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hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr5e-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.1697561538.0468bd9609d6.7281.19 +3 -0
  5. test.tsv +0 -0
  6. training.log +240 -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 16:53:50 0.0000 0.4473 0.1332 0.7722 0.7333 0.7523 0.6174
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+ 2 16:55:23 0.0000 0.1249 0.1369 0.7530 0.7755 0.7641 0.6333
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+ 3 16:56:59 0.0000 0.0882 0.1446 0.7134 0.8095 0.7584 0.6290
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+ 4 16:58:36 0.0000 0.0645 0.1931 0.8156 0.7946 0.8050 0.6961
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+ 5 17:00:16 0.0000 0.0474 0.1732 0.7845 0.8122 0.7981 0.6831
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+ 7 17:03:27 0.0000 0.0247 0.1910 0.7908 0.8231 0.8067 0.6986
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+ 8 17:05:04 0.0000 0.0178 0.1929 0.8045 0.8286 0.8164 0.7131
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+ 9 17:06:38 0.0000 0.0112 0.2090 0.8061 0.8259 0.8159 0.7099
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+ 10 17:08:13 0.0000 0.0076 0.2263 0.8160 0.8204 0.8182 0.7145
runs/events.out.tfevents.1697561538.0468bd9609d6.7281.19 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 16:52:18,091 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,092 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=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 16:52:18,092 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,092 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
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+ - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
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+ 2023-10-17 16:52:18,092 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,092 Train: 7142 sentences
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+ 2023-10-17 16:52:18,092 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 16:52:18,092 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,092 Training Params:
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+ 2023-10-17 16:52:18,092 - learning_rate: "5e-05"
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+ 2023-10-17 16:52:18,092 - mini_batch_size: "4"
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+ 2023-10-17 16:52:18,092 - max_epochs: "10"
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+ 2023-10-17 16:52:18,092 - shuffle: "True"
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+ 2023-10-17 16:52:18,092 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,092 Plugins:
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+ 2023-10-17 16:52:18,093 - TensorboardLogger
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+ 2023-10-17 16:52:18,093 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 16:52:18,093 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,093 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 16:52:18,093 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 16:52:18,093 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,093 Computation:
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+ 2023-10-17 16:52:18,093 - compute on device: cuda:0
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+ 2023-10-17 16:52:18,093 - embedding storage: none
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+ 2023-10-17 16:52:18,093 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,093 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-17 16:52:18,093 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,093 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:52:18,093 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 16:52:27,358 epoch 1 - iter 178/1786 - loss 2.21871897 - time (sec): 9.26 - samples/sec: 2878.90 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 16:52:36,409 epoch 1 - iter 356/1786 - loss 1.38296011 - time (sec): 18.31 - samples/sec: 2845.66 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 16:52:45,400 epoch 1 - iter 534/1786 - loss 1.05615250 - time (sec): 27.31 - samples/sec: 2783.47 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 16:52:54,087 epoch 1 - iter 712/1786 - loss 0.85769517 - time (sec): 35.99 - samples/sec: 2784.06 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 16:53:02,572 epoch 1 - iter 890/1786 - loss 0.73275737 - time (sec): 44.48 - samples/sec: 2790.69 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 16:53:11,664 epoch 1 - iter 1068/1786 - loss 0.63820486 - time (sec): 53.57 - samples/sec: 2796.92 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 16:53:20,466 epoch 1 - iter 1246/1786 - loss 0.56890668 - time (sec): 62.37 - samples/sec: 2799.26 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 16:53:29,472 epoch 1 - iter 1424/1786 - loss 0.51716606 - time (sec): 71.38 - samples/sec: 2802.99 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 16:53:38,038 epoch 1 - iter 1602/1786 - loss 0.48029995 - time (sec): 79.94 - samples/sec: 2793.74 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 16:53:46,960 epoch 1 - iter 1780/1786 - loss 0.44806609 - time (sec): 88.87 - samples/sec: 2792.18 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 16:53:47,217 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:53:47,217 EPOCH 1 done: loss 0.4473 - lr: 0.000050
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+ 2023-10-17 16:53:50,565 DEV : loss 0.13324852287769318 - f1-score (micro avg) 0.7523
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+ 2023-10-17 16:53:50,581 saving best model
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+ 2023-10-17 16:53:50,908 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:54:00,065 epoch 2 - iter 178/1786 - loss 0.13770242 - time (sec): 9.16 - samples/sec: 2870.67 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 16:54:09,059 epoch 2 - iter 356/1786 - loss 0.13827377 - time (sec): 18.15 - samples/sec: 2864.91 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 16:54:17,988 epoch 2 - iter 534/1786 - loss 0.13360174 - time (sec): 27.08 - samples/sec: 2907.68 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 16:54:27,211 epoch 2 - iter 712/1786 - loss 0.12801144 - time (sec): 36.30 - samples/sec: 2833.67 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 16:54:36,104 epoch 2 - iter 890/1786 - loss 0.12858850 - time (sec): 45.20 - samples/sec: 2831.78 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 16:54:44,902 epoch 2 - iter 1068/1786 - loss 0.12567109 - time (sec): 53.99 - samples/sec: 2824.77 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 16:54:53,158 epoch 2 - iter 1246/1786 - loss 0.12596144 - time (sec): 62.25 - samples/sec: 2802.84 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 16:55:01,552 epoch 2 - iter 1424/1786 - loss 0.12696890 - time (sec): 70.64 - samples/sec: 2806.15 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 16:55:10,194 epoch 2 - iter 1602/1786 - loss 0.12429806 - time (sec): 79.29 - samples/sec: 2823.60 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 16:55:18,590 epoch 2 - iter 1780/1786 - loss 0.12508328 - time (sec): 87.68 - samples/sec: 2825.63 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 16:55:18,875 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:55:18,875 EPOCH 2 done: loss 0.1249 - lr: 0.000044
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+ 2023-10-17 16:55:23,260 DEV : loss 0.13685545325279236 - f1-score (micro avg) 0.7641
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+ 2023-10-17 16:55:23,284 saving best model
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+ 2023-10-17 16:55:23,802 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:55:33,412 epoch 3 - iter 178/1786 - loss 0.08647078 - time (sec): 9.61 - samples/sec: 2576.96 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 16:55:42,204 epoch 3 - iter 356/1786 - loss 0.08617597 - time (sec): 18.40 - samples/sec: 2580.69 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 16:55:51,071 epoch 3 - iter 534/1786 - loss 0.08424719 - time (sec): 27.27 - samples/sec: 2641.65 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 16:55:59,868 epoch 3 - iter 712/1786 - loss 0.08587144 - time (sec): 36.06 - samples/sec: 2659.75 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 16:56:08,770 epoch 3 - iter 890/1786 - loss 0.08794462 - time (sec): 44.97 - samples/sec: 2682.94 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 16:56:17,535 epoch 3 - iter 1068/1786 - loss 0.08815726 - time (sec): 53.73 - samples/sec: 2688.62 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 16:56:26,777 epoch 3 - iter 1246/1786 - loss 0.08905607 - time (sec): 62.97 - samples/sec: 2729.34 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 16:56:35,913 epoch 3 - iter 1424/1786 - loss 0.08723379 - time (sec): 72.11 - samples/sec: 2753.83 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 16:56:44,974 epoch 3 - iter 1602/1786 - loss 0.08706683 - time (sec): 81.17 - samples/sec: 2762.42 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 16:56:54,035 epoch 3 - iter 1780/1786 - loss 0.08825144 - time (sec): 90.23 - samples/sec: 2750.82 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 16:56:54,315 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:56:54,315 EPOCH 3 done: loss 0.0882 - lr: 0.000039
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+ 2023-10-17 16:56:59,077 DEV : loss 0.14459823071956635 - f1-score (micro avg) 0.7584
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+ 2023-10-17 16:56:59,106 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:57:08,002 epoch 4 - iter 178/1786 - loss 0.06433659 - time (sec): 8.89 - samples/sec: 2562.16 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 16:57:17,044 epoch 4 - iter 356/1786 - loss 0.06363777 - time (sec): 17.94 - samples/sec: 2718.43 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 16:57:26,135 epoch 4 - iter 534/1786 - loss 0.06827523 - time (sec): 27.03 - samples/sec: 2719.40 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 16:57:35,404 epoch 4 - iter 712/1786 - loss 0.06514736 - time (sec): 36.30 - samples/sec: 2728.35 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 16:57:45,569 epoch 4 - iter 890/1786 - loss 0.06336587 - time (sec): 46.46 - samples/sec: 2657.00 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 16:57:55,140 epoch 4 - iter 1068/1786 - loss 0.06381780 - time (sec): 56.03 - samples/sec: 2651.17 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 16:58:04,219 epoch 4 - iter 1246/1786 - loss 0.06361061 - time (sec): 65.11 - samples/sec: 2676.83 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 16:58:13,784 epoch 4 - iter 1424/1786 - loss 0.06257745 - time (sec): 74.68 - samples/sec: 2658.04 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 16:58:22,720 epoch 4 - iter 1602/1786 - loss 0.06274981 - time (sec): 83.61 - samples/sec: 2671.70 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 16:58:31,629 epoch 4 - iter 1780/1786 - loss 0.06404258 - time (sec): 92.52 - samples/sec: 2678.43 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 16:58:31,931 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 16:58:31,931 EPOCH 4 done: loss 0.0645 - lr: 0.000033
130
+ 2023-10-17 16:58:36,305 DEV : loss 0.19311568140983582 - f1-score (micro avg) 0.805
131
+ 2023-10-17 16:58:36,325 saving best model
132
+ 2023-10-17 16:58:36,922 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 16:58:46,869 epoch 5 - iter 178/1786 - loss 0.04260479 - time (sec): 9.94 - samples/sec: 2357.89 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 16:58:57,159 epoch 5 - iter 356/1786 - loss 0.04983370 - time (sec): 20.24 - samples/sec: 2454.69 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 16:59:07,160 epoch 5 - iter 534/1786 - loss 0.04828306 - time (sec): 30.24 - samples/sec: 2471.15 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 16:59:17,235 epoch 5 - iter 712/1786 - loss 0.04893202 - time (sec): 40.31 - samples/sec: 2496.01 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 16:59:26,584 epoch 5 - iter 890/1786 - loss 0.04637195 - time (sec): 49.66 - samples/sec: 2501.21 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 16:59:36,704 epoch 5 - iter 1068/1786 - loss 0.04625644 - time (sec): 59.78 - samples/sec: 2473.47 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 16:59:45,822 epoch 5 - iter 1246/1786 - loss 0.04898382 - time (sec): 68.90 - samples/sec: 2511.42 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 16:59:54,824 epoch 5 - iter 1424/1786 - loss 0.04759295 - time (sec): 77.90 - samples/sec: 2544.97 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 17:00:03,625 epoch 5 - iter 1602/1786 - loss 0.04680758 - time (sec): 86.70 - samples/sec: 2587.51 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 17:00:12,295 epoch 5 - iter 1780/1786 - loss 0.04743130 - time (sec): 95.37 - samples/sec: 2597.21 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 17:00:12,588 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 17:00:12,589 EPOCH 5 done: loss 0.0474 - lr: 0.000028
145
+ 2023-10-17 17:00:16,651 DEV : loss 0.17322644591331482 - f1-score (micro avg) 0.7981
146
+ 2023-10-17 17:00:16,668 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 17:00:25,622 epoch 6 - iter 178/1786 - loss 0.03367132 - time (sec): 8.95 - samples/sec: 2649.14 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 17:00:34,756 epoch 6 - iter 356/1786 - loss 0.02946445 - time (sec): 18.09 - samples/sec: 2716.66 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 17:00:43,640 epoch 6 - iter 534/1786 - loss 0.03288777 - time (sec): 26.97 - samples/sec: 2698.65 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 17:00:52,601 epoch 6 - iter 712/1786 - loss 0.03447713 - time (sec): 35.93 - samples/sec: 2741.62 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 17:01:01,792 epoch 6 - iter 890/1786 - loss 0.03420441 - time (sec): 45.12 - samples/sec: 2753.07 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 17:01:10,808 epoch 6 - iter 1068/1786 - loss 0.03406023 - time (sec): 54.14 - samples/sec: 2754.14 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 17:01:19,758 epoch 6 - iter 1246/1786 - loss 0.03617254 - time (sec): 63.09 - samples/sec: 2758.50 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 17:01:28,690 epoch 6 - iter 1424/1786 - loss 0.03612102 - time (sec): 72.02 - samples/sec: 2735.54 - lr: 0.000023 - momentum: 0.000000
155
+ 2023-10-17 17:01:37,772 epoch 6 - iter 1602/1786 - loss 0.03765794 - time (sec): 81.10 - samples/sec: 2746.72 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 17:01:46,880 epoch 6 - iter 1780/1786 - loss 0.03667936 - time (sec): 90.21 - samples/sec: 2751.60 - lr: 0.000022 - momentum: 0.000000
157
+ 2023-10-17 17:01:47,163 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 17:01:47,163 EPOCH 6 done: loss 0.0366 - lr: 0.000022
159
+ 2023-10-17 17:01:52,645 DEV : loss 0.15160760283470154 - f1-score (micro avg) 0.8108
160
+ 2023-10-17 17:01:52,669 saving best model
161
+ 2023-10-17 17:01:53,166 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 17:02:02,034 epoch 7 - iter 178/1786 - loss 0.02243616 - time (sec): 8.87 - samples/sec: 2670.58 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 17:02:10,842 epoch 7 - iter 356/1786 - loss 0.02990473 - time (sec): 17.67 - samples/sec: 2698.56 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 17:02:19,800 epoch 7 - iter 534/1786 - loss 0.02790768 - time (sec): 26.63 - samples/sec: 2745.16 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 17:02:28,678 epoch 7 - iter 712/1786 - loss 0.02686670 - time (sec): 35.51 - samples/sec: 2767.72 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 17:02:37,705 epoch 7 - iter 890/1786 - loss 0.02818929 - time (sec): 44.54 - samples/sec: 2794.28 - lr: 0.000019 - momentum: 0.000000
167
+ 2023-10-17 17:02:46,743 epoch 7 - iter 1068/1786 - loss 0.02833242 - time (sec): 53.58 - samples/sec: 2778.54 - lr: 0.000019 - momentum: 0.000000
168
+ 2023-10-17 17:02:55,461 epoch 7 - iter 1246/1786 - loss 0.02713084 - time (sec): 62.29 - samples/sec: 2786.84 - lr: 0.000018 - momentum: 0.000000
169
+ 2023-10-17 17:03:04,492 epoch 7 - iter 1424/1786 - loss 0.02636015 - time (sec): 71.32 - samples/sec: 2794.61 - lr: 0.000018 - momentum: 0.000000
170
+ 2023-10-17 17:03:13,874 epoch 7 - iter 1602/1786 - loss 0.02528908 - time (sec): 80.71 - samples/sec: 2787.60 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 17:03:22,759 epoch 7 - iter 1780/1786 - loss 0.02472222 - time (sec): 89.59 - samples/sec: 2770.71 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 17:03:23,041 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:03:23,041 EPOCH 7 done: loss 0.0247 - lr: 0.000017
174
+ 2023-10-17 17:03:27,251 DEV : loss 0.19103629887104034 - f1-score (micro avg) 0.8067
175
+ 2023-10-17 17:03:27,268 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 17:03:36,570 epoch 8 - iter 178/1786 - loss 0.01260488 - time (sec): 9.30 - samples/sec: 2824.02 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 17:03:45,688 epoch 8 - iter 356/1786 - loss 0.01459491 - time (sec): 18.42 - samples/sec: 2767.29 - lr: 0.000016 - momentum: 0.000000
178
+ 2023-10-17 17:03:54,755 epoch 8 - iter 534/1786 - loss 0.01672564 - time (sec): 27.49 - samples/sec: 2767.12 - lr: 0.000015 - momentum: 0.000000
179
+ 2023-10-17 17:04:03,758 epoch 8 - iter 712/1786 - loss 0.01689015 - time (sec): 36.49 - samples/sec: 2761.92 - lr: 0.000014 - momentum: 0.000000
180
+ 2023-10-17 17:04:13,230 epoch 8 - iter 890/1786 - loss 0.01743950 - time (sec): 45.96 - samples/sec: 2746.11 - lr: 0.000014 - momentum: 0.000000
181
+ 2023-10-17 17:04:22,645 epoch 8 - iter 1068/1786 - loss 0.01652408 - time (sec): 55.38 - samples/sec: 2739.98 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 17:04:32,047 epoch 8 - iter 1246/1786 - loss 0.01619837 - time (sec): 64.78 - samples/sec: 2731.32 - lr: 0.000013 - momentum: 0.000000
183
+ 2023-10-17 17:04:40,986 epoch 8 - iter 1424/1786 - loss 0.01653528 - time (sec): 73.72 - samples/sec: 2702.23 - lr: 0.000012 - momentum: 0.000000
184
+ 2023-10-17 17:04:50,044 epoch 8 - iter 1602/1786 - loss 0.01772735 - time (sec): 82.77 - samples/sec: 2690.22 - lr: 0.000012 - momentum: 0.000000
185
+ 2023-10-17 17:04:59,741 epoch 8 - iter 1780/1786 - loss 0.01789757 - time (sec): 92.47 - samples/sec: 2680.76 - lr: 0.000011 - momentum: 0.000000
186
+ 2023-10-17 17:05:00,037 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-17 17:05:00,037 EPOCH 8 done: loss 0.0178 - lr: 0.000011
188
+ 2023-10-17 17:05:04,178 DEV : loss 0.19288192689418793 - f1-score (micro avg) 0.8164
189
+ 2023-10-17 17:05:04,196 saving best model
190
+ 2023-10-17 17:05:04,690 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-17 17:05:13,676 epoch 9 - iter 178/1786 - loss 0.01009022 - time (sec): 8.98 - samples/sec: 2756.42 - lr: 0.000011 - momentum: 0.000000
192
+ 2023-10-17 17:05:22,516 epoch 9 - iter 356/1786 - loss 0.00986010 - time (sec): 17.82 - samples/sec: 2738.92 - lr: 0.000010 - momentum: 0.000000
193
+ 2023-10-17 17:05:31,550 epoch 9 - iter 534/1786 - loss 0.00972978 - time (sec): 26.86 - samples/sec: 2715.07 - lr: 0.000009 - momentum: 0.000000
194
+ 2023-10-17 17:05:40,522 epoch 9 - iter 712/1786 - loss 0.01005396 - time (sec): 35.83 - samples/sec: 2724.34 - lr: 0.000009 - momentum: 0.000000
195
+ 2023-10-17 17:05:49,410 epoch 9 - iter 890/1786 - loss 0.00974288 - time (sec): 44.72 - samples/sec: 2707.57 - lr: 0.000008 - momentum: 0.000000
196
+ 2023-10-17 17:05:58,027 epoch 9 - iter 1068/1786 - loss 0.01028181 - time (sec): 53.34 - samples/sec: 2735.43 - lr: 0.000008 - momentum: 0.000000
197
+ 2023-10-17 17:06:06,447 epoch 9 - iter 1246/1786 - loss 0.01054176 - time (sec): 61.76 - samples/sec: 2746.07 - lr: 0.000007 - momentum: 0.000000
198
+ 2023-10-17 17:06:15,508 epoch 9 - iter 1424/1786 - loss 0.01094470 - time (sec): 70.82 - samples/sec: 2794.60 - lr: 0.000007 - momentum: 0.000000
199
+ 2023-10-17 17:06:24,863 epoch 9 - iter 1602/1786 - loss 0.01150858 - time (sec): 80.17 - samples/sec: 2785.58 - lr: 0.000006 - momentum: 0.000000
200
+ 2023-10-17 17:06:33,903 epoch 9 - iter 1780/1786 - loss 0.01116154 - time (sec): 89.21 - samples/sec: 2777.51 - lr: 0.000006 - momentum: 0.000000
201
+ 2023-10-17 17:06:34,220 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-17 17:06:34,220 EPOCH 9 done: loss 0.0112 - lr: 0.000006
203
+ 2023-10-17 17:06:38,448 DEV : loss 0.20903374254703522 - f1-score (micro avg) 0.8159
204
+ 2023-10-17 17:06:38,465 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-17 17:06:47,597 epoch 10 - iter 178/1786 - loss 0.00568008 - time (sec): 9.13 - samples/sec: 2739.71 - lr: 0.000005 - momentum: 0.000000
206
+ 2023-10-17 17:06:56,476 epoch 10 - iter 356/1786 - loss 0.00720514 - time (sec): 18.01 - samples/sec: 2737.90 - lr: 0.000004 - momentum: 0.000000
207
+ 2023-10-17 17:07:06,276 epoch 10 - iter 534/1786 - loss 0.00805828 - time (sec): 27.81 - samples/sec: 2667.98 - lr: 0.000004 - momentum: 0.000000
208
+ 2023-10-17 17:07:15,509 epoch 10 - iter 712/1786 - loss 0.00769778 - time (sec): 37.04 - samples/sec: 2677.42 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 17:07:24,581 epoch 10 - iter 890/1786 - loss 0.00701365 - time (sec): 46.11 - samples/sec: 2705.04 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-17 17:07:33,235 epoch 10 - iter 1068/1786 - loss 0.00703323 - time (sec): 54.77 - samples/sec: 2724.05 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 17:07:42,565 epoch 10 - iter 1246/1786 - loss 0.00729937 - time (sec): 64.10 - samples/sec: 2767.76 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-17 17:07:51,492 epoch 10 - iter 1424/1786 - loss 0.00712708 - time (sec): 73.03 - samples/sec: 2778.64 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 17:07:59,964 epoch 10 - iter 1602/1786 - loss 0.00771465 - time (sec): 81.50 - samples/sec: 2769.03 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-17 17:08:08,709 epoch 10 - iter 1780/1786 - loss 0.00761523 - time (sec): 90.24 - samples/sec: 2746.66 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-17 17:08:08,994 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-17 17:08:08,995 EPOCH 10 done: loss 0.0076 - lr: 0.000000
217
+ 2023-10-17 17:08:13,185 DEV : loss 0.22629648447036743 - f1-score (micro avg) 0.8182
218
+ 2023-10-17 17:08:13,202 saving best model
219
+ 2023-10-17 17:08:14,229 ----------------------------------------------------------------------------------------------------
220
+ 2023-10-17 17:08:14,231 Loading model from best epoch ...
221
+ 2023-10-17 17:08:15,809 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
222
+ 2023-10-17 17:08:25,470
223
+ Results:
224
+ - F-score (micro) 0.7015
225
+ - F-score (macro) 0.6396
226
+ - Accuracy 0.555
227
+
228
+ By class:
229
+ precision recall f1-score support
230
+
231
+ LOC 0.6994 0.7032 0.7013 1095
232
+ PER 0.7741 0.7688 0.7714 1012
233
+ ORG 0.5105 0.5462 0.5277 357
234
+ HumanProd 0.4528 0.7273 0.5581 33
235
+
236
+ micro avg 0.6954 0.7076 0.7015 2497
237
+ macro avg 0.6092 0.6864 0.6396 2497
238
+ weighted avg 0.6994 0.7076 0.7030 2497
239
+
240
+ 2023-10-17 17:08:25,470 ----------------------------------------------------------------------------------------------------