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flair-icdar-fr

Token Classification
Flair
PyTorch
TensorBoard
French
sequence-tagger-model
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flair-icdar-fr
File size: 24,187 Bytes 
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2023-10-18 21:25:18,822 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,823 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 128)
        (position_embeddings): Embedding(512, 128)
        (token_type_embeddings): Embedding(2, 128)
        (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-1): 2 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=128, out_features=128, bias=True)
                (key): Linear(in_features=128, out_features=128, bias=True)
                (value): Linear(in_features=128, out_features=128, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=128, out_features=128, bias=True)
                (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=128, out_features=512, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=512, out_features=128, bias=True)
              (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=128, out_features=128, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=128, out_features=13, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-18 21:25:18,823 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,823 MultiCorpus: 7936 train + 992 dev + 992 test sentences
 - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
2023-10-18 21:25:18,823 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,823 Train:  7936 sentences
2023-10-18 21:25:18,823         (train_with_dev=False, train_with_test=False)
2023-10-18 21:25:18,823 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,823 Training Params:
2023-10-18 21:25:18,823  - learning_rate: "5e-05" 
2023-10-18 21:25:18,823  - mini_batch_size: "4"
2023-10-18 21:25:18,823  - max_epochs: "10"
2023-10-18 21:25:18,823  - shuffle: "True"
2023-10-18 21:25:18,823 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,823 Plugins:
2023-10-18 21:25:18,823  - TensorboardLogger
2023-10-18 21:25:18,823  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 21:25:18,823 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,823 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 21:25:18,823  - metric: "('micro avg', 'f1-score')"
2023-10-18 21:25:18,823 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,823 Computation:
2023-10-18 21:25:18,824  - compute on device: cuda:0
2023-10-18 21:25:18,824  - embedding storage: none
2023-10-18 21:25:18,824 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,824 Model training base path: "hmbench-icdar/fr-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-18 21:25:18,824 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,824 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:18,824 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 21:25:21,871 epoch 1 - iter 198/1984 - loss 3.05001127 - time (sec): 3.05 - samples/sec: 5386.70 - lr: 0.000005 - momentum: 0.000000
2023-10-18 21:25:24,628 epoch 1 - iter 396/1984 - loss 2.47724953 - time (sec): 5.80 - samples/sec: 5922.14 - lr: 0.000010 - momentum: 0.000000
2023-10-18 21:25:27,667 epoch 1 - iter 594/1984 - loss 1.93619435 - time (sec): 8.84 - samples/sec: 5709.43 - lr: 0.000015 - momentum: 0.000000
2023-10-18 21:25:30,706 epoch 1 - iter 792/1984 - loss 1.58401951 - time (sec): 11.88 - samples/sec: 5618.92 - lr: 0.000020 - momentum: 0.000000
2023-10-18 21:25:33,758 epoch 1 - iter 990/1984 - loss 1.35401175 - time (sec): 14.93 - samples/sec: 5563.79 - lr: 0.000025 - momentum: 0.000000
2023-10-18 21:25:36,794 epoch 1 - iter 1188/1984 - loss 1.20410373 - time (sec): 17.97 - samples/sec: 5524.66 - lr: 0.000030 - momentum: 0.000000
2023-10-18 21:25:39,832 epoch 1 - iter 1386/1984 - loss 1.09303606 - time (sec): 21.01 - samples/sec: 5470.81 - lr: 0.000035 - momentum: 0.000000
2023-10-18 21:25:42,910 epoch 1 - iter 1584/1984 - loss 0.99807468 - time (sec): 24.09 - samples/sec: 5451.75 - lr: 0.000040 - momentum: 0.000000
2023-10-18 21:25:45,923 epoch 1 - iter 1782/1984 - loss 0.92290600 - time (sec): 27.10 - samples/sec: 5442.29 - lr: 0.000045 - momentum: 0.000000
2023-10-18 21:25:49,008 epoch 1 - iter 1980/1984 - loss 0.86043295 - time (sec): 30.18 - samples/sec: 5425.81 - lr: 0.000050 - momentum: 0.000000
2023-10-18 21:25:49,068 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:49,068 EPOCH 1 done: loss 0.8594 - lr: 0.000050
2023-10-18 21:25:50,560 DEV : loss 0.21192067861557007 - f1-score (micro avg)  0.2945
2023-10-18 21:25:50,578 saving best model
2023-10-18 21:25:50,613 ----------------------------------------------------------------------------------------------------
2023-10-18 21:25:53,727 epoch 2 - iter 198/1984 - loss 0.30950366 - time (sec): 3.11 - samples/sec: 4927.98 - lr: 0.000049 - momentum: 0.000000
2023-10-18 21:25:56,796 epoch 2 - iter 396/1984 - loss 0.28151013 - time (sec): 6.18 - samples/sec: 5145.74 - lr: 0.000049 - momentum: 0.000000
2023-10-18 21:25:59,865 epoch 2 - iter 594/1984 - loss 0.28431083 - time (sec): 9.25 - samples/sec: 5342.97 - lr: 0.000048 - momentum: 0.000000
2023-10-18 21:26:02,940 epoch 2 - iter 792/1984 - loss 0.27867074 - time (sec): 12.33 - samples/sec: 5398.12 - lr: 0.000048 - momentum: 0.000000
2023-10-18 21:26:06,023 epoch 2 - iter 990/1984 - loss 0.27356192 - time (sec): 15.41 - samples/sec: 5362.82 - lr: 0.000047 - momentum: 0.000000
2023-10-18 21:26:09,040 epoch 2 - iter 1188/1984 - loss 0.26808423 - time (sec): 18.43 - samples/sec: 5380.47 - lr: 0.000047 - momentum: 0.000000
2023-10-18 21:26:11,822 epoch 2 - iter 1386/1984 - loss 0.26113663 - time (sec): 21.21 - samples/sec: 5451.69 - lr: 0.000046 - momentum: 0.000000
2023-10-18 21:26:14,900 epoch 2 - iter 1584/1984 - loss 0.25788671 - time (sec): 24.29 - samples/sec: 5438.15 - lr: 0.000046 - momentum: 0.000000
2023-10-18 21:26:17,947 epoch 2 - iter 1782/1984 - loss 0.25525919 - time (sec): 27.33 - samples/sec: 5421.86 - lr: 0.000045 - momentum: 0.000000
2023-10-18 21:26:20,762 epoch 2 - iter 1980/1984 - loss 0.25444187 - time (sec): 30.15 - samples/sec: 5429.33 - lr: 0.000044 - momentum: 0.000000
2023-10-18 21:26:20,822 ----------------------------------------------------------------------------------------------------
2023-10-18 21:26:20,822 EPOCH 2 done: loss 0.2546 - lr: 0.000044
2023-10-18 21:26:22,634 DEV : loss 0.16650356352329254 - f1-score (micro avg)  0.4051
2023-10-18 21:26:22,654 saving best model
2023-10-18 21:26:22,690 ----------------------------------------------------------------------------------------------------
2023-10-18 21:26:25,787 epoch 3 - iter 198/1984 - loss 0.21161120 - time (sec): 3.10 - samples/sec: 5483.96 - lr: 0.000044 - momentum: 0.000000
2023-10-18 21:26:28,837 epoch 3 - iter 396/1984 - loss 0.20257772 - time (sec): 6.15 - samples/sec: 5328.22 - lr: 0.000043 - momentum: 0.000000
2023-10-18 21:26:31,767 epoch 3 - iter 594/1984 - loss 0.21363359 - time (sec): 9.08 - samples/sec: 5582.96 - lr: 0.000043 - momentum: 0.000000
2023-10-18 21:26:34,842 epoch 3 - iter 792/1984 - loss 0.20789207 - time (sec): 12.15 - samples/sec: 5470.71 - lr: 0.000042 - momentum: 0.000000
2023-10-18 21:26:37,879 epoch 3 - iter 990/1984 - loss 0.21055604 - time (sec): 15.19 - samples/sec: 5444.07 - lr: 0.000042 - momentum: 0.000000
2023-10-18 21:26:40,954 epoch 3 - iter 1188/1984 - loss 0.20723027 - time (sec): 18.26 - samples/sec: 5444.07 - lr: 0.000041 - momentum: 0.000000
2023-10-18 21:26:44,031 epoch 3 - iter 1386/1984 - loss 0.20657276 - time (sec): 21.34 - samples/sec: 5424.20 - lr: 0.000041 - momentum: 0.000000
2023-10-18 21:26:47,055 epoch 3 - iter 1584/1984 - loss 0.20547288 - time (sec): 24.36 - samples/sec: 5434.88 - lr: 0.000040 - momentum: 0.000000
2023-10-18 21:26:50,070 epoch 3 - iter 1782/1984 - loss 0.20632094 - time (sec): 27.38 - samples/sec: 5413.63 - lr: 0.000039 - momentum: 0.000000
2023-10-18 21:26:53,106 epoch 3 - iter 1980/1984 - loss 0.20495296 - time (sec): 30.41 - samples/sec: 5376.32 - lr: 0.000039 - momentum: 0.000000
2023-10-18 21:26:53,176 ----------------------------------------------------------------------------------------------------
2023-10-18 21:26:53,176 EPOCH 3 done: loss 0.2046 - lr: 0.000039
2023-10-18 21:26:55,394 DEV : loss 0.15022146701812744 - f1-score (micro avg)  0.5321
2023-10-18 21:26:55,413 saving best model
2023-10-18 21:26:55,447 ----------------------------------------------------------------------------------------------------
2023-10-18 21:26:58,566 epoch 4 - iter 198/1984 - loss 0.18075544 - time (sec): 3.12 - samples/sec: 5291.79 - lr: 0.000038 - momentum: 0.000000
2023-10-18 21:27:01,634 epoch 4 - iter 396/1984 - loss 0.18240152 - time (sec): 6.19 - samples/sec: 5143.93 - lr: 0.000038 - momentum: 0.000000
2023-10-18 21:27:04,650 epoch 4 - iter 594/1984 - loss 0.17441944 - time (sec): 9.20 - samples/sec: 5216.19 - lr: 0.000037 - momentum: 0.000000
2023-10-18 21:27:07,674 epoch 4 - iter 792/1984 - loss 0.17622746 - time (sec): 12.23 - samples/sec: 5214.64 - lr: 0.000037 - momentum: 0.000000
2023-10-18 21:27:10,762 epoch 4 - iter 990/1984 - loss 0.17870370 - time (sec): 15.31 - samples/sec: 5266.42 - lr: 0.000036 - momentum: 0.000000
2023-10-18 21:27:13,782 epoch 4 - iter 1188/1984 - loss 0.17730408 - time (sec): 18.33 - samples/sec: 5322.13 - lr: 0.000036 - momentum: 0.000000
2023-10-18 21:27:16,890 epoch 4 - iter 1386/1984 - loss 0.17390393 - time (sec): 21.44 - samples/sec: 5403.97 - lr: 0.000035 - momentum: 0.000000
2023-10-18 21:27:20,088 epoch 4 - iter 1584/1984 - loss 0.17431675 - time (sec): 24.64 - samples/sec: 5366.83 - lr: 0.000034 - momentum: 0.000000
2023-10-18 21:27:23,148 epoch 4 - iter 1782/1984 - loss 0.17801776 - time (sec): 27.70 - samples/sec: 5333.04 - lr: 0.000034 - momentum: 0.000000
2023-10-18 21:27:26,231 epoch 4 - iter 1980/1984 - loss 0.17957717 - time (sec): 30.78 - samples/sec: 5317.52 - lr: 0.000033 - momentum: 0.000000
2023-10-18 21:27:26,291 ----------------------------------------------------------------------------------------------------
2023-10-18 21:27:26,291 EPOCH 4 done: loss 0.1797 - lr: 0.000033
2023-10-18 21:27:28,115 DEV : loss 0.14466138184070587 - f1-score (micro avg)  0.5683
2023-10-18 21:27:28,133 saving best model
2023-10-18 21:27:28,167 ----------------------------------------------------------------------------------------------------
2023-10-18 21:27:31,281 epoch 5 - iter 198/1984 - loss 0.18093443 - time (sec): 3.11 - samples/sec: 5540.53 - lr: 0.000033 - momentum: 0.000000
2023-10-18 21:27:34,301 epoch 5 - iter 396/1984 - loss 0.17976008 - time (sec): 6.13 - samples/sec: 5491.12 - lr: 0.000032 - momentum: 0.000000
2023-10-18 21:27:37,345 epoch 5 - iter 594/1984 - loss 0.17027389 - time (sec): 9.18 - samples/sec: 5444.29 - lr: 0.000032 - momentum: 0.000000
2023-10-18 21:27:40,347 epoch 5 - iter 792/1984 - loss 0.16509799 - time (sec): 12.18 - samples/sec: 5422.24 - lr: 0.000031 - momentum: 0.000000
2023-10-18 21:27:43,351 epoch 5 - iter 990/1984 - loss 0.16344421 - time (sec): 15.18 - samples/sec: 5364.28 - lr: 0.000031 - momentum: 0.000000
2023-10-18 21:27:46,391 epoch 5 - iter 1188/1984 - loss 0.16463321 - time (sec): 18.22 - samples/sec: 5341.67 - lr: 0.000030 - momentum: 0.000000
2023-10-18 21:27:49,456 epoch 5 - iter 1386/1984 - loss 0.16486788 - time (sec): 21.29 - samples/sec: 5405.39 - lr: 0.000029 - momentum: 0.000000
2023-10-18 21:27:52,493 epoch 5 - iter 1584/1984 - loss 0.16396812 - time (sec): 24.33 - samples/sec: 5388.54 - lr: 0.000029 - momentum: 0.000000
2023-10-18 21:27:55,556 epoch 5 - iter 1782/1984 - loss 0.16439615 - time (sec): 27.39 - samples/sec: 5372.41 - lr: 0.000028 - momentum: 0.000000
2023-10-18 21:27:58,492 epoch 5 - iter 1980/1984 - loss 0.16305676 - time (sec): 30.32 - samples/sec: 5399.14 - lr: 0.000028 - momentum: 0.000000
2023-10-18 21:27:58,549 ----------------------------------------------------------------------------------------------------
2023-10-18 21:27:58,549 EPOCH 5 done: loss 0.1629 - lr: 0.000028
2023-10-18 21:28:00,391 DEV : loss 0.1396612972021103 - f1-score (micro avg)  0.6016
2023-10-18 21:28:00,410 saving best model
2023-10-18 21:28:00,443 ----------------------------------------------------------------------------------------------------
2023-10-18 21:28:03,439 epoch 6 - iter 198/1984 - loss 0.16107360 - time (sec): 3.00 - samples/sec: 5139.73 - lr: 0.000027 - momentum: 0.000000
2023-10-18 21:28:06,549 epoch 6 - iter 396/1984 - loss 0.15421664 - time (sec): 6.11 - samples/sec: 5364.46 - lr: 0.000027 - momentum: 0.000000
2023-10-18 21:28:09,674 epoch 6 - iter 594/1984 - loss 0.15915795 - time (sec): 9.23 - samples/sec: 5330.25 - lr: 0.000026 - momentum: 0.000000
2023-10-18 21:28:12,713 epoch 6 - iter 792/1984 - loss 0.15438849 - time (sec): 12.27 - samples/sec: 5364.46 - lr: 0.000026 - momentum: 0.000000
2023-10-18 21:28:15,664 epoch 6 - iter 990/1984 - loss 0.15292486 - time (sec): 15.22 - samples/sec: 5399.57 - lr: 0.000025 - momentum: 0.000000
2023-10-18 21:28:18,518 epoch 6 - iter 1188/1984 - loss 0.15391287 - time (sec): 18.07 - samples/sec: 5466.01 - lr: 0.000024 - momentum: 0.000000
2023-10-18 21:28:21,258 epoch 6 - iter 1386/1984 - loss 0.15391731 - time (sec): 20.81 - samples/sec: 5532.31 - lr: 0.000024 - momentum: 0.000000
2023-10-18 21:28:24,192 epoch 6 - iter 1584/1984 - loss 0.15364826 - time (sec): 23.75 - samples/sec: 5517.99 - lr: 0.000023 - momentum: 0.000000
2023-10-18 21:28:27,215 epoch 6 - iter 1782/1984 - loss 0.15355381 - time (sec): 26.77 - samples/sec: 5506.02 - lr: 0.000023 - momentum: 0.000000
2023-10-18 21:28:30,278 epoch 6 - iter 1980/1984 - loss 0.15357759 - time (sec): 29.84 - samples/sec: 5488.25 - lr: 0.000022 - momentum: 0.000000
2023-10-18 21:28:30,343 ----------------------------------------------------------------------------------------------------
2023-10-18 21:28:30,343 EPOCH 6 done: loss 0.1536 - lr: 0.000022
2023-10-18 21:28:32,160 DEV : loss 0.14352329075336456 - f1-score (micro avg)  0.6138
2023-10-18 21:28:32,179 saving best model
2023-10-18 21:28:32,213 ----------------------------------------------------------------------------------------------------
2023-10-18 21:28:35,313 epoch 7 - iter 198/1984 - loss 0.14010170 - time (sec): 3.10 - samples/sec: 5495.13 - lr: 0.000022 - momentum: 0.000000
2023-10-18 21:28:38,375 epoch 7 - iter 396/1984 - loss 0.14331514 - time (sec): 6.16 - samples/sec: 5523.70 - lr: 0.000021 - momentum: 0.000000
2023-10-18 21:28:41,433 epoch 7 - iter 594/1984 - loss 0.14376651 - time (sec): 9.22 - samples/sec: 5353.76 - lr: 0.000021 - momentum: 0.000000
2023-10-18 21:28:44,525 epoch 7 - iter 792/1984 - loss 0.14327862 - time (sec): 12.31 - samples/sec: 5209.88 - lr: 0.000020 - momentum: 0.000000
2023-10-18 21:28:47,677 epoch 7 - iter 990/1984 - loss 0.14545548 - time (sec): 15.46 - samples/sec: 5195.18 - lr: 0.000019 - momentum: 0.000000
2023-10-18 21:28:50,714 epoch 7 - iter 1188/1984 - loss 0.14341143 - time (sec): 18.50 - samples/sec: 5255.03 - lr: 0.000019 - momentum: 0.000000
2023-10-18 21:28:53,759 epoch 7 - iter 1386/1984 - loss 0.14255578 - time (sec): 21.54 - samples/sec: 5272.56 - lr: 0.000018 - momentum: 0.000000
2023-10-18 21:28:56,904 epoch 7 - iter 1584/1984 - loss 0.14220705 - time (sec): 24.69 - samples/sec: 5262.27 - lr: 0.000018 - momentum: 0.000000
2023-10-18 21:28:59,957 epoch 7 - iter 1782/1984 - loss 0.14227082 - time (sec): 27.74 - samples/sec: 5273.02 - lr: 0.000017 - momentum: 0.000000
2023-10-18 21:29:03,032 epoch 7 - iter 1980/1984 - loss 0.14322288 - time (sec): 30.82 - samples/sec: 5315.07 - lr: 0.000017 - momentum: 0.000000
2023-10-18 21:29:03,091 ----------------------------------------------------------------------------------------------------
2023-10-18 21:29:03,091 EPOCH 7 done: loss 0.1432 - lr: 0.000017
2023-10-18 21:29:04,914 DEV : loss 0.1460934579372406 - f1-score (micro avg)  0.6181
2023-10-18 21:29:04,933 saving best model
2023-10-18 21:29:04,967 ----------------------------------------------------------------------------------------------------
2023-10-18 21:29:07,991 epoch 8 - iter 198/1984 - loss 0.15508407 - time (sec): 3.02 - samples/sec: 5680.49 - lr: 0.000016 - momentum: 0.000000
2023-10-18 21:29:11,034 epoch 8 - iter 396/1984 - loss 0.14857609 - time (sec): 6.07 - samples/sec: 5447.32 - lr: 0.000016 - momentum: 0.000000
2023-10-18 21:29:14,053 epoch 8 - iter 594/1984 - loss 0.14831674 - time (sec): 9.09 - samples/sec: 5539.95 - lr: 0.000015 - momentum: 0.000000
2023-10-18 21:29:16,965 epoch 8 - iter 792/1984 - loss 0.14431698 - time (sec): 12.00 - samples/sec: 5580.93 - lr: 0.000014 - momentum: 0.000000
2023-10-18 21:29:19,967 epoch 8 - iter 990/1984 - loss 0.14072716 - time (sec): 15.00 - samples/sec: 5527.79 - lr: 0.000014 - momentum: 0.000000
2023-10-18 21:29:23,046 epoch 8 - iter 1188/1984 - loss 0.13730687 - time (sec): 18.08 - samples/sec: 5511.35 - lr: 0.000013 - momentum: 0.000000
2023-10-18 21:29:26,169 epoch 8 - iter 1386/1984 - loss 0.13802672 - time (sec): 21.20 - samples/sec: 5447.35 - lr: 0.000013 - momentum: 0.000000
2023-10-18 21:29:29,261 epoch 8 - iter 1584/1984 - loss 0.13646616 - time (sec): 24.29 - samples/sec: 5454.78 - lr: 0.000012 - momentum: 0.000000
2023-10-18 21:29:32,269 epoch 8 - iter 1782/1984 - loss 0.13626809 - time (sec): 27.30 - samples/sec: 5441.22 - lr: 0.000012 - momentum: 0.000000
2023-10-18 21:29:35,338 epoch 8 - iter 1980/1984 - loss 0.13563883 - time (sec): 30.37 - samples/sec: 5385.09 - lr: 0.000011 - momentum: 0.000000
2023-10-18 21:29:35,401 ----------------------------------------------------------------------------------------------------
2023-10-18 21:29:35,401 EPOCH 8 done: loss 0.1354 - lr: 0.000011
2023-10-18 21:29:37,660 DEV : loss 0.14632448554039001 - f1-score (micro avg)  0.6125
2023-10-18 21:29:37,681 ----------------------------------------------------------------------------------------------------
2023-10-18 21:29:40,791 epoch 9 - iter 198/1984 - loss 0.12436606 - time (sec): 3.11 - samples/sec: 4996.85 - lr: 0.000011 - momentum: 0.000000
2023-10-18 21:29:43,807 epoch 9 - iter 396/1984 - loss 0.12130140 - time (sec): 6.12 - samples/sec: 5243.22 - lr: 0.000010 - momentum: 0.000000
2023-10-18 21:29:46,923 epoch 9 - iter 594/1984 - loss 0.12036860 - time (sec): 9.24 - samples/sec: 5222.59 - lr: 0.000009 - momentum: 0.000000
2023-10-18 21:29:49,986 epoch 9 - iter 792/1984 - loss 0.12390287 - time (sec): 12.30 - samples/sec: 5269.18 - lr: 0.000009 - momentum: 0.000000
2023-10-18 21:29:52,890 epoch 9 - iter 990/1984 - loss 0.12172877 - time (sec): 15.21 - samples/sec: 5357.59 - lr: 0.000008 - momentum: 0.000000
2023-10-18 21:29:55,968 epoch 9 - iter 1188/1984 - loss 0.12359398 - time (sec): 18.29 - samples/sec: 5348.49 - lr: 0.000008 - momentum: 0.000000
2023-10-18 21:29:59,087 epoch 9 - iter 1386/1984 - loss 0.12601590 - time (sec): 21.41 - samples/sec: 5335.60 - lr: 0.000007 - momentum: 0.000000
2023-10-18 21:30:02,163 epoch 9 - iter 1584/1984 - loss 0.12731249 - time (sec): 24.48 - samples/sec: 5329.19 - lr: 0.000007 - momentum: 0.000000
2023-10-18 21:30:05,015 epoch 9 - iter 1782/1984 - loss 0.13000702 - time (sec): 27.33 - samples/sec: 5411.53 - lr: 0.000006 - momentum: 0.000000
2023-10-18 21:30:07,908 epoch 9 - iter 1980/1984 - loss 0.13026706 - time (sec): 30.23 - samples/sec: 5415.84 - lr: 0.000006 - momentum: 0.000000
2023-10-18 21:30:07,967 ----------------------------------------------------------------------------------------------------
2023-10-18 21:30:07,968 EPOCH 9 done: loss 0.1302 - lr: 0.000006
2023-10-18 21:30:09,795 DEV : loss 0.14653360843658447 - f1-score (micro avg)  0.6208
2023-10-18 21:30:09,816 saving best model
2023-10-18 21:30:09,851 ----------------------------------------------------------------------------------------------------
2023-10-18 21:30:13,178 epoch 10 - iter 198/1984 - loss 0.10287722 - time (sec): 3.33 - samples/sec: 4987.47 - lr: 0.000005 - momentum: 0.000000
2023-10-18 21:30:16,230 epoch 10 - iter 396/1984 - loss 0.11633178 - time (sec): 6.38 - samples/sec: 5153.20 - lr: 0.000004 - momentum: 0.000000
2023-10-18 21:30:19,312 epoch 10 - iter 594/1984 - loss 0.12476201 - time (sec): 9.46 - samples/sec: 5193.09 - lr: 0.000004 - momentum: 0.000000
2023-10-18 21:30:22,352 epoch 10 - iter 792/1984 - loss 0.12536802 - time (sec): 12.50 - samples/sec: 5209.88 - lr: 0.000003 - momentum: 0.000000
2023-10-18 21:30:25,421 epoch 10 - iter 990/1984 - loss 0.12695762 - time (sec): 15.57 - samples/sec: 5294.90 - lr: 0.000003 - momentum: 0.000000
2023-10-18 21:30:28,513 epoch 10 - iter 1188/1984 - loss 0.12707600 - time (sec): 18.66 - samples/sec: 5283.15 - lr: 0.000002 - momentum: 0.000000
2023-10-18 21:30:31,586 epoch 10 - iter 1386/1984 - loss 0.12684283 - time (sec): 21.73 - samples/sec: 5283.84 - lr: 0.000002 - momentum: 0.000000
2023-10-18 21:30:34,749 epoch 10 - iter 1584/1984 - loss 0.12638192 - time (sec): 24.90 - samples/sec: 5252.68 - lr: 0.000001 - momentum: 0.000000
2023-10-18 21:30:37,835 epoch 10 - iter 1782/1984 - loss 0.12660082 - time (sec): 27.98 - samples/sec: 5271.15 - lr: 0.000001 - momentum: 0.000000
2023-10-18 21:30:40,835 epoch 10 - iter 1980/1984 - loss 0.12678588 - time (sec): 30.98 - samples/sec: 5285.51 - lr: 0.000000 - momentum: 0.000000
2023-10-18 21:30:40,894 ----------------------------------------------------------------------------------------------------
2023-10-18 21:30:40,894 EPOCH 10 done: loss 0.1267 - lr: 0.000000
2023-10-18 21:30:42,746 DEV : loss 0.1484779566526413 - f1-score (micro avg)  0.6175
2023-10-18 21:30:42,793 ----------------------------------------------------------------------------------------------------
2023-10-18 21:30:42,793 Loading model from best epoch ...
2023-10-18 21:30:42,875 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
2023-10-18 21:30:44,397 
Results:
- F-score (micro) 0.6425
- F-score (macro) 0.4918
- Accuracy 0.5084

By class:
              precision    recall  f1-score   support

         LOC     0.7192    0.7664    0.7421       655
         PER     0.4534    0.6547    0.5358       223
         ORG     0.3778    0.1339    0.1977       127

   micro avg     0.6244    0.6617    0.6425      1005
   macro avg     0.5168    0.5183    0.4918      1005
weighted avg     0.6171    0.6617    0.6275      1005

2023-10-18 21:30:44,397 ----------------------------------------------------------------------------------------------------