0.100000
2022-08-24 11:05:21,505 epoch 9 - iter 246/416 - loss 0.10580129 - samples/sec: 337.66 - lr: 0.100000
2022-08-24 11:05:25,352 epoch 9 - iter 287/416 - loss 0.10519137 - samples/sec: 341.45 - lr: 0.100000
2022-08-24 11:05:29,167 epoch 9 - iter 328/416 - loss 0.10576453 - samples/sec: 344.33 - lr: 0.100000
2022-08-24 11:05:33,091 epoch 9 - iter 369/416 - loss 0.10522369 - samples/sec: 334.70 - lr: 0.100000
2022-08-24 11:05:36,953 epoch 9 - iter 410/416 - loss 0.10499612 - samples/sec: 339.97 - lr: 0.100000
2022-08-24 11:05:37,541 ----------------------------------------------------------------------------------------------------
2022-08-24 11:05:37,542 EPOCH 9 done: loss 0.1052 - lr 0.100000
2022-08-24 11:05:44,490 Evaluating as a multi-label problem: False
2022-08-24 11:05:44,720 DEV : loss 0.07029640674591064 - f1-score (micro avg) 0.9719
2022-08-24 11:05:44,884 BAD EPOCHS (no improvement): 0
2022-08-24 11:05:44,886 saving best model
2022-08-24 11:05:48,465 ----------------------------------------------------------------------------------------------------
2022-08-24 11:05:52,355 epoch 10 - iter 41/416 - loss 0.10064182 - samples/sec: 337.69 - lr: 0.100000
2022-08-24 11:05:56,216 epoch 10 - iter 82/416 - loss 0.09818265 - samples/sec: 340.21 - lr: 0.100000
2022-08-24 11:06:00,337 epoch 10 - iter 123/416 - loss 0.09808745 - samples/sec: 318.69 - lr: 0.100000
2022-08-24 11:06:04,274 epoch 10 - iter 164/416 - loss 0.10072979 - samples/sec: 333.58 - lr: 0.100000
2022-08-24 11:06:08,146 epoch 10 - iter 205/416 - loss 0.09947450 - samples/sec: 339.18 - lr: 0.100000
2022-08-24 11:06:12,068 epoch 10 - iter 246/416 - loss 0.09907584 - samples/sec: 334.96 - lr: 0.100000
2022-08-24 11:06:16,056 epoch 10 - iter 287/416 - loss 0.09971183 - samples/sec: 329.34 - lr: 0.100000
2022-08-24 11:06:19,927 epoch 10 - iter 328/416 - loss 0.10065394 - samples/sec: 339.34 - lr: 0.100000
2022-08-24 11:06:23,749 epoch 10 - iter 369/416 - loss 0.10061685 - samples/sec: 343.62 - lr: 0.100000
2022-08-24 11:06:27,632 epoch 10 - iter 410/416 - loss 0.10070568 - samples/sec: 338.22 - lr: 0.100000
2022-08-24 11:06:28,178 ----------------------------------------------------------------------------------------------------
2022-08-24 11:06:28,182 EPOCH 10 done: loss 0.1005 - lr 0.100000
2022-08-24 11:06:35,003 Evaluating as a multi-label problem: False
2022-08-24 11:06:35,224 DEV : loss 0.07123567163944244 - f1-score (micro avg) 0.9716
2022-08-24 11:06:35,379 BAD EPOCHS (no improvement): 1
2022-08-24 11:06:39,298 ----------------------------------------------------------------------------------------------------
2022-08-24 11:06:39,299 loading file resources/taggers/best-model.pt
2022-08-24 11:06:42,251 SequenceTagger predicts: Dictionary with 34 tags: <unk>, PSP, NN, VM, NNP, SYM, VAUX, JJ, NNPC, PRP, CC, NNC, QC, NST, DEM, RP, QF, NEG, RB, QCC, QO, INTF, JJC, WQ, RDP, UNK, PRPC, NSTC, RBC, QFC, CCC, INJ, <START>, <STOP>
2022-08-24 11:06:49,127 Evaluating as a multi-label problem: False
2022-08-24 11:06:49,348 0.9709 0.9709 0.9709 0.9709
2022-08-24 11:06:49,349
Results:
- F-score (micro) 0.9709
- F-score (macro) 0.8174
- Accuracy 0.9709
By class:
precision recall f1-score support
PSP 0.9971 0.9975 0.9973 7182
NN 0.9710 0.9685 0.9697 7181
VM 0.9942 0.9923 0.9933 3643
NNP 0.9400 0.9076 0.9235 2846
SYM 1.0000 1.0000 1.0000 2420
VAUX 0.9928 0.9973 0.9950 2216
JJ 0.9409 0.9560 0.9484 1933
NNPC 0.8861 0.8938 0.8899 1592
PRP 0.9851 0.9829 0.9840 1348
CC 0.9892 0.9938 0.9915 1289
NNC 0.7871 0.8778 0.8300 851
QC 0.9866 0.9933 0.9899 593
NST 1.0000 0.9960 0.9980 500
RP 0.9958 0.9754 0.9855 487
DEM 0.9622 0.9935 0.9776 461
QF 0.9668 0.9357 0.9510 280
NEG 1.0000 1.0000 1.0000 190
RB 0.9677 0.8889 0.9266 135
QCC 0.9796 0.9697 0.9746 99
QO 0.9821 0.9649 0.9735 57
JJC 0.8846 0.4792 0.6216 48
INTF 0.7576 0.9615 0.8475 26
WQ 0.9524 0.9524 0.9524 21
RDP 0.8462 0.6875 0.7586 16
UNK 0.3333 0.4286 0.3750 7
PRPC 1.0000 0.5000 0.6667 4
NSTC 1.0000 1.0000 1.0000 2
RBC 0.0000 0.0000 0.0000 1
QFC 0.0000 0.0000 0.0000 1
CCC 0.0000 0.0000 0.0000 1
accuracy 0.9709 35430
macro avg 0.8366 0.8098 0.8174 35430
weighted avg 0.9713 0.9709 0.9709 35430
2022-08-24 11:06:49,349 ----------------------------------------------------------------------------------------------------
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