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hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5

Token Classification
Flair
PyTorch
TensorBoard
French
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697552504.bce904bcef33.2023.16 +3 -0
  5. test.tsv +0 -0
  6. training.log +238 -0
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dev.tsv ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 14:23:19 0.0000 0.4079 0.0948 0.7208 0.7387 0.7296 0.5904
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+ 2 14:24:55 0.0000 0.1131 0.0937 0.7259 0.7579 0.7416 0.6047
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+ 3 14:26:29 0.0000 0.0846 0.1210 0.7117 0.7986 0.7527 0.6198
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+ 4 14:28:04 0.0000 0.0691 0.1572 0.7162 0.7964 0.7542 0.6263
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+ 5 14:29:40 0.0000 0.0493 0.1639 0.7519 0.7986 0.7745 0.6465
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+ 6 14:31:15 0.0000 0.0367 0.1994 0.7558 0.7738 0.7647 0.6322
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+ 7 14:32:53 0.0000 0.0250 0.2021 0.7236 0.8054 0.7623 0.6340
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+ 8 14:34:31 0.0000 0.0187 0.2278 0.7428 0.7839 0.7628 0.6311
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+ 9 14:36:09 0.0000 0.0140 0.2351 0.7435 0.7805 0.7616 0.6284
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+ 10 14:37:45 0.0000 0.0086 0.2348 0.7360 0.7885 0.7613 0.6302
runs/events.out.tfevents.1697552504.bce904bcef33.2023.16 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 14:21:44,567 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,568 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): ElectraModel(
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+ (embeddings): ElectraEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
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+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): ElectraEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x ElectraLayer(
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+ (attention): ElectraAttention(
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+ (self): ElectraSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): ElectraSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ (intermediate): ElectraIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): ElectraOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 14:21:44,568 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,568 MultiCorpus: 7936 train + 992 dev + 992 test sentences
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+ - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 Train: 7936 sentences
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+ 2023-10-17 14:21:44,569 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 Training Params:
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+ 2023-10-17 14:21:44,569 - learning_rate: "3e-05"
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+ 2023-10-17 14:21:44,569 - mini_batch_size: "4"
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+ 2023-10-17 14:21:44,569 - max_epochs: "10"
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+ 2023-10-17 14:21:44,569 - shuffle: "True"
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 Plugins:
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+ 2023-10-17 14:21:44,569 - TensorboardLogger
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+ 2023-10-17 14:21:44,569 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 14:21:44,569 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 Computation:
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+ 2023-10-17 14:21:44,569 - compute on device: cuda:0
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+ 2023-10-17 14:21:44,569 - embedding storage: none
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 Model training base path: "hmbench-icdar/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:21:44,569 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 14:21:53,339 epoch 1 - iter 198/1984 - loss 2.35272971 - time (sec): 8.77 - samples/sec: 1839.29 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 14:22:02,373 epoch 1 - iter 396/1984 - loss 1.35226237 - time (sec): 17.80 - samples/sec: 1885.07 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 14:22:11,529 epoch 1 - iter 594/1984 - loss 0.99696504 - time (sec): 26.96 - samples/sec: 1827.49 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 14:22:20,576 epoch 1 - iter 792/1984 - loss 0.80282302 - time (sec): 36.01 - samples/sec: 1811.71 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 14:22:29,709 epoch 1 - iter 990/1984 - loss 0.68889213 - time (sec): 45.14 - samples/sec: 1785.58 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 14:22:38,778 epoch 1 - iter 1188/1984 - loss 0.59950974 - time (sec): 54.21 - samples/sec: 1788.54 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 14:22:48,278 epoch 1 - iter 1386/1984 - loss 0.53152483 - time (sec): 63.71 - samples/sec: 1787.61 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:22:57,520 epoch 1 - iter 1584/1984 - loss 0.48053194 - time (sec): 72.95 - samples/sec: 1789.09 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:23:06,591 epoch 1 - iter 1782/1984 - loss 0.44152631 - time (sec): 82.02 - samples/sec: 1792.58 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:23:16,377 epoch 1 - iter 1980/1984 - loss 0.40853255 - time (sec): 91.81 - samples/sec: 1782.55 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 14:23:16,556 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:23:16,557 EPOCH 1 done: loss 0.4079 - lr: 0.000030
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+ 2023-10-17 14:23:19,804 DEV : loss 0.09482365101575851 - f1-score (micro avg) 0.7296
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+ 2023-10-17 14:23:19,827 saving best model
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+ 2023-10-17 14:23:20,267 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:23:29,741 epoch 2 - iter 198/1984 - loss 0.10656840 - time (sec): 9.47 - samples/sec: 1799.02 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 14:23:38,991 epoch 2 - iter 396/1984 - loss 0.10818698 - time (sec): 18.72 - samples/sec: 1785.10 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 14:23:48,043 epoch 2 - iter 594/1984 - loss 0.11021072 - time (sec): 27.77 - samples/sec: 1791.14 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 14:23:57,001 epoch 2 - iter 792/1984 - loss 0.11163153 - time (sec): 36.73 - samples/sec: 1776.91 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 14:24:06,134 epoch 2 - iter 990/1984 - loss 0.11183860 - time (sec): 45.87 - samples/sec: 1789.70 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 14:24:15,206 epoch 2 - iter 1188/1984 - loss 0.11055508 - time (sec): 54.94 - samples/sec: 1783.11 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 14:24:24,164 epoch 2 - iter 1386/1984 - loss 0.11239117 - time (sec): 63.90 - samples/sec: 1783.76 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 14:24:33,227 epoch 2 - iter 1584/1984 - loss 0.11240077 - time (sec): 72.96 - samples/sec: 1784.23 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:24:42,472 epoch 2 - iter 1782/1984 - loss 0.11194093 - time (sec): 82.20 - samples/sec: 1791.13 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:24:51,688 epoch 2 - iter 1980/1984 - loss 0.11315701 - time (sec): 91.42 - samples/sec: 1790.60 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:24:51,873 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:24:51,873 EPOCH 2 done: loss 0.1131 - lr: 0.000027
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+ 2023-10-17 14:24:55,803 DEV : loss 0.09371839463710785 - f1-score (micro avg) 0.7416
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+ 2023-10-17 14:24:55,825 saving best model
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+ 2023-10-17 14:24:56,405 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 14:25:05,416 epoch 3 - iter 198/1984 - loss 0.08688426 - time (sec): 9.00 - samples/sec: 1688.59 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 14:25:14,414 epoch 3 - iter 396/1984 - loss 0.08535351 - time (sec): 18.00 - samples/sec: 1749.88 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 14:25:23,215 epoch 3 - iter 594/1984 - loss 0.08392751 - time (sec): 26.80 - samples/sec: 1804.77 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 14:25:31,824 epoch 3 - iter 792/1984 - loss 0.08103732 - time (sec): 35.41 - samples/sec: 1840.55 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:25:40,506 epoch 3 - iter 990/1984 - loss 0.08337144 - time (sec): 44.09 - samples/sec: 1849.78 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:25:49,403 epoch 3 - iter 1188/1984 - loss 0.08370851 - time (sec): 52.99 - samples/sec: 1861.28 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:25:58,429 epoch 3 - iter 1386/1984 - loss 0.08381845 - time (sec): 62.01 - samples/sec: 1844.58 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:26:07,378 epoch 3 - iter 1584/1984 - loss 0.08471920 - time (sec): 70.96 - samples/sec: 1839.09 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:26:16,525 epoch 3 - iter 1782/1984 - loss 0.08345150 - time (sec): 80.11 - samples/sec: 1841.82 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:26:25,615 epoch 3 - iter 1980/1984 - loss 0.08467558 - time (sec): 89.20 - samples/sec: 1834.64 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 14:26:25,813 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:26:25,813 EPOCH 3 done: loss 0.0846 - lr: 0.000023
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+ 2023-10-17 14:26:29,205 DEV : loss 0.12102336436510086 - f1-score (micro avg) 0.7527
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+ 2023-10-17 14:26:29,226 saving best model
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+ 2023-10-17 14:26:29,740 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:26:38,903 epoch 4 - iter 198/1984 - loss 0.06092971 - time (sec): 9.16 - samples/sec: 1722.01 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 14:26:48,164 epoch 4 - iter 396/1984 - loss 0.06579458 - time (sec): 18.42 - samples/sec: 1768.71 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 14:26:57,322 epoch 4 - iter 594/1984 - loss 0.06716833 - time (sec): 27.58 - samples/sec: 1757.12 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 14:27:06,588 epoch 4 - iter 792/1984 - loss 0.06375569 - time (sec): 36.84 - samples/sec: 1767.60 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 14:27:15,756 epoch 4 - iter 990/1984 - loss 0.06406550 - time (sec): 46.01 - samples/sec: 1774.90 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 14:27:24,732 epoch 4 - iter 1188/1984 - loss 0.06699263 - time (sec): 54.99 - samples/sec: 1778.23 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:27:33,952 epoch 4 - iter 1386/1984 - loss 0.06689916 - time (sec): 64.21 - samples/sec: 1779.83 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:27:42,808 epoch 4 - iter 1584/1984 - loss 0.06814155 - time (sec): 73.06 - samples/sec: 1785.97 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:27:51,811 epoch 4 - iter 1782/1984 - loss 0.06896506 - time (sec): 82.07 - samples/sec: 1795.00 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:28:00,740 epoch 4 - iter 1980/1984 - loss 0.06896094 - time (sec): 91.00 - samples/sec: 1798.71 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:28:00,926 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 14:28:00,926 EPOCH 4 done: loss 0.0691 - lr: 0.000020
131
+ 2023-10-17 14:28:04,626 DEV : loss 0.1571902483701706 - f1-score (micro avg) 0.7542
132
+ 2023-10-17 14:28:04,649 saving best model
133
+ 2023-10-17 14:28:05,140 ----------------------------------------------------------------------------------------------------
134
+ 2023-10-17 14:28:14,051 epoch 5 - iter 198/1984 - loss 0.04531813 - time (sec): 8.91 - samples/sec: 1796.32 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:28:23,211 epoch 5 - iter 396/1984 - loss 0.04647017 - time (sec): 18.07 - samples/sec: 1815.98 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 14:28:32,522 epoch 5 - iter 594/1984 - loss 0.04475855 - time (sec): 27.38 - samples/sec: 1830.73 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 14:28:41,587 epoch 5 - iter 792/1984 - loss 0.04669245 - time (sec): 36.45 - samples/sec: 1818.97 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 14:28:50,763 epoch 5 - iter 990/1984 - loss 0.04912804 - time (sec): 45.62 - samples/sec: 1834.17 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 14:28:59,777 epoch 5 - iter 1188/1984 - loss 0.04867525 - time (sec): 54.64 - samples/sec: 1821.02 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 14:29:09,023 epoch 5 - iter 1386/1984 - loss 0.04927445 - time (sec): 63.88 - samples/sec: 1821.15 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 14:29:18,033 epoch 5 - iter 1584/1984 - loss 0.04954311 - time (sec): 72.89 - samples/sec: 1809.93 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 14:29:27,001 epoch 5 - iter 1782/1984 - loss 0.04932246 - time (sec): 81.86 - samples/sec: 1803.75 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 14:29:36,222 epoch 5 - iter 1980/1984 - loss 0.04937008 - time (sec): 91.08 - samples/sec: 1796.77 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-10-17 14:29:36,399 ----------------------------------------------------------------------------------------------------
145
+ 2023-10-17 14:29:36,399 EPOCH 5 done: loss 0.0493 - lr: 0.000017
146
+ 2023-10-17 14:29:39,976 DEV : loss 0.16390633583068848 - f1-score (micro avg) 0.7745
147
+ 2023-10-17 14:29:40,003 saving best model
148
+ 2023-10-17 14:29:40,607 ----------------------------------------------------------------------------------------------------
149
+ 2023-10-17 14:29:49,628 epoch 6 - iter 198/1984 - loss 0.03464468 - time (sec): 9.02 - samples/sec: 1836.48 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 14:29:58,781 epoch 6 - iter 396/1984 - loss 0.03116501 - time (sec): 18.17 - samples/sec: 1841.59 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 14:30:07,928 epoch 6 - iter 594/1984 - loss 0.03152775 - time (sec): 27.32 - samples/sec: 1836.49 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-10-17 14:30:17,082 epoch 6 - iter 792/1984 - loss 0.03071046 - time (sec): 36.47 - samples/sec: 1832.41 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 14:30:26,291 epoch 6 - iter 990/1984 - loss 0.03185003 - time (sec): 45.68 - samples/sec: 1820.68 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 14:30:35,672 epoch 6 - iter 1188/1984 - loss 0.03355453 - time (sec): 55.06 - samples/sec: 1823.91 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-10-17 14:30:44,728 epoch 6 - iter 1386/1984 - loss 0.03464690 - time (sec): 64.12 - samples/sec: 1807.92 - lr: 0.000014 - momentum: 0.000000
156
+ 2023-10-17 14:30:53,876 epoch 6 - iter 1584/1984 - loss 0.03696921 - time (sec): 73.27 - samples/sec: 1795.05 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 14:31:02,724 epoch 6 - iter 1782/1984 - loss 0.03702742 - time (sec): 82.11 - samples/sec: 1799.57 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 14:31:11,783 epoch 6 - iter 1980/1984 - loss 0.03663836 - time (sec): 91.17 - samples/sec: 1795.02 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 14:31:11,968 ----------------------------------------------------------------------------------------------------
160
+ 2023-10-17 14:31:11,968 EPOCH 6 done: loss 0.0367 - lr: 0.000013
161
+ 2023-10-17 14:31:15,510 DEV : loss 0.19935466349124908 - f1-score (micro avg) 0.7647
162
+ 2023-10-17 14:31:15,540 ----------------------------------------------------------------------------------------------------
163
+ 2023-10-17 14:31:26,185 epoch 7 - iter 198/1984 - loss 0.02511532 - time (sec): 10.64 - samples/sec: 1492.77 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 14:31:36,808 epoch 7 - iter 396/1984 - loss 0.02353593 - time (sec): 21.27 - samples/sec: 1529.14 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 14:31:46,481 epoch 7 - iter 594/1984 - loss 0.02725110 - time (sec): 30.94 - samples/sec: 1579.91 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 14:31:55,734 epoch 7 - iter 792/1984 - loss 0.02742823 - time (sec): 40.19 - samples/sec: 1651.96 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 14:32:05,005 epoch 7 - iter 990/1984 - loss 0.02499010 - time (sec): 49.46 - samples/sec: 1704.82 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 14:32:14,065 epoch 7 - iter 1188/1984 - loss 0.02517202 - time (sec): 58.52 - samples/sec: 1716.61 - lr: 0.000011 - momentum: 0.000000
169
+ 2023-10-17 14:32:22,809 epoch 7 - iter 1386/1984 - loss 0.02492481 - time (sec): 67.27 - samples/sec: 1727.30 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-10-17 14:32:31,645 epoch 7 - iter 1584/1984 - loss 0.02477025 - time (sec): 76.10 - samples/sec: 1737.03 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-10-17 14:32:40,907 epoch 7 - iter 1782/1984 - loss 0.02572704 - time (sec): 85.37 - samples/sec: 1736.07 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 14:32:49,995 epoch 7 - iter 1980/1984 - loss 0.02491306 - time (sec): 94.45 - samples/sec: 1732.11 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 14:32:50,174 ----------------------------------------------------------------------------------------------------
174
+ 2023-10-17 14:32:50,174 EPOCH 7 done: loss 0.0250 - lr: 0.000010
175
+ 2023-10-17 14:32:53,683 DEV : loss 0.20214584469795227 - f1-score (micro avg) 0.7623
176
+ 2023-10-17 14:32:53,716 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-17 14:33:02,786 epoch 8 - iter 198/1984 - loss 0.01165556 - time (sec): 9.07 - samples/sec: 1793.90 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 14:33:11,842 epoch 8 - iter 396/1984 - loss 0.01479537 - time (sec): 18.13 - samples/sec: 1782.05 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 14:33:20,675 epoch 8 - iter 594/1984 - loss 0.01696006 - time (sec): 26.96 - samples/sec: 1809.49 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-17 14:33:29,808 epoch 8 - iter 792/1984 - loss 0.01648117 - time (sec): 36.09 - samples/sec: 1809.14 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 14:33:38,982 epoch 8 - iter 990/1984 - loss 0.01744306 - time (sec): 45.27 - samples/sec: 1798.74 - lr: 0.000008 - momentum: 0.000000
182
+ 2023-10-17 14:33:48,084 epoch 8 - iter 1188/1984 - loss 0.01772065 - time (sec): 54.37 - samples/sec: 1786.89 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 14:33:57,336 epoch 8 - iter 1386/1984 - loss 0.01868319 - time (sec): 63.62 - samples/sec: 1786.50 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-17 14:34:06,576 epoch 8 - iter 1584/1984 - loss 0.01859987 - time (sec): 72.86 - samples/sec: 1790.02 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-10-17 14:34:17,150 epoch 8 - iter 1782/1984 - loss 0.01846760 - time (sec): 83.43 - samples/sec: 1753.15 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 14:34:27,610 epoch 8 - iter 1980/1984 - loss 0.01868024 - time (sec): 93.89 - samples/sec: 1742.66 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-17 14:34:27,831 ----------------------------------------------------------------------------------------------------
188
+ 2023-10-17 14:34:27,831 EPOCH 8 done: loss 0.0187 - lr: 0.000007
189
+ 2023-10-17 14:34:31,286 DEV : loss 0.22779549658298492 - f1-score (micro avg) 0.7628
190
+ 2023-10-17 14:34:31,310 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-17 14:34:41,901 epoch 9 - iter 198/1984 - loss 0.01906451 - time (sec): 10.59 - samples/sec: 1589.50 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-17 14:34:51,675 epoch 9 - iter 396/1984 - loss 0.01555467 - time (sec): 20.36 - samples/sec: 1658.32 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-10-17 14:35:01,204 epoch 9 - iter 594/1984 - loss 0.01521979 - time (sec): 29.89 - samples/sec: 1678.39 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-17 14:35:10,391 epoch 9 - iter 792/1984 - loss 0.01491455 - time (sec): 39.08 - samples/sec: 1697.20 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 14:35:19,561 epoch 9 - iter 990/1984 - loss 0.01387345 - time (sec): 48.25 - samples/sec: 1725.91 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-17 14:35:28,646 epoch 9 - iter 1188/1984 - loss 0.01431168 - time (sec): 57.33 - samples/sec: 1728.81 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-17 14:35:37,776 epoch 9 - iter 1386/1984 - loss 0.01452248 - time (sec): 66.46 - samples/sec: 1739.53 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 14:35:46,951 epoch 9 - iter 1584/1984 - loss 0.01440257 - time (sec): 75.64 - samples/sec: 1742.47 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-17 14:35:56,334 epoch 9 - iter 1782/1984 - loss 0.01404208 - time (sec): 85.02 - samples/sec: 1738.26 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-17 14:36:05,792 epoch 9 - iter 1980/1984 - loss 0.01392469 - time (sec): 94.48 - samples/sec: 1732.59 - lr: 0.000003 - momentum: 0.000000
201
+ 2023-10-17 14:36:05,980 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-17 14:36:05,980 EPOCH 9 done: loss 0.0140 - lr: 0.000003
203
+ 2023-10-17 14:36:09,397 DEV : loss 0.23514559864997864 - f1-score (micro avg) 0.7616
204
+ 2023-10-17 14:36:09,418 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-17 14:36:18,647 epoch 10 - iter 198/1984 - loss 0.00949880 - time (sec): 9.23 - samples/sec: 1832.95 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-10-17 14:36:27,628 epoch 10 - iter 396/1984 - loss 0.00775092 - time (sec): 18.21 - samples/sec: 1832.79 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-17 14:36:36,789 epoch 10 - iter 594/1984 - loss 0.00839411 - time (sec): 27.37 - samples/sec: 1833.08 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 14:36:45,901 epoch 10 - iter 792/1984 - loss 0.00870864 - time (sec): 36.48 - samples/sec: 1796.51 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 14:36:55,161 epoch 10 - iter 990/1984 - loss 0.00887633 - time (sec): 45.74 - samples/sec: 1804.60 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 14:37:04,293 epoch 10 - iter 1188/1984 - loss 0.00855463 - time (sec): 54.87 - samples/sec: 1791.29 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 14:37:13,457 epoch 10 - iter 1386/1984 - loss 0.00875616 - time (sec): 64.04 - samples/sec: 1789.47 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 14:37:22,534 epoch 10 - iter 1584/1984 - loss 0.00856302 - time (sec): 73.11 - samples/sec: 1784.41 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 14:37:31,887 epoch 10 - iter 1782/1984 - loss 0.00882088 - time (sec): 82.47 - samples/sec: 1770.65 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 14:37:41,392 epoch 10 - iter 1980/1984 - loss 0.00856634 - time (sec): 91.97 - samples/sec: 1780.44 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-17 14:37:41,566 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-17 14:37:41,566 EPOCH 10 done: loss 0.0086 - lr: 0.000000
217
+ 2023-10-17 14:37:44,996 DEV : loss 0.23482687771320343 - f1-score (micro avg) 0.7613
218
+ 2023-10-17 14:37:45,439 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-17 14:37:45,440 Loading model from best epoch ...
220
+ 2023-10-17 14:37:48,035 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
221
+ 2023-10-17 14:37:50,922
222
+ Results:
223
+ - F-score (micro) 0.8006
224
+ - F-score (macro) 0.7098
225
+ - Accuracy 0.6849
226
+
227
+ By class:
228
+ precision recall f1-score support
229
+
230
+ LOC 0.8376 0.8901 0.8631 655
231
+ PER 0.7311 0.7803 0.7549 223
232
+ ORG 0.6087 0.4409 0.5114 127
233
+
234
+ micro avg 0.7924 0.8090 0.8006 1005
235
+ macro avg 0.7258 0.7038 0.7098 1005
236
+ weighted avg 0.7851 0.8090 0.7946 1005
237
+
238
+ 2023-10-17 14:37:50,922 ----------------------------------------------------------------------------------------------------