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hmbench-newseye-de-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3

Token Classification
Flair
PyTorch
German
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. test.tsv +0 -0
  5. training.log +242 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:4a3efa1f30e0725961ed2640a2eaed44a89a7d0d34ad4cc27a89e3d63fdeb988
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+ size 443323527
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 16:41:25 0.0000 0.4047 0.1570 0.1951 0.5606 0.2895 0.1694
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+ 2 16:44:44 0.0000 0.1487 0.1409 0.2557 0.4886 0.3357 0.2028
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+ 3 16:48:03 0.0000 0.0970 0.2011 0.2378 0.5795 0.3372 0.2045
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+ 4 16:51:22 0.0000 0.0687 0.3041 0.2773 0.6212 0.3834 0.2391
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+ 5 16:54:41 0.0000 0.0501 0.3422 0.2697 0.6610 0.3831 0.2384
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+ 6 16:58:00 0.0000 0.0367 0.4011 0.2628 0.6402 0.3727 0.2306
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+ 7 17:01:19 0.0000 0.0268 0.3619 0.3019 0.6117 0.4043 0.2545
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+ 8 17:04:38 0.0000 0.0200 0.4061 0.3045 0.6212 0.4087 0.2583
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+ 9 17:07:59 0.0000 0.0136 0.5201 0.2586 0.6553 0.3708 0.2284
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+ 10 17:11:18 0.0000 0.0096 0.4862 0.2799 0.6515 0.3916 0.2445
test.tsv ADDED
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training.log ADDED
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+ 2023-10-15 16:38:09,257 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
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+ (embeddings): BertEmbeddings(
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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): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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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): BertSelfOutput(
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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): BertIntermediate(
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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): BertOutput(
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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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+ (pooler): BertPooler(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (activation): Tanh()
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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=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-15 16:38:09,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences
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+ - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator
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+ 2023-10-15 16:38:09,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 Train: 20847 sentences
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+ 2023-10-15 16:38:09,258 (train_with_dev=False, train_with_test=False)
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+ 2023-10-15 16:38:09,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 Training Params:
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+ 2023-10-15 16:38:09,258 - learning_rate: "3e-05"
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+ 2023-10-15 16:38:09,258 - mini_batch_size: "8"
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+ 2023-10-15 16:38:09,258 - max_epochs: "10"
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+ 2023-10-15 16:38:09,258 - shuffle: "True"
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+ 2023-10-15 16:38:09,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 Plugins:
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+ 2023-10-15 16:38:09,258 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-15 16:38:09,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-15 16:38:09,258 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-15 16:38:09,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 Computation:
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+ 2023-10-15 16:38:09,258 - compute on device: cuda:0
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+ 2023-10-15 16:38:09,258 - embedding storage: none
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+ 2023-10-15 16:38:09,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,258 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-15 16:38:09,259 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:09,259 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:38:28,045 epoch 1 - iter 260/2606 - loss 1.78304728 - time (sec): 18.79 - samples/sec: 1856.12 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-15 16:38:46,594 epoch 1 - iter 520/2606 - loss 1.15216820 - time (sec): 37.33 - samples/sec: 1898.75 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-15 16:39:05,821 epoch 1 - iter 780/2606 - loss 0.86568725 - time (sec): 56.56 - samples/sec: 1910.94 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-15 16:39:25,235 epoch 1 - iter 1040/2606 - loss 0.70886674 - time (sec): 75.98 - samples/sec: 1914.21 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-15 16:39:44,071 epoch 1 - iter 1300/2606 - loss 0.61350901 - time (sec): 94.81 - samples/sec: 1924.89 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-15 16:40:03,717 epoch 1 - iter 1560/2606 - loss 0.54047028 - time (sec): 114.46 - samples/sec: 1940.17 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-15 16:40:22,096 epoch 1 - iter 1820/2606 - loss 0.49291843 - time (sec): 132.84 - samples/sec: 1946.58 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 16:40:40,387 epoch 1 - iter 2080/2606 - loss 0.45739180 - time (sec): 151.13 - samples/sec: 1951.82 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 16:40:59,388 epoch 1 - iter 2340/2606 - loss 0.43100594 - time (sec): 170.13 - samples/sec: 1932.42 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 16:41:18,986 epoch 1 - iter 2600/2606 - loss 0.40515264 - time (sec): 189.73 - samples/sec: 1933.21 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-15 16:41:19,367 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:41:19,367 EPOCH 1 done: loss 0.4047 - lr: 0.000030
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+ 2023-10-15 16:41:25,180 DEV : loss 0.1570487767457962 - f1-score (micro avg) 0.2895
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+ 2023-10-15 16:41:25,209 saving best model
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+ 2023-10-15 16:41:25,591 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:41:43,895 epoch 2 - iter 260/2606 - loss 0.20098129 - time (sec): 18.30 - samples/sec: 1820.25 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-15 16:42:02,865 epoch 2 - iter 520/2606 - loss 0.18249185 - time (sec): 37.27 - samples/sec: 1887.05 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-15 16:42:21,212 epoch 2 - iter 780/2606 - loss 0.17192597 - time (sec): 55.62 - samples/sec: 1902.07 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-15 16:42:40,345 epoch 2 - iter 1040/2606 - loss 0.16132827 - time (sec): 74.75 - samples/sec: 1912.44 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-15 16:42:58,644 epoch 2 - iter 1300/2606 - loss 0.16050052 - time (sec): 93.05 - samples/sec: 1920.11 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-15 16:43:18,524 epoch 2 - iter 1560/2606 - loss 0.15792990 - time (sec): 112.93 - samples/sec: 1931.30 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-15 16:43:37,445 epoch 2 - iter 1820/2606 - loss 0.15662757 - time (sec): 131.85 - samples/sec: 1927.40 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-15 16:43:56,056 epoch 2 - iter 2080/2606 - loss 0.15414219 - time (sec): 150.46 - samples/sec: 1930.09 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 16:44:15,590 epoch 2 - iter 2340/2606 - loss 0.15078285 - time (sec): 170.00 - samples/sec: 1928.67 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 16:44:35,237 epoch 2 - iter 2600/2606 - loss 0.14899030 - time (sec): 189.64 - samples/sec: 1930.76 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 16:44:35,739 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:44:35,739 EPOCH 2 done: loss 0.1487 - lr: 0.000027
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+ 2023-10-15 16:44:44,918 DEV : loss 0.14093472063541412 - f1-score (micro avg) 0.3357
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+ 2023-10-15 16:44:44,945 saving best model
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+ 2023-10-15 16:44:45,501 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:45:03,303 epoch 3 - iter 260/2606 - loss 0.10491536 - time (sec): 17.80 - samples/sec: 1883.64 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-15 16:45:21,796 epoch 3 - iter 520/2606 - loss 0.10122591 - time (sec): 36.29 - samples/sec: 1890.96 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-15 16:45:42,689 epoch 3 - iter 780/2606 - loss 0.09961218 - time (sec): 57.18 - samples/sec: 1923.87 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-15 16:46:02,554 epoch 3 - iter 1040/2606 - loss 0.09635866 - time (sec): 77.05 - samples/sec: 1914.08 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-15 16:46:21,515 epoch 3 - iter 1300/2606 - loss 0.09838515 - time (sec): 96.01 - samples/sec: 1921.98 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-15 16:46:39,827 epoch 3 - iter 1560/2606 - loss 0.09935068 - time (sec): 114.32 - samples/sec: 1931.86 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-15 16:46:58,868 epoch 3 - iter 1820/2606 - loss 0.09839679 - time (sec): 133.36 - samples/sec: 1931.00 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 16:47:17,465 epoch 3 - iter 2080/2606 - loss 0.09880177 - time (sec): 151.96 - samples/sec: 1925.39 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 16:47:35,970 epoch 3 - iter 2340/2606 - loss 0.09778731 - time (sec): 170.46 - samples/sec: 1926.54 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 16:47:55,086 epoch 3 - iter 2600/2606 - loss 0.09692475 - time (sec): 189.58 - samples/sec: 1934.04 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-15 16:47:55,463 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:47:55,463 EPOCH 3 done: loss 0.0970 - lr: 0.000023
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+ 2023-10-15 16:48:03,673 DEV : loss 0.20114129781723022 - f1-score (micro avg) 0.3372
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+ 2023-10-15 16:48:03,699 saving best model
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+ 2023-10-15 16:48:04,299 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:48:22,997 epoch 4 - iter 260/2606 - loss 0.05906672 - time (sec): 18.70 - samples/sec: 1981.27 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-15 16:48:42,832 epoch 4 - iter 520/2606 - loss 0.06444519 - time (sec): 38.53 - samples/sec: 1949.40 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-15 16:49:01,447 epoch 4 - iter 780/2606 - loss 0.06892697 - time (sec): 57.15 - samples/sec: 1942.37 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-15 16:49:20,367 epoch 4 - iter 1040/2606 - loss 0.06689883 - time (sec): 76.07 - samples/sec: 1935.40 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-15 16:49:39,006 epoch 4 - iter 1300/2606 - loss 0.06644882 - time (sec): 94.71 - samples/sec: 1937.06 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-15 16:49:58,334 epoch 4 - iter 1560/2606 - loss 0.06701638 - time (sec): 114.03 - samples/sec: 1942.46 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 16:50:17,146 epoch 4 - iter 1820/2606 - loss 0.06820439 - time (sec): 132.85 - samples/sec: 1947.78 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 16:50:36,366 epoch 4 - iter 2080/2606 - loss 0.06885462 - time (sec): 152.07 - samples/sec: 1934.72 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 16:50:54,739 epoch 4 - iter 2340/2606 - loss 0.06876316 - time (sec): 170.44 - samples/sec: 1931.29 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-15 16:51:13,995 epoch 4 - iter 2600/2606 - loss 0.06837534 - time (sec): 189.70 - samples/sec: 1933.23 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-15 16:51:14,350 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-15 16:51:14,350 EPOCH 4 done: loss 0.0687 - lr: 0.000020
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+ 2023-10-15 16:51:22,591 DEV : loss 0.30411383509635925 - f1-score (micro avg) 0.3834
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+ 2023-10-15 16:51:22,619 saving best model
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+ 2023-10-15 16:51:23,272 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 16:51:44,016 epoch 5 - iter 260/2606 - loss 0.05158986 - time (sec): 20.74 - samples/sec: 1921.40 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-15 16:52:02,369 epoch 5 - iter 520/2606 - loss 0.05047142 - time (sec): 39.10 - samples/sec: 1943.13 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-15 16:52:20,621 epoch 5 - iter 780/2606 - loss 0.05164843 - time (sec): 57.35 - samples/sec: 1939.10 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-15 16:52:40,125 epoch 5 - iter 1040/2606 - loss 0.04863786 - time (sec): 76.85 - samples/sec: 1950.69 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-15 16:52:59,496 epoch 5 - iter 1300/2606 - loss 0.05114388 - time (sec): 96.22 - samples/sec: 1942.27 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-15 16:53:18,027 epoch 5 - iter 1560/2606 - loss 0.05030163 - time (sec): 114.75 - samples/sec: 1934.20 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-15 16:53:36,276 epoch 5 - iter 1820/2606 - loss 0.04918969 - time (sec): 133.00 - samples/sec: 1927.14 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-15 16:53:55,482 epoch 5 - iter 2080/2606 - loss 0.04969722 - time (sec): 152.21 - samples/sec: 1928.53 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-15 16:54:13,930 epoch 5 - iter 2340/2606 - loss 0.04985016 - time (sec): 170.66 - samples/sec: 1933.53 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-15 16:54:32,872 epoch 5 - iter 2600/2606 - loss 0.05013012 - time (sec): 189.60 - samples/sec: 1933.97 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-15 16:54:33,288 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-15 16:54:33,288 EPOCH 5 done: loss 0.0501 - lr: 0.000017
148
+ 2023-10-15 16:54:41,515 DEV : loss 0.3421619236469269 - f1-score (micro avg) 0.3831
149
+ 2023-10-15 16:54:41,542 ----------------------------------------------------------------------------------------------------
150
+ 2023-10-15 16:55:00,271 epoch 6 - iter 260/2606 - loss 0.02836933 - time (sec): 18.73 - samples/sec: 1912.47 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-15 16:55:19,461 epoch 6 - iter 520/2606 - loss 0.03322309 - time (sec): 37.92 - samples/sec: 1958.69 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-15 16:55:39,110 epoch 6 - iter 780/2606 - loss 0.03417720 - time (sec): 57.57 - samples/sec: 1947.43 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-15 16:55:58,123 epoch 6 - iter 1040/2606 - loss 0.03561005 - time (sec): 76.58 - samples/sec: 1936.64 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-15 16:56:15,638 epoch 6 - iter 1300/2606 - loss 0.03797069 - time (sec): 94.09 - samples/sec: 1934.24 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-15 16:56:35,345 epoch 6 - iter 1560/2606 - loss 0.03933476 - time (sec): 113.80 - samples/sec: 1940.11 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-15 16:56:53,573 epoch 6 - iter 1820/2606 - loss 0.03849459 - time (sec): 132.03 - samples/sec: 1940.99 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-15 16:57:14,002 epoch 6 - iter 2080/2606 - loss 0.03816553 - time (sec): 152.46 - samples/sec: 1927.13 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-15 16:57:32,779 epoch 6 - iter 2340/2606 - loss 0.03680409 - time (sec): 171.24 - samples/sec: 1928.56 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-15 16:57:51,496 epoch 6 - iter 2600/2606 - loss 0.03661753 - time (sec): 189.95 - samples/sec: 1928.30 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-15 16:57:52,077 ----------------------------------------------------------------------------------------------------
161
+ 2023-10-15 16:57:52,077 EPOCH 6 done: loss 0.0367 - lr: 0.000013
162
+ 2023-10-15 16:58:00,492 DEV : loss 0.40107467770576477 - f1-score (micro avg) 0.3727
163
+ 2023-10-15 16:58:00,522 ----------------------------------------------------------------------------------------------------
164
+ 2023-10-15 16:58:18,805 epoch 7 - iter 260/2606 - loss 0.02807878 - time (sec): 18.28 - samples/sec: 1847.43 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-15 16:58:37,987 epoch 7 - iter 520/2606 - loss 0.02940039 - time (sec): 37.46 - samples/sec: 1892.59 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-15 16:58:56,264 epoch 7 - iter 780/2606 - loss 0.02822614 - time (sec): 55.74 - samples/sec: 1876.29 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-15 16:59:15,716 epoch 7 - iter 1040/2606 - loss 0.02631228 - time (sec): 75.19 - samples/sec: 1894.14 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-15 16:59:35,276 epoch 7 - iter 1300/2606 - loss 0.02795012 - time (sec): 94.75 - samples/sec: 1905.97 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-15 16:59:53,587 epoch 7 - iter 1560/2606 - loss 0.02688673 - time (sec): 113.06 - samples/sec: 1916.11 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-15 17:00:12,419 epoch 7 - iter 1820/2606 - loss 0.02730602 - time (sec): 131.90 - samples/sec: 1933.54 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-15 17:00:31,363 epoch 7 - iter 2080/2606 - loss 0.02716500 - time (sec): 150.84 - samples/sec: 1937.16 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-15 17:00:50,648 epoch 7 - iter 2340/2606 - loss 0.02641485 - time (sec): 170.12 - samples/sec: 1931.45 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-15 17:01:10,567 epoch 7 - iter 2600/2606 - loss 0.02685152 - time (sec): 190.04 - samples/sec: 1929.68 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-15 17:01:11,036 ----------------------------------------------------------------------------------------------------
175
+ 2023-10-15 17:01:11,036 EPOCH 7 done: loss 0.0268 - lr: 0.000010
176
+ 2023-10-15 17:01:19,242 DEV : loss 0.36185500025749207 - f1-score (micro avg) 0.4043
177
+ 2023-10-15 17:01:19,273 saving best model
178
+ 2023-10-15 17:01:19,919 ----------------------------------------------------------------------------------------------------
179
+ 2023-10-15 17:01:39,331 epoch 8 - iter 260/2606 - loss 0.01643423 - time (sec): 19.41 - samples/sec: 2026.45 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-10-15 17:01:59,051 epoch 8 - iter 520/2606 - loss 0.01851950 - time (sec): 39.13 - samples/sec: 1976.19 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-10-15 17:02:18,480 epoch 8 - iter 780/2606 - loss 0.01850720 - time (sec): 58.56 - samples/sec: 1959.55 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-15 17:02:36,845 epoch 8 - iter 1040/2606 - loss 0.02011290 - time (sec): 76.92 - samples/sec: 1946.80 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-10-15 17:02:55,283 epoch 8 - iter 1300/2606 - loss 0.01977515 - time (sec): 95.36 - samples/sec: 1940.14 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-15 17:03:14,146 epoch 8 - iter 1560/2606 - loss 0.01909661 - time (sec): 114.22 - samples/sec: 1935.70 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-15 17:03:33,519 epoch 8 - iter 1820/2606 - loss 0.01939229 - time (sec): 133.60 - samples/sec: 1937.25 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-10-15 17:03:51,713 epoch 8 - iter 2080/2606 - loss 0.02001143 - time (sec): 151.79 - samples/sec: 1932.90 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-15 17:04:10,894 epoch 8 - iter 2340/2606 - loss 0.01961925 - time (sec): 170.97 - samples/sec: 1929.77 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-15 17:04:29,158 epoch 8 - iter 2600/2606 - loss 0.02003412 - time (sec): 189.23 - samples/sec: 1934.90 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-10-15 17:04:29,709 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-15 17:04:29,709 EPOCH 8 done: loss 0.0200 - lr: 0.000007
191
+ 2023-10-15 17:04:38,859 DEV : loss 0.4060736298561096 - f1-score (micro avg) 0.4087
192
+ 2023-10-15 17:04:38,892 saving best model
193
+ 2023-10-15 17:04:39,411 ----------------------------------------------------------------------------------------------------
194
+ 2023-10-15 17:04:58,303 epoch 9 - iter 260/2606 - loss 0.01725916 - time (sec): 18.89 - samples/sec: 1881.58 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-15 17:05:17,100 epoch 9 - iter 520/2606 - loss 0.01816425 - time (sec): 37.68 - samples/sec: 1897.84 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-15 17:05:35,858 epoch 9 - iter 780/2606 - loss 0.01585953 - time (sec): 56.44 - samples/sec: 1915.98 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-10-15 17:05:54,651 epoch 9 - iter 1040/2606 - loss 0.01513412 - time (sec): 75.24 - samples/sec: 1919.13 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-15 17:06:13,353 epoch 9 - iter 1300/2606 - loss 0.01568114 - time (sec): 93.94 - samples/sec: 1914.89 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-15 17:06:33,351 epoch 9 - iter 1560/2606 - loss 0.01491052 - time (sec): 113.94 - samples/sec: 1920.33 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-10-15 17:06:52,085 epoch 9 - iter 1820/2606 - loss 0.01453602 - time (sec): 132.67 - samples/sec: 1923.52 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-15 17:07:10,935 epoch 9 - iter 2080/2606 - loss 0.01454802 - time (sec): 151.52 - samples/sec: 1919.01 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-15 17:07:30,661 epoch 9 - iter 2340/2606 - loss 0.01402508 - time (sec): 171.25 - samples/sec: 1920.68 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-10-15 17:07:50,213 epoch 9 - iter 2600/2606 - loss 0.01358373 - time (sec): 190.80 - samples/sec: 1921.95 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-15 17:07:50,566 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-15 17:07:50,567 EPOCH 9 done: loss 0.0136 - lr: 0.000003
206
+ 2023-10-15 17:07:59,619 DEV : loss 0.5201111435890198 - f1-score (micro avg) 0.3708
207
+ 2023-10-15 17:07:59,648 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-15 17:08:18,216 epoch 10 - iter 260/2606 - loss 0.00949472 - time (sec): 18.57 - samples/sec: 1933.16 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-15 17:08:37,149 epoch 10 - iter 520/2606 - loss 0.00956979 - time (sec): 37.50 - samples/sec: 1952.46 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-15 17:08:55,678 epoch 10 - iter 780/2606 - loss 0.00953648 - time (sec): 56.03 - samples/sec: 1970.66 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-15 17:09:14,776 epoch 10 - iter 1040/2606 - loss 0.00918127 - time (sec): 75.13 - samples/sec: 1951.47 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-15 17:09:34,593 epoch 10 - iter 1300/2606 - loss 0.00921523 - time (sec): 94.94 - samples/sec: 1953.32 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-15 17:09:53,789 epoch 10 - iter 1560/2606 - loss 0.00881008 - time (sec): 114.14 - samples/sec: 1942.44 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-15 17:10:12,660 epoch 10 - iter 1820/2606 - loss 0.00913303 - time (sec): 133.01 - samples/sec: 1941.32 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-15 17:10:31,001 epoch 10 - iter 2080/2606 - loss 0.00971053 - time (sec): 151.35 - samples/sec: 1938.83 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-15 17:10:49,857 epoch 10 - iter 2340/2606 - loss 0.00969515 - time (sec): 170.21 - samples/sec: 1933.79 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-15 17:11:09,481 epoch 10 - iter 2600/2606 - loss 0.00962657 - time (sec): 189.83 - samples/sec: 1932.12 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-15 17:11:09,863 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-15 17:11:09,863 EPOCH 10 done: loss 0.0096 - lr: 0.000000
220
+ 2023-10-15 17:11:18,876 DEV : loss 0.4862280786037445 - f1-score (micro avg) 0.3916
221
+ 2023-10-15 17:11:19,325 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-15 17:11:19,326 Loading model from best epoch ...
223
+ 2023-10-15 17:11:20,868 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
224
+ 2023-10-15 17:11:37,464
225
+ Results:
226
+ - F-score (micro) 0.464
227
+ - F-score (macro) 0.3025
228
+ - Accuracy 0.3068
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ LOC 0.4868 0.5783 0.5286 1214
234
+ PER 0.4134 0.5050 0.4546 808
235
+ ORG 0.2706 0.1955 0.2270 353
236
+ HumanProd 0.0000 0.0000 0.0000 15
237
+
238
+ micro avg 0.4380 0.4933 0.4640 2390
239
+ macro avg 0.2927 0.3197 0.3025 2390
240
+ weighted avg 0.4270 0.4933 0.4557 2390
241
+
242
+ 2023-10-15 17:11:37,464 ----------------------------------------------------------------------------------------------------