2023-09-04 13:39:01,977 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,978 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 768)
        (position_embeddings): Embedding(512, 768)
        (token_type_embeddings): Embedding(2, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-11): 12 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=768, out_features=768, bias=True)
                (key): Linear(in_features=768, out_features=768, bias=True)
                (value): Linear(in_features=768, out_features=768, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=768, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=3072, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,979 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
 - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,979 Train:  5901 sentences
2023-09-04 13:39:01,979         (train_with_dev=False, train_with_test=False)
2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,979 Training Params:
2023-09-04 13:39:01,979  - learning_rate: "5e-05" 
2023-09-04 13:39:01,979  - mini_batch_size: "8"
2023-09-04 13:39:01,979  - max_epochs: "10"
2023-09-04 13:39:01,979  - shuffle: "True"
2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,979 Plugins:
2023-09-04 13:39:01,979  - LinearScheduler | warmup_fraction: '0.1'
2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,980 Final evaluation on model from best epoch (best-model.pt)
2023-09-04 13:39:01,980  - metric: "('micro avg', 'f1-score')"
2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,980 Computation:
2023-09-04 13:39:01,980  - compute on device: cuda:0
2023-09-04 13:39:01,980  - embedding storage: none
2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,980 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
2023-09-04 13:39:14,291 epoch 1 - iter 73/738 - loss 2.44326612 - time (sec): 12.31 - samples/sec: 1238.74 - lr: 0.000005 - momentum: 0.000000
2023-09-04 13:39:27,922 epoch 1 - iter 146/738 - loss 1.47856637 - time (sec): 25.94 - samples/sec: 1258.14 - lr: 0.000010 - momentum: 0.000000
2023-09-04 13:39:40,565 epoch 1 - iter 219/738 - loss 1.14694216 - time (sec): 38.58 - samples/sec: 1236.59 - lr: 0.000015 - momentum: 0.000000
2023-09-04 13:39:54,257 epoch 1 - iter 292/738 - loss 0.93905184 - time (sec): 52.28 - samples/sec: 1224.86 - lr: 0.000020 - momentum: 0.000000
2023-09-04 13:40:08,310 epoch 1 - iter 365/738 - loss 0.80096618 - time (sec): 66.33 - samples/sec: 1223.15 - lr: 0.000025 - momentum: 0.000000
2023-09-04 13:40:22,630 epoch 1 - iter 438/738 - loss 0.70508476 - time (sec): 80.65 - samples/sec: 1216.96 - lr: 0.000030 - momentum: 0.000000
2023-09-04 13:40:38,180 epoch 1 - iter 511/738 - loss 0.63104936 - time (sec): 96.20 - samples/sec: 1203.40 - lr: 0.000035 - momentum: 0.000000
2023-09-04 13:40:51,146 epoch 1 - iter 584/738 - loss 0.58093276 - time (sec): 109.16 - samples/sec: 1203.18 - lr: 0.000039 - momentum: 0.000000
2023-09-04 13:41:07,323 epoch 1 - iter 657/738 - loss 0.53122241 - time (sec): 125.34 - samples/sec: 1190.77 - lr: 0.000044 - momentum: 0.000000
2023-09-04 13:41:20,266 epoch 1 - iter 730/738 - loss 0.49879954 - time (sec): 138.28 - samples/sec: 1190.28 - lr: 0.000049 - momentum: 0.000000
2023-09-04 13:41:22,063 ----------------------------------------------------------------------------------------------------
2023-09-04 13:41:22,064 EPOCH 1 done: loss 0.4968 - lr: 0.000049
2023-09-04 13:41:36,135 DEV : loss 0.14711907505989075 - f1-score (micro avg)  0.6766
2023-09-04 13:41:36,163 saving best model
2023-09-04 13:41:36,647 ----------------------------------------------------------------------------------------------------
2023-09-04 13:41:50,044 epoch 2 - iter 73/738 - loss 0.13120650 - time (sec): 13.40 - samples/sec: 1225.66 - lr: 0.000049 - momentum: 0.000000
2023-09-04 13:42:04,790 epoch 2 - iter 146/738 - loss 0.13558307 - time (sec): 28.14 - samples/sec: 1188.56 - lr: 0.000049 - momentum: 0.000000
2023-09-04 13:42:20,969 epoch 2 - iter 219/738 - loss 0.13389094 - time (sec): 44.32 - samples/sec: 1169.25 - lr: 0.000048 - momentum: 0.000000
2023-09-04 13:42:33,703 epoch 2 - iter 292/738 - loss 0.13115667 - time (sec): 57.05 - samples/sec: 1179.88 - lr: 0.000048 - momentum: 0.000000
2023-09-04 13:42:46,226 epoch 2 - iter 365/738 - loss 0.13074652 - time (sec): 69.58 - samples/sec: 1192.30 - lr: 0.000047 - momentum: 0.000000
2023-09-04 13:43:02,142 epoch 2 - iter 438/738 - loss 0.12915508 - time (sec): 85.49 - samples/sec: 1181.98 - lr: 0.000047 - momentum: 0.000000
2023-09-04 13:43:15,964 epoch 2 - iter 511/738 - loss 0.12777004 - time (sec): 99.32 - samples/sec: 1182.98 - lr: 0.000046 - momentum: 0.000000
2023-09-04 13:43:29,576 epoch 2 - iter 584/738 - loss 0.12429028 - time (sec): 112.93 - samples/sec: 1180.99 - lr: 0.000046 - momentum: 0.000000
2023-09-04 13:43:42,613 epoch 2 - iter 657/738 - loss 0.12318573 - time (sec): 125.96 - samples/sec: 1183.61 - lr: 0.000045 - momentum: 0.000000
2023-09-04 13:43:55,550 epoch 2 - iter 730/738 - loss 0.12152834 - time (sec): 138.90 - samples/sec: 1188.39 - lr: 0.000045 - momentum: 0.000000
2023-09-04 13:43:56,718 ----------------------------------------------------------------------------------------------------
2023-09-04 13:43:56,718 EPOCH 2 done: loss 0.1220 - lr: 0.000045
2023-09-04 13:44:14,588 DEV : loss 0.1313476413488388 - f1-score (micro avg)  0.7638
2023-09-04 13:44:14,617 saving best model
2023-09-04 13:44:15,947 ----------------------------------------------------------------------------------------------------
2023-09-04 13:44:28,284 epoch 3 - iter 73/738 - loss 0.05671349 - time (sec): 12.34 - samples/sec: 1230.07 - lr: 0.000044 - momentum: 0.000000
2023-09-04 13:44:41,649 epoch 3 - iter 146/738 - loss 0.06884812 - time (sec): 25.70 - samples/sec: 1238.47 - lr: 0.000043 - momentum: 0.000000
2023-09-04 13:44:55,404 epoch 3 - iter 219/738 - loss 0.07228850 - time (sec): 39.46 - samples/sec: 1222.20 - lr: 0.000043 - momentum: 0.000000
2023-09-04 13:45:11,524 epoch 3 - iter 292/738 - loss 0.06995531 - time (sec): 55.58 - samples/sec: 1213.70 - lr: 0.000042 - momentum: 0.000000
2023-09-04 13:45:26,135 epoch 3 - iter 365/738 - loss 0.07259794 - time (sec): 70.19 - samples/sec: 1194.49 - lr: 0.000042 - momentum: 0.000000
2023-09-04 13:45:38,808 epoch 3 - iter 438/738 - loss 0.07407768 - time (sec): 82.86 - samples/sec: 1200.50 - lr: 0.000041 - momentum: 0.000000
2023-09-04 13:45:53,814 epoch 3 - iter 511/738 - loss 0.07541008 - time (sec): 97.87 - samples/sec: 1193.07 - lr: 0.000041 - momentum: 0.000000
2023-09-04 13:46:06,906 epoch 3 - iter 584/738 - loss 0.07337803 - time (sec): 110.96 - samples/sec: 1195.41 - lr: 0.000040 - momentum: 0.000000
2023-09-04 13:46:20,624 epoch 3 - iter 657/738 - loss 0.07572131 - time (sec): 124.67 - samples/sec: 1194.47 - lr: 0.000040 - momentum: 0.000000
2023-09-04 13:46:34,123 epoch 3 - iter 730/738 - loss 0.07457011 - time (sec): 138.17 - samples/sec: 1191.28 - lr: 0.000039 - momentum: 0.000000
2023-09-04 13:46:35,620 ----------------------------------------------------------------------------------------------------
2023-09-04 13:46:35,620 EPOCH 3 done: loss 0.0744 - lr: 0.000039
2023-09-04 13:46:53,398 DEV : loss 0.13625338673591614 - f1-score (micro avg)  0.7869
2023-09-04 13:46:53,426 saving best model
2023-09-04 13:46:54,791 ----------------------------------------------------------------------------------------------------
2023-09-04 13:47:09,408 epoch 4 - iter 73/738 - loss 0.04743530 - time (sec): 14.61 - samples/sec: 1182.99 - lr: 0.000038 - momentum: 0.000000
2023-09-04 13:47:22,832 epoch 4 - iter 146/738 - loss 0.04786156 - time (sec): 28.04 - samples/sec: 1199.93 - lr: 0.000038 - momentum: 0.000000
2023-09-04 13:47:37,098 epoch 4 - iter 219/738 - loss 0.04448843 - time (sec): 42.31 - samples/sec: 1201.29 - lr: 0.000037 - momentum: 0.000000
2023-09-04 13:47:50,064 epoch 4 - iter 292/738 - loss 0.04481142 - time (sec): 55.27 - samples/sec: 1205.00 - lr: 0.000037 - momentum: 0.000000
2023-09-04 13:48:01,974 epoch 4 - iter 365/738 - loss 0.04460981 - time (sec): 67.18 - samples/sec: 1213.70 - lr: 0.000036 - momentum: 0.000000
2023-09-04 13:48:16,980 epoch 4 - iter 438/738 - loss 0.04525369 - time (sec): 82.19 - samples/sec: 1200.51 - lr: 0.000036 - momentum: 0.000000
2023-09-04 13:48:33,254 epoch 4 - iter 511/738 - loss 0.04819381 - time (sec): 98.46 - samples/sec: 1193.80 - lr: 0.000035 - momentum: 0.000000
2023-09-04 13:48:45,973 epoch 4 - iter 584/738 - loss 0.04935949 - time (sec): 111.18 - samples/sec: 1193.27 - lr: 0.000035 - momentum: 0.000000
2023-09-04 13:48:59,423 epoch 4 - iter 657/738 - loss 0.04824922 - time (sec): 124.63 - samples/sec: 1190.56 - lr: 0.000034 - momentum: 0.000000
2023-09-04 13:49:12,677 epoch 4 - iter 730/738 - loss 0.04996471 - time (sec): 137.88 - samples/sec: 1194.19 - lr: 0.000033 - momentum: 0.000000
2023-09-04 13:49:14,385 ----------------------------------------------------------------------------------------------------
2023-09-04 13:49:14,386 EPOCH 4 done: loss 0.0497 - lr: 0.000033
2023-09-04 13:49:32,177 DEV : loss 0.14975322782993317 - f1-score (micro avg)  0.8032
2023-09-04 13:49:32,205 saving best model
2023-09-04 13:49:33,547 ----------------------------------------------------------------------------------------------------
2023-09-04 13:49:48,290 epoch 5 - iter 73/738 - loss 0.04353647 - time (sec): 14.74 - samples/sec: 1227.44 - lr: 0.000033 - momentum: 0.000000
2023-09-04 13:50:01,528 epoch 5 - iter 146/738 - loss 0.03590759 - time (sec): 27.98 - samples/sec: 1215.28 - lr: 0.000032 - momentum: 0.000000
2023-09-04 13:50:16,811 epoch 5 - iter 219/738 - loss 0.03904111 - time (sec): 43.26 - samples/sec: 1187.85 - lr: 0.000032 - momentum: 0.000000
2023-09-04 13:50:29,758 epoch 5 - iter 292/738 - loss 0.03738277 - time (sec): 56.21 - samples/sec: 1187.90 - lr: 0.000031 - momentum: 0.000000
2023-09-04 13:50:42,348 epoch 5 - iter 365/738 - loss 0.03701919 - time (sec): 68.80 - samples/sec: 1195.49 - lr: 0.000031 - momentum: 0.000000
2023-09-04 13:50:55,788 epoch 5 - iter 438/738 - loss 0.03547966 - time (sec): 82.24 - samples/sec: 1188.77 - lr: 0.000030 - momentum: 0.000000
2023-09-04 13:51:10,184 epoch 5 - iter 511/738 - loss 0.03745342 - time (sec): 96.64 - samples/sec: 1182.32 - lr: 0.000030 - momentum: 0.000000
2023-09-04 13:51:23,925 epoch 5 - iter 584/738 - loss 0.03628508 - time (sec): 110.38 - samples/sec: 1177.95 - lr: 0.000029 - momentum: 0.000000
2023-09-04 13:51:36,527 epoch 5 - iter 657/738 - loss 0.03647575 - time (sec): 122.98 - samples/sec: 1183.22 - lr: 0.000028 - momentum: 0.000000
2023-09-04 13:51:52,693 epoch 5 - iter 730/738 - loss 0.03683964 - time (sec): 139.14 - samples/sec: 1183.49 - lr: 0.000028 - momentum: 0.000000
2023-09-04 13:51:54,084 ----------------------------------------------------------------------------------------------------
2023-09-04 13:51:54,084 EPOCH 5 done: loss 0.0370 - lr: 0.000028
2023-09-04 13:52:11,957 DEV : loss 0.19077804684638977 - f1-score (micro avg)  0.7933
2023-09-04 13:52:11,986 ----------------------------------------------------------------------------------------------------
2023-09-04 13:52:27,594 epoch 6 - iter 73/738 - loss 0.02834930 - time (sec): 15.61 - samples/sec: 1157.16 - lr: 0.000027 - momentum: 0.000000
2023-09-04 13:52:42,613 epoch 6 - iter 146/738 - loss 0.02133025 - time (sec): 30.63 - samples/sec: 1156.70 - lr: 0.000027 - momentum: 0.000000
2023-09-04 13:52:58,195 epoch 6 - iter 219/738 - loss 0.02769820 - time (sec): 46.21 - samples/sec: 1163.21 - lr: 0.000026 - momentum: 0.000000
2023-09-04 13:53:11,283 epoch 6 - iter 292/738 - loss 0.02666574 - time (sec): 59.30 - samples/sec: 1175.44 - lr: 0.000026 - momentum: 0.000000
2023-09-04 13:53:24,476 epoch 6 - iter 365/738 - loss 0.02534577 - time (sec): 72.49 - samples/sec: 1181.85 - lr: 0.000025 - momentum: 0.000000
2023-09-04 13:53:38,741 epoch 6 - iter 438/738 - loss 0.02624624 - time (sec): 86.75 - samples/sec: 1175.51 - lr: 0.000025 - momentum: 0.000000
2023-09-04 13:53:52,542 epoch 6 - iter 511/738 - loss 0.02536390 - time (sec): 100.56 - samples/sec: 1175.88 - lr: 0.000024 - momentum: 0.000000
2023-09-04 13:54:05,072 epoch 6 - iter 584/738 - loss 0.02502495 - time (sec): 113.09 - samples/sec: 1178.87 - lr: 0.000023 - momentum: 0.000000
2023-09-04 13:54:17,863 epoch 6 - iter 657/738 - loss 0.02611623 - time (sec): 125.88 - samples/sec: 1181.91 - lr: 0.000023 - momentum: 0.000000
2023-09-04 13:54:30,487 epoch 6 - iter 730/738 - loss 0.02596158 - time (sec): 138.50 - samples/sec: 1187.68 - lr: 0.000022 - momentum: 0.000000
2023-09-04 13:54:31,774 ----------------------------------------------------------------------------------------------------
2023-09-04 13:54:31,774 EPOCH 6 done: loss 0.0258 - lr: 0.000022
2023-09-04 13:54:49,616 DEV : loss 0.19920460879802704 - f1-score (micro avg)  0.8066
2023-09-04 13:54:49,656 saving best model
2023-09-04 13:54:51,032 ----------------------------------------------------------------------------------------------------
2023-09-04 13:55:02,883 epoch 7 - iter 73/738 - loss 0.00733074 - time (sec): 11.85 - samples/sec: 1297.71 - lr: 0.000022 - momentum: 0.000000
2023-09-04 13:55:18,108 epoch 7 - iter 146/738 - loss 0.01089914 - time (sec): 27.07 - samples/sec: 1219.12 - lr: 0.000021 - momentum: 0.000000
2023-09-04 13:55:29,575 epoch 7 - iter 219/738 - loss 0.01512421 - time (sec): 38.54 - samples/sec: 1240.06 - lr: 0.000021 - momentum: 0.000000
2023-09-04 13:55:45,104 epoch 7 - iter 292/738 - loss 0.01728633 - time (sec): 54.07 - samples/sec: 1205.68 - lr: 0.000020 - momentum: 0.000000
2023-09-04 13:55:57,489 epoch 7 - iter 365/738 - loss 0.01658937 - time (sec): 66.46 - samples/sec: 1214.60 - lr: 0.000020 - momentum: 0.000000
2023-09-04 13:56:09,788 epoch 7 - iter 438/738 - loss 0.01645276 - time (sec): 78.75 - samples/sec: 1217.15 - lr: 0.000019 - momentum: 0.000000
2023-09-04 13:56:27,505 epoch 7 - iter 511/738 - loss 0.01627472 - time (sec): 96.47 - samples/sec: 1198.84 - lr: 0.000018 - momentum: 0.000000
2023-09-04 13:56:42,146 epoch 7 - iter 584/738 - loss 0.01756879 - time (sec): 111.11 - samples/sec: 1195.67 - lr: 0.000018 - momentum: 0.000000
2023-09-04 13:56:56,291 epoch 7 - iter 657/738 - loss 0.01829967 - time (sec): 125.26 - samples/sec: 1192.94 - lr: 0.000017 - momentum: 0.000000
2023-09-04 13:57:09,157 epoch 7 - iter 730/738 - loss 0.01839564 - time (sec): 138.12 - samples/sec: 1194.13 - lr: 0.000017 - momentum: 0.000000
2023-09-04 13:57:10,334 ----------------------------------------------------------------------------------------------------
2023-09-04 13:57:10,334 EPOCH 7 done: loss 0.0184 - lr: 0.000017
2023-09-04 13:57:27,976 DEV : loss 0.20733937621116638 - f1-score (micro avg)  0.819
2023-09-04 13:57:28,004 saving best model
2023-09-04 13:57:29,349 ----------------------------------------------------------------------------------------------------
2023-09-04 13:57:42,968 epoch 8 - iter 73/738 - loss 0.00755884 - time (sec): 13.62 - samples/sec: 1232.40 - lr: 0.000016 - momentum: 0.000000
2023-09-04 13:57:57,518 epoch 8 - iter 146/738 - loss 0.01094801 - time (sec): 28.17 - samples/sec: 1215.25 - lr: 0.000016 - momentum: 0.000000
2023-09-04 13:58:12,532 epoch 8 - iter 219/738 - loss 0.01345009 - time (sec): 43.18 - samples/sec: 1212.92 - lr: 0.000015 - momentum: 0.000000
2023-09-04 13:58:27,267 epoch 8 - iter 292/738 - loss 0.01377918 - time (sec): 57.92 - samples/sec: 1193.53 - lr: 0.000015 - momentum: 0.000000
2023-09-04 13:58:41,071 epoch 8 - iter 365/738 - loss 0.01339208 - time (sec): 71.72 - samples/sec: 1191.09 - lr: 0.000014 - momentum: 0.000000
2023-09-04 13:58:55,299 epoch 8 - iter 438/738 - loss 0.01203734 - time (sec): 85.95 - samples/sec: 1191.47 - lr: 0.000013 - momentum: 0.000000
2023-09-04 13:59:07,948 epoch 8 - iter 511/738 - loss 0.01212847 - time (sec): 98.60 - samples/sec: 1198.06 - lr: 0.000013 - momentum: 0.000000
2023-09-04 13:59:20,292 epoch 8 - iter 584/738 - loss 0.01169538 - time (sec): 110.94 - samples/sec: 1195.53 - lr: 0.000012 - momentum: 0.000000
2023-09-04 13:59:32,502 epoch 8 - iter 657/738 - loss 0.01207296 - time (sec): 123.15 - samples/sec: 1201.39 - lr: 0.000012 - momentum: 0.000000
2023-09-04 13:59:46,166 epoch 8 - iter 730/738 - loss 0.01207065 - time (sec): 136.82 - samples/sec: 1201.05 - lr: 0.000011 - momentum: 0.000000
2023-09-04 13:59:48,061 ----------------------------------------------------------------------------------------------------
2023-09-04 13:59:48,062 EPOCH 8 done: loss 0.0121 - lr: 0.000011
2023-09-04 14:00:05,760 DEV : loss 0.22424915432929993 - f1-score (micro avg)  0.8211
2023-09-04 14:00:05,789 saving best model
2023-09-04 14:00:07,330 ----------------------------------------------------------------------------------------------------
2023-09-04 14:00:23,426 epoch 9 - iter 73/738 - loss 0.00950458 - time (sec): 16.09 - samples/sec: 1109.60 - lr: 0.000011 - momentum: 0.000000
2023-09-04 14:00:37,770 epoch 9 - iter 146/738 - loss 0.00586520 - time (sec): 30.44 - samples/sec: 1144.33 - lr: 0.000010 - momentum: 0.000000
2023-09-04 14:00:50,878 epoch 9 - iter 219/738 - loss 0.00633319 - time (sec): 43.55 - samples/sec: 1154.87 - lr: 0.000010 - momentum: 0.000000
2023-09-04 14:01:04,305 epoch 9 - iter 292/738 - loss 0.00635097 - time (sec): 56.97 - samples/sec: 1170.47 - lr: 0.000009 - momentum: 0.000000
2023-09-04 14:01:16,152 epoch 9 - iter 365/738 - loss 0.00833242 - time (sec): 68.82 - samples/sec: 1179.97 - lr: 0.000008 - momentum: 0.000000
2023-09-04 14:01:30,812 epoch 9 - iter 438/738 - loss 0.00844827 - time (sec): 83.48 - samples/sec: 1175.24 - lr: 0.000008 - momentum: 0.000000
2023-09-04 14:01:45,571 epoch 9 - iter 511/738 - loss 0.00906392 - time (sec): 98.24 - samples/sec: 1179.60 - lr: 0.000007 - momentum: 0.000000
2023-09-04 14:01:58,091 epoch 9 - iter 584/738 - loss 0.00900934 - time (sec): 110.76 - samples/sec: 1192.16 - lr: 0.000007 - momentum: 0.000000
2023-09-04 14:02:11,172 epoch 9 - iter 657/738 - loss 0.00922364 - time (sec): 123.84 - samples/sec: 1197.24 - lr: 0.000006 - momentum: 0.000000
2023-09-04 14:02:25,334 epoch 9 - iter 730/738 - loss 0.00885846 - time (sec): 138.00 - samples/sec: 1192.42 - lr: 0.000006 - momentum: 0.000000
2023-09-04 14:02:27,068 ----------------------------------------------------------------------------------------------------
2023-09-04 14:02:27,069 EPOCH 9 done: loss 0.0089 - lr: 0.000006
2023-09-04 14:02:44,747 DEV : loss 0.23065683245658875 - f1-score (micro avg)  0.8222
2023-09-04 14:02:44,776 saving best model
2023-09-04 14:02:46,116 ----------------------------------------------------------------------------------------------------
2023-09-04 14:03:01,144 epoch 10 - iter 73/738 - loss 0.00620805 - time (sec): 15.03 - samples/sec: 1192.24 - lr: 0.000005 - momentum: 0.000000
2023-09-04 14:03:13,444 epoch 10 - iter 146/738 - loss 0.00387357 - time (sec): 27.33 - samples/sec: 1204.29 - lr: 0.000004 - momentum: 0.000000
2023-09-04 14:03:28,017 epoch 10 - iter 219/738 - loss 0.00355136 - time (sec): 41.90 - samples/sec: 1191.72 - lr: 0.000004 - momentum: 0.000000
2023-09-04 14:03:41,576 epoch 10 - iter 292/738 - loss 0.00333989 - time (sec): 55.46 - samples/sec: 1177.08 - lr: 0.000003 - momentum: 0.000000
2023-09-04 14:03:56,887 epoch 10 - iter 365/738 - loss 0.00509374 - time (sec): 70.77 - samples/sec: 1186.52 - lr: 0.000003 - momentum: 0.000000
2023-09-04 14:04:10,482 epoch 10 - iter 438/738 - loss 0.00479139 - time (sec): 84.36 - samples/sec: 1186.07 - lr: 0.000002 - momentum: 0.000000
2023-09-04 14:04:24,233 epoch 10 - iter 511/738 - loss 0.00497244 - time (sec): 98.11 - samples/sec: 1188.17 - lr: 0.000002 - momentum: 0.000000
2023-09-04 14:04:37,794 epoch 10 - iter 584/738 - loss 0.00546920 - time (sec): 111.68 - samples/sec: 1193.67 - lr: 0.000001 - momentum: 0.000000
2023-09-04 14:04:50,405 epoch 10 - iter 657/738 - loss 0.00520653 - time (sec): 124.29 - samples/sec: 1199.38 - lr: 0.000001 - momentum: 0.000000
2023-09-04 14:05:05,287 epoch 10 - iter 730/738 - loss 0.00520739 - time (sec): 139.17 - samples/sec: 1186.35 - lr: 0.000000 - momentum: 0.000000
2023-09-04 14:05:06,400 ----------------------------------------------------------------------------------------------------
2023-09-04 14:05:06,400 EPOCH 10 done: loss 0.0052 - lr: 0.000000
2023-09-04 14:05:24,012 DEV : loss 0.231268510222435 - f1-score (micro avg)  0.8163
2023-09-04 14:05:24,519 ----------------------------------------------------------------------------------------------------
2023-09-04 14:05:24,520 Loading model from best epoch ...
2023-09-04 14:05:26,338 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod
2023-09-04 14:05:41,002 
Results:
- F-score (micro) 0.8057
- F-score (macro) 0.715
- Accuracy 0.6981

By class:
              precision    recall  f1-score   support

         loc     0.8461    0.8904    0.8677       858
        pers     0.7628    0.8026    0.7822       537
         org     0.6441    0.5758    0.6080       132
        time     0.5574    0.6296    0.5913        54
        prod     0.7885    0.6721    0.7257        61

   micro avg     0.7922    0.8197    0.8057      1642
   macro avg     0.7198    0.7141    0.7150      1642
weighted avg     0.7910    0.8197    0.8045      1642

2023-09-04 14:05:41,002 ----------------------------------------------------------------------------------------------------