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hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3

Token Classification
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English
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.1697543860.4c6324b99746.1159.10 +3 -0
  5. test.tsv +0 -0
  6. training.log +236 -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 11:58:57 0.0000 0.4127 0.0612 0.7696 0.7468 0.7580 0.6211
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+ 2 12:00:15 0.0000 0.0749 0.0584 0.7529 0.8228 0.7863 0.6588
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+ 3 12:01:33 0.0000 0.0480 0.0665 0.7582 0.7806 0.7692 0.6401
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+ 4 12:02:50 0.0000 0.0304 0.0793 0.7705 0.7932 0.7817 0.6528
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+ 5 12:04:06 0.0000 0.0228 0.0969 0.7984 0.8186 0.8083 0.6953
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+ 6 12:05:22 0.0000 0.0162 0.1125 0.7881 0.7848 0.7865 0.6667
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+ 7 12:06:37 0.0000 0.0114 0.1195 0.7884 0.8017 0.7950 0.6738
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+ 8 12:07:52 0.0000 0.0070 0.1221 0.7751 0.8143 0.7942 0.6796
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+ 9 12:09:07 0.0000 0.0045 0.1310 0.7760 0.8186 0.7967 0.6783
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+ 10 12:10:22 0.0000 0.0033 0.1353 0.7640 0.8059 0.7844 0.6655
runs/events.out.tfevents.1697543860.4c6324b99746.1159.10 ADDED
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+ version https://git-lfs.github.com/spec/v1
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 11:57:40,102 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,104 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 11:57:40,105 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,105 MultiCorpus: 6183 train + 680 dev + 2113 test sentences
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+ - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator
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+ 2023-10-17 11:57:40,105 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,105 Train: 6183 sentences
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+ 2023-10-17 11:57:40,105 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 11:57:40,105 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,105 Training Params:
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+ 2023-10-17 11:57:40,105 - learning_rate: "3e-05"
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+ 2023-10-17 11:57:40,105 - mini_batch_size: "8"
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+ 2023-10-17 11:57:40,105 - max_epochs: "10"
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+ 2023-10-17 11:57:40,105 - shuffle: "True"
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+ 2023-10-17 11:57:40,105 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,105 Plugins:
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+ 2023-10-17 11:57:40,106 - TensorboardLogger
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+ 2023-10-17 11:57:40,106 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 11:57:40,106 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,106 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 11:57:40,106 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 11:57:40,106 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,106 Computation:
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+ 2023-10-17 11:57:40,106 - compute on device: cuda:0
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+ 2023-10-17 11:57:40,106 - embedding storage: none
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+ 2023-10-17 11:57:40,106 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,106 Model training base path: "hmbench-topres19th/en-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-17 11:57:40,106 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,106 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:40,106 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 11:57:47,156 epoch 1 - iter 77/773 - loss 2.62934140 - time (sec): 7.05 - samples/sec: 1746.12 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 11:57:54,316 epoch 1 - iter 154/773 - loss 1.60891642 - time (sec): 14.21 - samples/sec: 1734.16 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 11:58:01,542 epoch 1 - iter 231/773 - loss 1.12039514 - time (sec): 21.43 - samples/sec: 1746.74 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 11:58:08,974 epoch 1 - iter 308/773 - loss 0.86475702 - time (sec): 28.87 - samples/sec: 1736.86 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 11:58:16,969 epoch 1 - iter 385/773 - loss 0.71122671 - time (sec): 36.86 - samples/sec: 1696.64 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 11:58:24,770 epoch 1 - iter 462/773 - loss 0.62105387 - time (sec): 44.66 - samples/sec: 1659.62 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 11:58:31,943 epoch 1 - iter 539/773 - loss 0.55330225 - time (sec): 51.83 - samples/sec: 1650.46 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 11:58:38,992 epoch 1 - iter 616/773 - loss 0.49625437 - time (sec): 58.88 - samples/sec: 1666.43 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 11:58:47,296 epoch 1 - iter 693/773 - loss 0.44972124 - time (sec): 67.19 - samples/sec: 1655.14 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 11:58:54,547 epoch 1 - iter 770/773 - loss 0.41433052 - time (sec): 74.44 - samples/sec: 1661.92 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 11:58:54,816 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:58:54,816 EPOCH 1 done: loss 0.4127 - lr: 0.000030
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+ 2023-10-17 11:58:57,267 DEV : loss 0.06118296831846237 - f1-score (micro avg) 0.758
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+ 2023-10-17 11:58:57,297 saving best model
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+ 2023-10-17 11:58:57,894 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:59:05,101 epoch 2 - iter 77/773 - loss 0.08295422 - time (sec): 7.20 - samples/sec: 1645.87 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 11:59:13,021 epoch 2 - iter 154/773 - loss 0.07699183 - time (sec): 15.12 - samples/sec: 1586.84 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 11:59:20,476 epoch 2 - iter 231/773 - loss 0.08084633 - time (sec): 22.58 - samples/sec: 1613.56 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 11:59:27,517 epoch 2 - iter 308/773 - loss 0.08317390 - time (sec): 29.62 - samples/sec: 1670.58 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 11:59:34,890 epoch 2 - iter 385/773 - loss 0.07884677 - time (sec): 36.99 - samples/sec: 1662.50 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 11:59:42,308 epoch 2 - iter 462/773 - loss 0.07771188 - time (sec): 44.41 - samples/sec: 1677.87 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 11:59:49,183 epoch 2 - iter 539/773 - loss 0.07602078 - time (sec): 51.29 - samples/sec: 1687.25 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 11:59:56,592 epoch 2 - iter 616/773 - loss 0.07570427 - time (sec): 58.70 - samples/sec: 1671.75 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 12:00:04,319 epoch 2 - iter 693/773 - loss 0.07508718 - time (sec): 66.42 - samples/sec: 1686.70 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 12:00:11,409 epoch 2 - iter 770/773 - loss 0.07433537 - time (sec): 73.51 - samples/sec: 1684.91 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 12:00:11,700 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:00:11,701 EPOCH 2 done: loss 0.0749 - lr: 0.000027
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+ 2023-10-17 12:00:14,996 DEV : loss 0.05837954208254814 - f1-score (micro avg) 0.7863
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+ 2023-10-17 12:00:15,033 saving best model
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+ 2023-10-17 12:00:16,970 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:00:24,464 epoch 3 - iter 77/773 - loss 0.04678876 - time (sec): 7.49 - samples/sec: 1585.33 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 12:00:31,494 epoch 3 - iter 154/773 - loss 0.04394092 - time (sec): 14.52 - samples/sec: 1587.83 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 12:00:38,728 epoch 3 - iter 231/773 - loss 0.04633664 - time (sec): 21.75 - samples/sec: 1633.15 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 12:00:46,112 epoch 3 - iter 308/773 - loss 0.05065715 - time (sec): 29.14 - samples/sec: 1663.43 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 12:00:53,391 epoch 3 - iter 385/773 - loss 0.04995056 - time (sec): 36.42 - samples/sec: 1682.30 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 12:01:00,605 epoch 3 - iter 462/773 - loss 0.04932353 - time (sec): 43.63 - samples/sec: 1689.86 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 12:01:08,272 epoch 3 - iter 539/773 - loss 0.04798072 - time (sec): 51.30 - samples/sec: 1679.29 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 12:01:16,193 epoch 3 - iter 616/773 - loss 0.04702234 - time (sec): 59.22 - samples/sec: 1664.05 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 12:01:23,550 epoch 3 - iter 693/773 - loss 0.04733206 - time (sec): 66.58 - samples/sec: 1670.05 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 12:01:30,536 epoch 3 - iter 770/773 - loss 0.04801480 - time (sec): 73.56 - samples/sec: 1684.20 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 12:01:30,791 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:01:30,791 EPOCH 3 done: loss 0.0480 - lr: 0.000023
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+ 2023-10-17 12:01:33,682 DEV : loss 0.06652045249938965 - f1-score (micro avg) 0.7692
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+ 2023-10-17 12:01:33,712 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:01:40,804 epoch 4 - iter 77/773 - loss 0.02865288 - time (sec): 7.09 - samples/sec: 1830.33 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 12:01:47,995 epoch 4 - iter 154/773 - loss 0.02826719 - time (sec): 14.28 - samples/sec: 1846.02 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 12:01:55,594 epoch 4 - iter 231/773 - loss 0.03000062 - time (sec): 21.88 - samples/sec: 1747.28 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 12:02:03,346 epoch 4 - iter 308/773 - loss 0.02857205 - time (sec): 29.63 - samples/sec: 1702.07 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 12:02:10,474 epoch 4 - iter 385/773 - loss 0.03006392 - time (sec): 36.76 - samples/sec: 1704.74 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 12:02:17,821 epoch 4 - iter 462/773 - loss 0.03064035 - time (sec): 44.11 - samples/sec: 1698.51 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 12:02:25,847 epoch 4 - iter 539/773 - loss 0.03032994 - time (sec): 52.13 - samples/sec: 1662.31 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 12:02:33,216 epoch 4 - iter 616/773 - loss 0.02951093 - time (sec): 59.50 - samples/sec: 1652.69 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 12:02:40,366 epoch 4 - iter 693/773 - loss 0.03013193 - time (sec): 66.65 - samples/sec: 1671.24 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 12:02:47,380 epoch 4 - iter 770/773 - loss 0.03053470 - time (sec): 73.67 - samples/sec: 1679.68 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 12:02:47,663 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 12:02:47,664 EPOCH 4 done: loss 0.0304 - lr: 0.000020
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+ 2023-10-17 12:02:50,759 DEV : loss 0.07929900288581848 - f1-score (micro avg) 0.7817
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+ 2023-10-17 12:02:50,793 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:02:57,890 epoch 5 - iter 77/773 - loss 0.02217696 - time (sec): 7.10 - samples/sec: 1726.68 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 12:03:05,723 epoch 5 - iter 154/773 - loss 0.01690662 - time (sec): 14.93 - samples/sec: 1715.44 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 12:03:12,550 epoch 5 - iter 231/773 - loss 0.02055394 - time (sec): 21.75 - samples/sec: 1703.10 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 12:03:19,440 epoch 5 - iter 308/773 - loss 0.02201265 - time (sec): 28.65 - samples/sec: 1705.46 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 12:03:26,685 epoch 5 - iter 385/773 - loss 0.02194969 - time (sec): 35.89 - samples/sec: 1699.08 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 12:03:34,360 epoch 5 - iter 462/773 - loss 0.02236795 - time (sec): 43.57 - samples/sec: 1683.10 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 12:03:42,008 epoch 5 - iter 539/773 - loss 0.02204855 - time (sec): 51.21 - samples/sec: 1682.44 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 12:03:49,369 epoch 5 - iter 616/773 - loss 0.02258293 - time (sec): 58.57 - samples/sec: 1688.36 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 12:03:56,358 epoch 5 - iter 693/773 - loss 0.02279416 - time (sec): 65.56 - samples/sec: 1697.30 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 12:04:03,656 epoch 5 - iter 770/773 - loss 0.02288678 - time (sec): 72.86 - samples/sec: 1695.26 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 12:04:03,956 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-17 12:04:03,957 EPOCH 5 done: loss 0.0228 - lr: 0.000017
144
+ 2023-10-17 12:04:06,846 DEV : loss 0.09690136462450027 - f1-score (micro avg) 0.8083
145
+ 2023-10-17 12:04:06,878 saving best model
146
+ 2023-10-17 12:04:08,284 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 12:04:15,535 epoch 6 - iter 77/773 - loss 0.01574627 - time (sec): 7.25 - samples/sec: 1634.93 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 12:04:22,680 epoch 6 - iter 154/773 - loss 0.01503167 - time (sec): 14.39 - samples/sec: 1606.59 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 12:04:30,417 epoch 6 - iter 231/773 - loss 0.01525419 - time (sec): 22.13 - samples/sec: 1619.30 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 12:04:37,501 epoch 6 - iter 308/773 - loss 0.01604884 - time (sec): 29.21 - samples/sec: 1681.45 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 12:04:44,519 epoch 6 - iter 385/773 - loss 0.01647546 - time (sec): 36.23 - samples/sec: 1699.69 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 12:04:51,629 epoch 6 - iter 462/773 - loss 0.01649118 - time (sec): 43.34 - samples/sec: 1703.29 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 12:04:58,910 epoch 6 - iter 539/773 - loss 0.01640551 - time (sec): 50.62 - samples/sec: 1708.22 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 12:05:06,068 epoch 6 - iter 616/773 - loss 0.01616345 - time (sec): 57.78 - samples/sec: 1709.61 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 12:05:12,936 epoch 6 - iter 693/773 - loss 0.01661672 - time (sec): 64.65 - samples/sec: 1723.18 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 12:05:19,659 epoch 6 - iter 770/773 - loss 0.01623904 - time (sec): 71.37 - samples/sec: 1733.95 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 12:05:19,954 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 12:05:19,954 EPOCH 6 done: loss 0.0162 - lr: 0.000013
159
+ 2023-10-17 12:05:22,876 DEV : loss 0.11253025382757187 - f1-score (micro avg) 0.7865
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+ 2023-10-17 12:05:22,905 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:05:29,670 epoch 7 - iter 77/773 - loss 0.01369959 - time (sec): 6.76 - samples/sec: 1740.54 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 12:05:36,767 epoch 7 - iter 154/773 - loss 0.01009396 - time (sec): 13.86 - samples/sec: 1732.16 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 12:05:43,890 epoch 7 - iter 231/773 - loss 0.00886952 - time (sec): 20.98 - samples/sec: 1749.26 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 12:05:50,813 epoch 7 - iter 308/773 - loss 0.00877763 - time (sec): 27.91 - samples/sec: 1739.80 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 12:05:57,700 epoch 7 - iter 385/773 - loss 0.00929459 - time (sec): 34.79 - samples/sec: 1733.43 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 12:06:04,692 epoch 7 - iter 462/773 - loss 0.00883992 - time (sec): 41.79 - samples/sec: 1743.78 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 12:06:12,154 epoch 7 - iter 539/773 - loss 0.00966097 - time (sec): 49.25 - samples/sec: 1769.92 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 12:06:19,005 epoch 7 - iter 616/773 - loss 0.00980374 - time (sec): 56.10 - samples/sec: 1767.70 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 12:06:25,948 epoch 7 - iter 693/773 - loss 0.01034364 - time (sec): 63.04 - samples/sec: 1764.13 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 12:06:33,616 epoch 7 - iter 770/773 - loss 0.01133078 - time (sec): 70.71 - samples/sec: 1747.32 - lr: 0.000010 - momentum: 0.000000
171
+ 2023-10-17 12:06:33,975 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-17 12:06:33,975 EPOCH 7 done: loss 0.0114 - lr: 0.000010
173
+ 2023-10-17 12:06:37,067 DEV : loss 0.11950261145830154 - f1-score (micro avg) 0.795
174
+ 2023-10-17 12:06:37,096 ----------------------------------------------------------------------------------------------------
175
+ 2023-10-17 12:06:44,893 epoch 8 - iter 77/773 - loss 0.00625140 - time (sec): 7.79 - samples/sec: 1497.23 - lr: 0.000010 - momentum: 0.000000
176
+ 2023-10-17 12:06:51,920 epoch 8 - iter 154/773 - loss 0.00852418 - time (sec): 14.82 - samples/sec: 1633.31 - lr: 0.000009 - momentum: 0.000000
177
+ 2023-10-17 12:06:58,955 epoch 8 - iter 231/773 - loss 0.00806622 - time (sec): 21.86 - samples/sec: 1629.72 - lr: 0.000009 - momentum: 0.000000
178
+ 2023-10-17 12:07:05,728 epoch 8 - iter 308/773 - loss 0.00853359 - time (sec): 28.63 - samples/sec: 1652.31 - lr: 0.000009 - momentum: 0.000000
179
+ 2023-10-17 12:07:12,918 epoch 8 - iter 385/773 - loss 0.00786367 - time (sec): 35.82 - samples/sec: 1688.05 - lr: 0.000008 - momentum: 0.000000
180
+ 2023-10-17 12:07:20,139 epoch 8 - iter 462/773 - loss 0.00702458 - time (sec): 43.04 - samples/sec: 1715.38 - lr: 0.000008 - momentum: 0.000000
181
+ 2023-10-17 12:07:27,147 epoch 8 - iter 539/773 - loss 0.00704735 - time (sec): 50.05 - samples/sec: 1722.89 - lr: 0.000008 - momentum: 0.000000
182
+ 2023-10-17 12:07:34,445 epoch 8 - iter 616/773 - loss 0.00717121 - time (sec): 57.35 - samples/sec: 1727.76 - lr: 0.000007 - momentum: 0.000000
183
+ 2023-10-17 12:07:41,580 epoch 8 - iter 693/773 - loss 0.00713439 - time (sec): 64.48 - samples/sec: 1724.76 - lr: 0.000007 - momentum: 0.000000
184
+ 2023-10-17 12:07:49,000 epoch 8 - iter 770/773 - loss 0.00703776 - time (sec): 71.90 - samples/sec: 1721.06 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-10-17 12:07:49,276 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-17 12:07:49,276 EPOCH 8 done: loss 0.0070 - lr: 0.000007
187
+ 2023-10-17 12:07:52,369 DEV : loss 0.12206191569566727 - f1-score (micro avg) 0.7942
188
+ 2023-10-17 12:07:52,400 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 12:07:59,464 epoch 9 - iter 77/773 - loss 0.00424893 - time (sec): 7.06 - samples/sec: 1783.62 - lr: 0.000006 - momentum: 0.000000
190
+ 2023-10-17 12:08:06,976 epoch 9 - iter 154/773 - loss 0.00355699 - time (sec): 14.57 - samples/sec: 1795.43 - lr: 0.000006 - momentum: 0.000000
191
+ 2023-10-17 12:08:13,824 epoch 9 - iter 231/773 - loss 0.00385022 - time (sec): 21.42 - samples/sec: 1774.45 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-17 12:08:20,704 epoch 9 - iter 308/773 - loss 0.00363732 - time (sec): 28.30 - samples/sec: 1791.32 - lr: 0.000005 - momentum: 0.000000
193
+ 2023-10-17 12:08:27,824 epoch 9 - iter 385/773 - loss 0.00344109 - time (sec): 35.42 - samples/sec: 1778.24 - lr: 0.000005 - momentum: 0.000000
194
+ 2023-10-17 12:08:34,742 epoch 9 - iter 462/773 - loss 0.00372679 - time (sec): 42.34 - samples/sec: 1761.91 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 12:08:41,980 epoch 9 - iter 539/773 - loss 0.00415038 - time (sec): 49.58 - samples/sec: 1752.63 - lr: 0.000004 - momentum: 0.000000
196
+ 2023-10-17 12:08:49,436 epoch 9 - iter 616/773 - loss 0.00418043 - time (sec): 57.03 - samples/sec: 1746.10 - lr: 0.000004 - momentum: 0.000000
197
+ 2023-10-17 12:08:56,848 epoch 9 - iter 693/773 - loss 0.00417050 - time (sec): 64.45 - samples/sec: 1747.90 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 12:09:04,074 epoch 9 - iter 770/773 - loss 0.00447844 - time (sec): 71.67 - samples/sec: 1726.86 - lr: 0.000003 - momentum: 0.000000
199
+ 2023-10-17 12:09:04,352 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-17 12:09:04,353 EPOCH 9 done: loss 0.0045 - lr: 0.000003
201
+ 2023-10-17 12:09:07,443 DEV : loss 0.13099931180477142 - f1-score (micro avg) 0.7967
202
+ 2023-10-17 12:09:07,475 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 12:09:14,596 epoch 10 - iter 77/773 - loss 0.00280890 - time (sec): 7.12 - samples/sec: 1699.23 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-17 12:09:22,633 epoch 10 - iter 154/773 - loss 0.00194984 - time (sec): 15.16 - samples/sec: 1701.26 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-10-17 12:09:29,624 epoch 10 - iter 231/773 - loss 0.00196051 - time (sec): 22.15 - samples/sec: 1728.94 - lr: 0.000002 - momentum: 0.000000
206
+ 2023-10-17 12:09:36,424 epoch 10 - iter 308/773 - loss 0.00257133 - time (sec): 28.95 - samples/sec: 1732.49 - lr: 0.000002 - momentum: 0.000000
207
+ 2023-10-17 12:09:43,620 epoch 10 - iter 385/773 - loss 0.00314051 - time (sec): 36.14 - samples/sec: 1741.09 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 12:09:50,669 epoch 10 - iter 462/773 - loss 0.00334541 - time (sec): 43.19 - samples/sec: 1755.01 - lr: 0.000001 - momentum: 0.000000
209
+ 2023-10-17 12:09:57,728 epoch 10 - iter 539/773 - loss 0.00324725 - time (sec): 50.25 - samples/sec: 1753.88 - lr: 0.000001 - momentum: 0.000000
210
+ 2023-10-17 12:10:04,717 epoch 10 - iter 616/773 - loss 0.00353126 - time (sec): 57.24 - samples/sec: 1728.44 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 12:10:11,916 epoch 10 - iter 693/773 - loss 0.00325470 - time (sec): 64.44 - samples/sec: 1736.98 - lr: 0.000000 - momentum: 0.000000
212
+ 2023-10-17 12:10:19,023 epoch 10 - iter 770/773 - loss 0.00330436 - time (sec): 71.55 - samples/sec: 1730.28 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 12:10:19,309 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-17 12:10:19,310 EPOCH 10 done: loss 0.0033 - lr: 0.000000
215
+ 2023-10-17 12:10:22,291 DEV : loss 0.1352890431880951 - f1-score (micro avg) 0.7844
216
+ 2023-10-17 12:10:22,951 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 12:10:22,953 Loading model from best epoch ...
218
+ 2023-10-17 12:10:25,332 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET
219
+ 2023-10-17 12:10:33,825
220
+ Results:
221
+ - F-score (micro) 0.8057
222
+ - F-score (macro) 0.7196
223
+ - Accuracy 0.6904
224
+
225
+ By class:
226
+ precision recall f1-score support
227
+
228
+ LOC 0.8495 0.8414 0.8455 946
229
+ BUILDING 0.6795 0.5730 0.6217 185
230
+ STREET 0.7255 0.6607 0.6916 56
231
+
232
+ micro avg 0.8208 0.7911 0.8057 1187
233
+ macro avg 0.7515 0.6917 0.7196 1187
234
+ weighted avg 0.8172 0.7911 0.8033 1187
235
+
236
+ 2023-10-17 12:10:33,825 ----------------------------------------------------------------------------------------------------