2023-10-17 23:31:54,079 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,080 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): ElectraModel(
      (embeddings): ElectraEmbeddings(
        (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): ElectraEncoder(
        (layer): ModuleList(
          (0-11): 12 x ElectraLayer(
            (attention): ElectraAttention(
              (self): ElectraSelfAttention(
                (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): ElectraSelfOutput(
                (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): ElectraIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): ElectraOutput(
              (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)
            )
          )
        )
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-17 23:31:54,080 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,080 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
 - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
2023-10-17 23:31:54,080 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 Train:  5901 sentences
2023-10-17 23:31:54,081         (train_with_dev=False, train_with_test=False)
2023-10-17 23:31:54,081 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 Training Params:
2023-10-17 23:31:54,081  - learning_rate: "3e-05" 
2023-10-17 23:31:54,081  - mini_batch_size: "4"
2023-10-17 23:31:54,081  - max_epochs: "10"
2023-10-17 23:31:54,081  - shuffle: "True"
2023-10-17 23:31:54,081 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 Plugins:
2023-10-17 23:31:54,081  - TensorboardLogger
2023-10-17 23:31:54,081  - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 23:31:54,081 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 23:31:54,081  - metric: "('micro avg', 'f1-score')"
2023-10-17 23:31:54,081 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 Computation:
2023-10-17 23:31:54,081  - compute on device: cuda:0
2023-10-17 23:31:54,081  - embedding storage: none
2023-10-17 23:31:54,081 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 Model training base path: "hmbench-hipe2020/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
2023-10-17 23:31:54,081 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 ----------------------------------------------------------------------------------------------------
2023-10-17 23:31:54,081 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 23:32:01,352 epoch 1 - iter 147/1476 - loss 2.80100325 - time (sec): 7.27 - samples/sec: 2332.20 - lr: 0.000003 - momentum: 0.000000
2023-10-17 23:32:08,598 epoch 1 - iter 294/1476 - loss 1.73992832 - time (sec): 14.52 - samples/sec: 2355.51 - lr: 0.000006 - momentum: 0.000000
2023-10-17 23:32:16,120 epoch 1 - iter 441/1476 - loss 1.28272528 - time (sec): 22.04 - samples/sec: 2380.08 - lr: 0.000009 - momentum: 0.000000
2023-10-17 23:32:23,324 epoch 1 - iter 588/1476 - loss 1.05514826 - time (sec): 29.24 - samples/sec: 2366.74 - lr: 0.000012 - momentum: 0.000000
2023-10-17 23:32:30,350 epoch 1 - iter 735/1476 - loss 0.91050463 - time (sec): 36.27 - samples/sec: 2358.18 - lr: 0.000015 - momentum: 0.000000
2023-10-17 23:32:37,153 epoch 1 - iter 882/1476 - loss 0.81059549 - time (sec): 43.07 - samples/sec: 2333.54 - lr: 0.000018 - momentum: 0.000000
2023-10-17 23:32:43,975 epoch 1 - iter 1029/1476 - loss 0.73282939 - time (sec): 49.89 - samples/sec: 2321.26 - lr: 0.000021 - momentum: 0.000000
2023-10-17 23:32:51,442 epoch 1 - iter 1176/1476 - loss 0.66324521 - time (sec): 57.36 - samples/sec: 2349.23 - lr: 0.000024 - momentum: 0.000000
2023-10-17 23:32:58,230 epoch 1 - iter 1323/1476 - loss 0.61491813 - time (sec): 64.15 - samples/sec: 2335.55 - lr: 0.000027 - momentum: 0.000000
2023-10-17 23:33:05,023 epoch 1 - iter 1470/1476 - loss 0.57169143 - time (sec): 70.94 - samples/sec: 2334.46 - lr: 0.000030 - momentum: 0.000000
2023-10-17 23:33:05,308 ----------------------------------------------------------------------------------------------------
2023-10-17 23:33:05,309 EPOCH 1 done: loss 0.5696 - lr: 0.000030
2023-10-17 23:33:11,826 DEV : loss 0.1295262575149536 - f1-score (micro avg)  0.7831
2023-10-17 23:33:11,862 saving best model
2023-10-17 23:33:12,267 ----------------------------------------------------------------------------------------------------
2023-10-17 23:33:19,808 epoch 2 - iter 147/1476 - loss 0.14873257 - time (sec): 7.54 - samples/sec: 2259.44 - lr: 0.000030 - momentum: 0.000000
2023-10-17 23:33:27,056 epoch 2 - iter 294/1476 - loss 0.13554378 - time (sec): 14.79 - samples/sec: 2242.31 - lr: 0.000029 - momentum: 0.000000
2023-10-17 23:33:33,971 epoch 2 - iter 441/1476 - loss 0.13082620 - time (sec): 21.70 - samples/sec: 2287.60 - lr: 0.000029 - momentum: 0.000000
2023-10-17 23:33:41,017 epoch 2 - iter 588/1476 - loss 0.12999291 - time (sec): 28.75 - samples/sec: 2285.60 - lr: 0.000029 - momentum: 0.000000
2023-10-17 23:33:47,740 epoch 2 - iter 735/1476 - loss 0.13337666 - time (sec): 35.47 - samples/sec: 2280.96 - lr: 0.000028 - momentum: 0.000000
2023-10-17 23:33:54,755 epoch 2 - iter 882/1476 - loss 0.12980013 - time (sec): 42.49 - samples/sec: 2302.94 - lr: 0.000028 - momentum: 0.000000
2023-10-17 23:34:01,945 epoch 2 - iter 1029/1476 - loss 0.12828897 - time (sec): 49.68 - samples/sec: 2314.15 - lr: 0.000028 - momentum: 0.000000
2023-10-17 23:34:09,580 epoch 2 - iter 1176/1476 - loss 0.12736958 - time (sec): 57.31 - samples/sec: 2345.37 - lr: 0.000027 - momentum: 0.000000
2023-10-17 23:34:16,710 epoch 2 - iter 1323/1476 - loss 0.12756206 - time (sec): 64.44 - samples/sec: 2337.22 - lr: 0.000027 - momentum: 0.000000
2023-10-17 23:34:23,713 epoch 2 - iter 1470/1476 - loss 0.12844708 - time (sec): 71.44 - samples/sec: 2322.32 - lr: 0.000027 - momentum: 0.000000
2023-10-17 23:34:24,002 ----------------------------------------------------------------------------------------------------
2023-10-17 23:34:24,002 EPOCH 2 done: loss 0.1282 - lr: 0.000027
2023-10-17 23:34:35,709 DEV : loss 0.11762264370918274 - f1-score (micro avg)  0.8046
2023-10-17 23:34:35,743 saving best model
2023-10-17 23:34:36,184 ----------------------------------------------------------------------------------------------------
2023-10-17 23:34:43,287 epoch 3 - iter 147/1476 - loss 0.06016337 - time (sec): 7.10 - samples/sec: 2283.90 - lr: 0.000026 - momentum: 0.000000
2023-10-17 23:34:50,147 epoch 3 - iter 294/1476 - loss 0.06475612 - time (sec): 13.96 - samples/sec: 2305.25 - lr: 0.000026 - momentum: 0.000000
2023-10-17 23:34:57,519 epoch 3 - iter 441/1476 - loss 0.07116651 - time (sec): 21.33 - samples/sec: 2292.73 - lr: 0.000026 - momentum: 0.000000
2023-10-17 23:35:04,498 epoch 3 - iter 588/1476 - loss 0.08196817 - time (sec): 28.31 - samples/sec: 2283.52 - lr: 0.000025 - momentum: 0.000000
2023-10-17 23:35:11,873 epoch 3 - iter 735/1476 - loss 0.08093682 - time (sec): 35.69 - samples/sec: 2309.40 - lr: 0.000025 - momentum: 0.000000
2023-10-17 23:35:18,750 epoch 3 - iter 882/1476 - loss 0.08196150 - time (sec): 42.56 - samples/sec: 2308.17 - lr: 0.000025 - momentum: 0.000000
2023-10-17 23:35:26,053 epoch 3 - iter 1029/1476 - loss 0.08435209 - time (sec): 49.87 - samples/sec: 2333.52 - lr: 0.000024 - momentum: 0.000000
2023-10-17 23:35:33,011 epoch 3 - iter 1176/1476 - loss 0.08397687 - time (sec): 56.83 - samples/sec: 2329.78 - lr: 0.000024 - momentum: 0.000000
2023-10-17 23:35:40,077 epoch 3 - iter 1323/1476 - loss 0.08214585 - time (sec): 63.89 - samples/sec: 2328.49 - lr: 0.000024 - momentum: 0.000000
2023-10-17 23:35:47,311 epoch 3 - iter 1470/1476 - loss 0.08451257 - time (sec): 71.13 - samples/sec: 2332.43 - lr: 0.000023 - momentum: 0.000000
2023-10-17 23:35:47,583 ----------------------------------------------------------------------------------------------------
2023-10-17 23:35:47,583 EPOCH 3 done: loss 0.0845 - lr: 0.000023
2023-10-17 23:35:59,745 DEV : loss 0.12455519288778305 - f1-score (micro avg)  0.8311
2023-10-17 23:35:59,780 saving best model
2023-10-17 23:36:00,314 ----------------------------------------------------------------------------------------------------
2023-10-17 23:36:07,595 epoch 4 - iter 147/1476 - loss 0.04135825 - time (sec): 7.28 - samples/sec: 2357.87 - lr: 0.000023 - momentum: 0.000000
2023-10-17 23:36:14,807 epoch 4 - iter 294/1476 - loss 0.06168456 - time (sec): 14.49 - samples/sec: 2251.52 - lr: 0.000023 - momentum: 0.000000
2023-10-17 23:36:22,206 epoch 4 - iter 441/1476 - loss 0.06131877 - time (sec): 21.89 - samples/sec: 2331.92 - lr: 0.000022 - momentum: 0.000000
2023-10-17 23:36:29,354 epoch 4 - iter 588/1476 - loss 0.06322859 - time (sec): 29.03 - samples/sec: 2356.02 - lr: 0.000022 - momentum: 0.000000
2023-10-17 23:36:36,295 epoch 4 - iter 735/1476 - loss 0.05963841 - time (sec): 35.98 - samples/sec: 2326.66 - lr: 0.000022 - momentum: 0.000000
2023-10-17 23:36:43,129 epoch 4 - iter 882/1476 - loss 0.06022421 - time (sec): 42.81 - samples/sec: 2302.29 - lr: 0.000021 - momentum: 0.000000
2023-10-17 23:36:50,355 epoch 4 - iter 1029/1476 - loss 0.05910469 - time (sec): 50.03 - samples/sec: 2309.56 - lr: 0.000021 - momentum: 0.000000
2023-10-17 23:36:57,678 epoch 4 - iter 1176/1476 - loss 0.05977555 - time (sec): 57.36 - samples/sec: 2308.40 - lr: 0.000021 - momentum: 0.000000
2023-10-17 23:37:04,755 epoch 4 - iter 1323/1476 - loss 0.05955432 - time (sec): 64.44 - samples/sec: 2296.44 - lr: 0.000020 - momentum: 0.000000
2023-10-17 23:37:12,241 epoch 4 - iter 1470/1476 - loss 0.05887704 - time (sec): 71.92 - samples/sec: 2306.74 - lr: 0.000020 - momentum: 0.000000
2023-10-17 23:37:12,500 ----------------------------------------------------------------------------------------------------
2023-10-17 23:37:12,501 EPOCH 4 done: loss 0.0589 - lr: 0.000020
2023-10-17 23:37:24,145 DEV : loss 0.17298074066638947 - f1-score (micro avg)  0.8363
2023-10-17 23:37:24,177 saving best model
2023-10-17 23:37:24,717 ----------------------------------------------------------------------------------------------------
2023-10-17 23:37:32,262 epoch 5 - iter 147/1476 - loss 0.03294191 - time (sec): 7.54 - samples/sec: 2111.85 - lr: 0.000020 - momentum: 0.000000
2023-10-17 23:37:39,929 epoch 5 - iter 294/1476 - loss 0.03494477 - time (sec): 15.21 - samples/sec: 2088.58 - lr: 0.000019 - momentum: 0.000000
2023-10-17 23:37:46,973 epoch 5 - iter 441/1476 - loss 0.03478031 - time (sec): 22.25 - samples/sec: 2167.73 - lr: 0.000019 - momentum: 0.000000
2023-10-17 23:37:54,339 epoch 5 - iter 588/1476 - loss 0.03960322 - time (sec): 29.62 - samples/sec: 2201.89 - lr: 0.000019 - momentum: 0.000000
2023-10-17 23:38:01,526 epoch 5 - iter 735/1476 - loss 0.03902751 - time (sec): 36.81 - samples/sec: 2204.17 - lr: 0.000018 - momentum: 0.000000
2023-10-17 23:38:09,120 epoch 5 - iter 882/1476 - loss 0.03994877 - time (sec): 44.40 - samples/sec: 2276.31 - lr: 0.000018 - momentum: 0.000000
2023-10-17 23:38:16,476 epoch 5 - iter 1029/1476 - loss 0.03929098 - time (sec): 51.76 - samples/sec: 2292.19 - lr: 0.000018 - momentum: 0.000000
2023-10-17 23:38:23,357 epoch 5 - iter 1176/1476 - loss 0.03869095 - time (sec): 58.64 - samples/sec: 2282.48 - lr: 0.000017 - momentum: 0.000000
2023-10-17 23:38:29,950 epoch 5 - iter 1323/1476 - loss 0.03956085 - time (sec): 65.23 - samples/sec: 2267.36 - lr: 0.000017 - momentum: 0.000000
2023-10-17 23:38:37,204 epoch 5 - iter 1470/1476 - loss 0.03973073 - time (sec): 72.49 - samples/sec: 2288.84 - lr: 0.000017 - momentum: 0.000000
2023-10-17 23:38:37,481 ----------------------------------------------------------------------------------------------------
2023-10-17 23:38:37,481 EPOCH 5 done: loss 0.0396 - lr: 0.000017
2023-10-17 23:38:49,143 DEV : loss 0.19069240987300873 - f1-score (micro avg)  0.8347
2023-10-17 23:38:49,173 ----------------------------------------------------------------------------------------------------
2023-10-17 23:38:56,442 epoch 6 - iter 147/1476 - loss 0.04151410 - time (sec): 7.27 - samples/sec: 2504.19 - lr: 0.000016 - momentum: 0.000000
2023-10-17 23:39:03,567 epoch 6 - iter 294/1476 - loss 0.03128771 - time (sec): 14.39 - samples/sec: 2396.11 - lr: 0.000016 - momentum: 0.000000
2023-10-17 23:39:10,534 epoch 6 - iter 441/1476 - loss 0.02813382 - time (sec): 21.36 - samples/sec: 2348.82 - lr: 0.000016 - momentum: 0.000000
2023-10-17 23:39:18,296 epoch 6 - iter 588/1476 - loss 0.02762635 - time (sec): 29.12 - samples/sec: 2410.53 - lr: 0.000015 - momentum: 0.000000
2023-10-17 23:39:25,189 epoch 6 - iter 735/1476 - loss 0.02714453 - time (sec): 36.01 - samples/sec: 2379.21 - lr: 0.000015 - momentum: 0.000000
2023-10-17 23:39:32,347 epoch 6 - iter 882/1476 - loss 0.02908034 - time (sec): 43.17 - samples/sec: 2358.30 - lr: 0.000015 - momentum: 0.000000
2023-10-17 23:39:39,421 epoch 6 - iter 1029/1476 - loss 0.02853843 - time (sec): 50.25 - samples/sec: 2369.55 - lr: 0.000014 - momentum: 0.000000
2023-10-17 23:39:46,368 epoch 6 - iter 1176/1476 - loss 0.02820722 - time (sec): 57.19 - samples/sec: 2340.90 - lr: 0.000014 - momentum: 0.000000
2023-10-17 23:39:53,086 epoch 6 - iter 1323/1476 - loss 0.02791306 - time (sec): 63.91 - samples/sec: 2341.08 - lr: 0.000014 - momentum: 0.000000
2023-10-17 23:40:00,050 epoch 6 - iter 1470/1476 - loss 0.02765959 - time (sec): 70.88 - samples/sec: 2340.75 - lr: 0.000013 - momentum: 0.000000
2023-10-17 23:40:00,317 ----------------------------------------------------------------------------------------------------
2023-10-17 23:40:00,317 EPOCH 6 done: loss 0.0276 - lr: 0.000013
2023-10-17 23:40:12,051 DEV : loss 0.2225208729505539 - f1-score (micro avg)  0.8227
2023-10-17 23:40:12,083 ----------------------------------------------------------------------------------------------------
2023-10-17 23:40:18,908 epoch 7 - iter 147/1476 - loss 0.02536894 - time (sec): 6.82 - samples/sec: 2256.74 - lr: 0.000013 - momentum: 0.000000
2023-10-17 23:40:25,940 epoch 7 - iter 294/1476 - loss 0.01875569 - time (sec): 13.86 - samples/sec: 2297.77 - lr: 0.000013 - momentum: 0.000000
2023-10-17 23:40:32,837 epoch 7 - iter 441/1476 - loss 0.01775067 - time (sec): 20.75 - samples/sec: 2274.90 - lr: 0.000012 - momentum: 0.000000
2023-10-17 23:40:39,922 epoch 7 - iter 588/1476 - loss 0.01897025 - time (sec): 27.84 - samples/sec: 2270.43 - lr: 0.000012 - momentum: 0.000000
2023-10-17 23:40:47,381 epoch 7 - iter 735/1476 - loss 0.01918291 - time (sec): 35.30 - samples/sec: 2312.85 - lr: 0.000012 - momentum: 0.000000
2023-10-17 23:40:54,197 epoch 7 - iter 882/1476 - loss 0.01739317 - time (sec): 42.11 - samples/sec: 2292.26 - lr: 0.000011 - momentum: 0.000000
2023-10-17 23:41:01,472 epoch 7 - iter 1029/1476 - loss 0.02015101 - time (sec): 49.39 - samples/sec: 2297.01 - lr: 0.000011 - momentum: 0.000000
2023-10-17 23:41:08,793 epoch 7 - iter 1176/1476 - loss 0.01942613 - time (sec): 56.71 - samples/sec: 2311.05 - lr: 0.000011 - momentum: 0.000000
2023-10-17 23:41:15,889 epoch 7 - iter 1323/1476 - loss 0.01868802 - time (sec): 63.80 - samples/sec: 2317.14 - lr: 0.000010 - momentum: 0.000000
2023-10-17 23:41:22,978 epoch 7 - iter 1470/1476 - loss 0.01907602 - time (sec): 70.89 - samples/sec: 2336.32 - lr: 0.000010 - momentum: 0.000000
2023-10-17 23:41:23,241 ----------------------------------------------------------------------------------------------------
2023-10-17 23:41:23,242 EPOCH 7 done: loss 0.0190 - lr: 0.000010
2023-10-17 23:41:34,910 DEV : loss 0.2076645791530609 - f1-score (micro avg)  0.8429
2023-10-17 23:41:34,943 saving best model
2023-10-17 23:41:35,534 ----------------------------------------------------------------------------------------------------
2023-10-17 23:41:42,573 epoch 8 - iter 147/1476 - loss 0.00728443 - time (sec): 7.04 - samples/sec: 2313.22 - lr: 0.000010 - momentum: 0.000000
2023-10-17 23:41:49,364 epoch 8 - iter 294/1476 - loss 0.01155375 - time (sec): 13.83 - samples/sec: 2230.80 - lr: 0.000009 - momentum: 0.000000
2023-10-17 23:41:56,955 epoch 8 - iter 441/1476 - loss 0.00998468 - time (sec): 21.42 - samples/sec: 2288.60 - lr: 0.000009 - momentum: 0.000000
2023-10-17 23:42:03,926 epoch 8 - iter 588/1476 - loss 0.00978555 - time (sec): 28.39 - samples/sec: 2255.65 - lr: 0.000009 - momentum: 0.000000
2023-10-17 23:42:11,251 epoch 8 - iter 735/1476 - loss 0.01395907 - time (sec): 35.72 - samples/sec: 2310.58 - lr: 0.000008 - momentum: 0.000000
2023-10-17 23:42:18,331 epoch 8 - iter 882/1476 - loss 0.01725840 - time (sec): 42.80 - samples/sec: 2310.54 - lr: 0.000008 - momentum: 0.000000
2023-10-17 23:42:25,186 epoch 8 - iter 1029/1476 - loss 0.01674081 - time (sec): 49.65 - samples/sec: 2303.01 - lr: 0.000008 - momentum: 0.000000
2023-10-17 23:42:31,919 epoch 8 - iter 1176/1476 - loss 0.01586411 - time (sec): 56.38 - samples/sec: 2297.83 - lr: 0.000007 - momentum: 0.000000
2023-10-17 23:42:38,881 epoch 8 - iter 1323/1476 - loss 0.01535313 - time (sec): 63.35 - samples/sec: 2295.24 - lr: 0.000007 - momentum: 0.000000
2023-10-17 23:42:46,810 epoch 8 - iter 1470/1476 - loss 0.01565060 - time (sec): 71.27 - samples/sec: 2323.79 - lr: 0.000007 - momentum: 0.000000
2023-10-17 23:42:47,094 ----------------------------------------------------------------------------------------------------
2023-10-17 23:42:47,094 EPOCH 8 done: loss 0.0156 - lr: 0.000007
2023-10-17 23:42:58,817 DEV : loss 0.1886121928691864 - f1-score (micro avg)  0.8538
2023-10-17 23:42:58,852 saving best model
2023-10-17 23:42:59,442 ----------------------------------------------------------------------------------------------------
2023-10-17 23:43:06,381 epoch 9 - iter 147/1476 - loss 0.00904489 - time (sec): 6.93 - samples/sec: 2327.73 - lr: 0.000006 - momentum: 0.000000
2023-10-17 23:43:13,534 epoch 9 - iter 294/1476 - loss 0.01213344 - time (sec): 14.09 - samples/sec: 2283.24 - lr: 0.000006 - momentum: 0.000000
2023-10-17 23:43:20,577 epoch 9 - iter 441/1476 - loss 0.00968888 - time (sec): 21.13 - samples/sec: 2369.80 - lr: 0.000006 - momentum: 0.000000
2023-10-17 23:43:27,560 epoch 9 - iter 588/1476 - loss 0.00805351 - time (sec): 28.11 - samples/sec: 2342.89 - lr: 0.000005 - momentum: 0.000000
2023-10-17 23:43:34,810 epoch 9 - iter 735/1476 - loss 0.00938282 - time (sec): 35.36 - samples/sec: 2362.91 - lr: 0.000005 - momentum: 0.000000
2023-10-17 23:43:41,875 epoch 9 - iter 882/1476 - loss 0.01116852 - time (sec): 42.43 - samples/sec: 2338.20 - lr: 0.000005 - momentum: 0.000000
2023-10-17 23:43:48,777 epoch 9 - iter 1029/1476 - loss 0.01064119 - time (sec): 49.33 - samples/sec: 2324.47 - lr: 0.000004 - momentum: 0.000000
2023-10-17 23:43:56,251 epoch 9 - iter 1176/1476 - loss 0.00962622 - time (sec): 56.80 - samples/sec: 2329.48 - lr: 0.000004 - momentum: 0.000000
2023-10-17 23:44:03,238 epoch 9 - iter 1323/1476 - loss 0.00924438 - time (sec): 63.79 - samples/sec: 2329.34 - lr: 0.000004 - momentum: 0.000000
2023-10-17 23:44:10,398 epoch 9 - iter 1470/1476 - loss 0.00894017 - time (sec): 70.95 - samples/sec: 2327.45 - lr: 0.000003 - momentum: 0.000000
2023-10-17 23:44:10,806 ----------------------------------------------------------------------------------------------------
2023-10-17 23:44:10,806 EPOCH 9 done: loss 0.0089 - lr: 0.000003
2023-10-17 23:44:22,347 DEV : loss 0.20553840696811676 - f1-score (micro avg)  0.8585
2023-10-17 23:44:22,380 saving best model
2023-10-17 23:44:22,978 ----------------------------------------------------------------------------------------------------
2023-10-17 23:44:30,453 epoch 10 - iter 147/1476 - loss 0.00318820 - time (sec): 7.47 - samples/sec: 2363.28 - lr: 0.000003 - momentum: 0.000000
2023-10-17 23:44:37,552 epoch 10 - iter 294/1476 - loss 0.00833510 - time (sec): 14.57 - samples/sec: 2359.01 - lr: 0.000003 - momentum: 0.000000
2023-10-17 23:44:44,723 epoch 10 - iter 441/1476 - loss 0.00762237 - time (sec): 21.74 - samples/sec: 2327.59 - lr: 0.000002 - momentum: 0.000000
2023-10-17 23:44:52,236 epoch 10 - iter 588/1476 - loss 0.00924017 - time (sec): 29.26 - samples/sec: 2352.49 - lr: 0.000002 - momentum: 0.000000
2023-10-17 23:44:59,478 epoch 10 - iter 735/1476 - loss 0.00768338 - time (sec): 36.50 - samples/sec: 2325.80 - lr: 0.000002 - momentum: 0.000000
2023-10-17 23:45:06,600 epoch 10 - iter 882/1476 - loss 0.00673783 - time (sec): 43.62 - samples/sec: 2327.48 - lr: 0.000001 - momentum: 0.000000
2023-10-17 23:45:13,489 epoch 10 - iter 1029/1476 - loss 0.00625813 - time (sec): 50.51 - samples/sec: 2314.98 - lr: 0.000001 - momentum: 0.000000
2023-10-17 23:45:20,557 epoch 10 - iter 1176/1476 - loss 0.00604069 - time (sec): 57.58 - samples/sec: 2306.39 - lr: 0.000001 - momentum: 0.000000
2023-10-17 23:45:27,808 epoch 10 - iter 1323/1476 - loss 0.00615467 - time (sec): 64.83 - samples/sec: 2301.76 - lr: 0.000000 - momentum: 0.000000
2023-10-17 23:45:34,888 epoch 10 - iter 1470/1476 - loss 0.00646005 - time (sec): 71.91 - samples/sec: 2306.16 - lr: 0.000000 - momentum: 0.000000
2023-10-17 23:45:35,144 ----------------------------------------------------------------------------------------------------
2023-10-17 23:45:35,144 EPOCH 10 done: loss 0.0064 - lr: 0.000000
2023-10-17 23:45:46,863 DEV : loss 0.20840205252170563 - f1-score (micro avg)  0.8595
2023-10-17 23:45:46,900 saving best model
2023-10-17 23:45:47,919 ----------------------------------------------------------------------------------------------------
2023-10-17 23:45:47,920 Loading model from best epoch ...
2023-10-17 23:45:49,391 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
2023-10-17 23:45:56,118 
Results:
- F-score (micro) 0.8122
- F-score (macro) 0.7254
- Accuracy 0.7022

By class:
              precision    recall  f1-score   support

         loc     0.8730    0.8811    0.8770       858
        pers     0.7660    0.8045    0.7847       537
         org     0.6357    0.6212    0.6284       132
        prod     0.7458    0.7213    0.7333        61
        time     0.5645    0.6481    0.6034        54

   micro avg     0.8030    0.8216    0.8122      1642
   macro avg     0.7170    0.7353    0.7254      1642
weighted avg     0.8040    0.8216    0.8125      1642

2023-10-17 23:45:56,118 ----------------------------------------------------------------------------------------------------